WO2020167934A1 - Systèmes agricoles commandés et procédés de gestion de systèmes agricoles - Google Patents

Systèmes agricoles commandés et procédés de gestion de systèmes agricoles Download PDF

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Publication number
WO2020167934A1
WO2020167934A1 PCT/US2020/017910 US2020017910W WO2020167934A1 WO 2020167934 A1 WO2020167934 A1 WO 2020167934A1 US 2020017910 W US2020017910 W US 2020017910W WO 2020167934 A1 WO2020167934 A1 WO 2020167934A1
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WIPO (PCT)
Prior art keywords
semantic
growth
plants
light
input
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PCT/US2020/017910
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English (en)
Inventor
Timo BONGARTZ
Sebastian Olschowski
Norbert Haas
Guido Angenendt
Marek Burza
Norbert Magg
Cristin Dziekonski
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Osram Gmbh
Osram Sylvania Inc.
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Priority claimed from US16/275,476 external-priority patent/US20190259108A1/en
Priority claimed from US16/786,001 external-priority patent/US11663414B2/en
Application filed by Osram Gmbh, Osram Sylvania Inc. filed Critical Osram Gmbh
Priority to CA3130218A priority Critical patent/CA3130218A1/fr
Priority to EP20755186.2A priority patent/EP3924808A4/fr
Priority to CN202080028508.6A priority patent/CN113966518B/zh
Publication of WO2020167934A1 publication Critical patent/WO2020167934A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F8/00—Arrangements for software engineering
    • G06F8/30—Creation or generation of source code
    • G06F8/35—Creation or generation of source code model driven
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
    • A01B79/00—Methods for working soil
    • A01B79/005—Precision agriculture
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • A01G7/04—Electric or magnetic or acoustic treatment of plants for promoting growth
    • A01G7/045—Electric or magnetic or acoustic treatment of plants for promoting growth with electric lighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
    • Y02P60/20—Reduction of greenhouse gas [GHG] emissions in agriculture, e.g. CO2
    • Y02P60/21—Dinitrogen oxide [N2O], e.g. using aquaponics, hydroponics or efficiency measures

Definitions

  • the present disclosure relates to a Controlled Agricultural System, an Agricultural Light Fixture for use in a Controlled Agricultural System and a Method for Agricultural Management.
  • a farm or agricultural system discussed here can have a quite different design and setup in detail, for instance depending on the type and size of the plants grown, but also on the location of the farm (e. g. vertical farm in a city) or other local requirements.
  • the embodiment of claim 1 relates to an agricultural system or farm with processing lines for growing plants.
  • a first processing line is configured to move a first plurality of plants through the agricultural system along a route and to apply a first growth condition to the first plurality of plants to satisfy a first active agent parameter for the first plurality of plants.
  • An active agent can for instance be a pharmaceutical ingredient, see the element "Medical Certificates" in detail.
  • Embodiments relating to the functionality of the farm as a whole are described in Chapter I "System Setup”.
  • the farm in particular a fully automated farm, can manage the entire growth system, applying not only a customized illumination to the plants (light recipes), but even customized environmental conditions (growth recipes) and solutions for maintaining or restoring plant health, see Chapter II "Plant Health/Growth” and Chapter III "Light/Growth Recipe”.
  • the farm is one element in a supply chain.
  • electrical energy is one of the most important ingoing goods, which is described in detail in Chapter V "Smart Grid”, particularly the interaction between a controlled agricultural system and a smart grid power supply.
  • an automated farm can also enable an alignment with downstream entities of food industry, in particular food producers. In simple words, exactly that crop (specific taste or nutrient content or the like) required in a food fab for processing a certain lot can be grown in the farm.
  • customers can address their requests for customized plants to the farm, which can be preprocessed and fed into the digital supply chain of the agricultural system.
  • the customer can monitor the growth of the customized plants by means of information on the respective growth stages provided by the farm to the customer.
  • Such interaction between farm and customer is described in more detail in Chapter VI "Customer Interaction".
  • a major risk for farmers and food producers is a crop damage or even total failure, which might end up in a total economic loss. Even though going indoors can reduce the risk of for instance a storm damage, other hazards remain, as for example an infection of the plants with fungi or diseases.
  • One major, though not the only, path of infection can be the interaction with an operating personnel bringing for instance spores from the environment outside into the farm. This can be one reason why a widely or even fully automated farm is advantageous.
  • the automatization in farming is hardly comparable to the production process optimization known from industrial goods. Apart from various plant specific needs, these "organic goods” change their morphology and size during production.
  • the element "Resizable Growth Area” proposes a growth area adjustable in size. The distance between the individual growth locations can be adapted based on the size of the plants grown there, allowing for a compact arrangement and efficient illumination at the beginning of the growth cycle and sufficient space as well as efficient illumination for the plants at the end thereof.
  • the growth area can be moved through the farm, wherein at different locations different illumination setups are provided, which are perfectly adapted to the actual size of the growth area at the respective location.
  • the growth locations can be trays floating on a waterway.
  • the transportation through the farm for instance along different illumination areas adapted to the respective growth stage, can be combined with an inherent water or also nutrient supply in the waterway.
  • the respective growth tray can be unloaded from the waterway to a specific treatment location, as discussed in "Horticulture Processing Line" in detail.
  • the specific treatment location can be comparable to a quarantine area, which can further reduce the interaction of an operating personnel at the standard processing line and the risk of a contamination.
  • the trays with the seeds or plants can also be moved on rails or elevators along the standard processing line (but also by transportation cars or moving arms or robots in general).
  • a method or tool for planning a highly automated farm is described in "Light Recipes and Workflow”.
  • the setup of a sensor device or array can be crucial for the automatization, for instance to detect infected plants and trigger their unloading or treatment.
  • An optimal arrangement or distribution of the sensors in a farm can be achieved by the method described in "Measuring Patterns”.
  • “Hydroponics” or “Horticulture Processing Line” can use plant health detection systems as for instance described in “Disease & Pest Control ", “Prophylaxis”, “Discolored Spots Detection” and/or light treatments as for instance described in “Light Guides”, “Temperature Control”, “Fungi Growth Inhibition”.
  • the speed of the production may even be adapted to allow for a proper sensing or treatment of the plants.
  • a proper function of the light fixtures in particular in the long run over their lifetime, data obtained according to the method described in “Failure Detection” is helpful. The approach described there can also reduce human interaction and the risk of infection thus.
  • the conditions in the farm or different zones thereof are customized to the specific needs of the plants in the respective growth stages. This customization can be supported or achieved by various sensor measurements.
  • feedback loops can be implemented so that the plants but also the sensors themselves are monitored.
  • the respective location or zone of the farm possibly also the farm as a whole, can switch into a kind of preservation mode.
  • the illumination, temperature, humidity and other important factors can be set to a point, which keeps the plants in a most comfortable condition without losing quality until the error (defect sensor or actual problem in the farm) is eliminated.
  • avoiding a crop failure, a reduction in crop quality or a reduced harvested biomass are a primary objectives.
  • Health/Growth addresses the health and growth of plants.
  • Agricultural Facilities particularly horticultural facilities such as greenhouses are not completely shielded from the external surroundings. Thus, pathogens or pests may occasionally be introduced or released in a horticultural farm, for example via the ventilation system, the watering and drainage removal system or when introducing seeds and germ buds. Additionally, humans or machines deployed from the outside into the facility, for example, automated guided agricultural robots, may introduce pests into a greenhouse. Therefore, it is important to detect stress or diseases of plants at an early stage, especially in a closed environment like a vertical farm, where diseases can spread easily. Then, these critical conditions may be countered by way of appropriate measures (e.g., pesticides) in order to contain the outbreak.
  • appropriate measures e.g., pesticides
  • Causes of discolorations can for example be caused by lack of nutrients or lack of chemical elements like Nitrogen (N), Phosphor (P), Potassium (K), Sulfur (S), Manganese (Mn), over-supply of nutrients, too much light, too rapid temperature changes, lack of air circulation, too dry air, too much irrigation, bacterial and virus infestation causing for example bacterial blight and bacterial wilt, soil contamination, soil temperature and many others.
  • plant leaves can develop holes.
  • cameras may be used to observe plants and detect color changes that could be associated with diseases, i.e. when the discolored parts have changed from their naturally provided colors (according to their actual growth stage) to a changed color impression, in other words when they have become discolored.
  • Discolorations can affect only parts or small segments of a plant body (stem, petals, and leaves) or greater areas.
  • some color changes signal a next stage of ripening, e.g. the change of color in fruits. For instance, tomatoes discolor from green to red while ripening, eventually triggering harvesting.
  • Plant growth can mean the height of the plant, the size and number and orientation of the leaves, the diameter of the plant, the plant morphology, and the height of the Apikalmeristem etc.
  • yield Prediction proposes a yield prediction for flowering plants by detecting the number of plants and considering the ripening probability.
  • Plants can be affected by several diseases, some of them caused by fungi. Therefore, it would be advantageous if growth of fungi could be inhibited automatically.
  • the element "Fungi Growth Inhibition" of the disclosure describes a controlled agricultural system configured for applying a fungi prevention illumination.
  • LiDAR Plant Surveillance describes using LiDAR for 3D plant surveillance, commissioning the system.
  • the controlled agricultural system is configured to be able to analyze the measured parameters and infer a disease or pest.
  • the controlled agricultural system may also be configured to be able to predict the yield based on measured parameters.
  • LAI Leaf Area Index
  • Substrate moisture includes, soil, rock wool, perlite etc.
  • temperature includes, soil, rock wool, perlite etc.
  • Plant parameters like: Plant height, Leaf area, number of flowers and/or fruits
  • the NDVI Normal density vegetation index
  • Chlorophyll strongly absorbs visible light 400-700 nm
  • NIR near infrared
  • o Value Index for CRI ranges from 0 to >15
  • sensors which can be used in the controlled agricultural system are:
  • the time interval between measurements depends on the kind of sensor. Environmental factors should be tracked every minute. Measurements with a camera systems for disease detection can be done 3-5 times per day. For example, mildew can occur over night.
  • the triggering of a measurement can be dependent on signals/events/levels/thresholds from other sensors (e.g. to cross-check/confirm detection by other means, or make the detection more specific).
  • Data can be collected and stored locally and/or in the cloud, i.e. the global internet network.
  • the data is transferred wireless (e.g. radio, via light) to the computing device of the controlled agricultural system und then processed, to be shown at the dashboard in the typical units (e.g. temperature °C, rel. humidity %, absolute humidity g/m 3 , etc.), but also wired data transfer can be an option.
  • the data may be transformed in values according to the metric system or systems used in other countries (like the imperial system).
  • the data may be processed first to render a space-resolved mapping. For example, if five sensors for temperature measurements are distributed in an area of 1 ha, the average value of the temperature may be compute as well as a kind of a temperature map for the greenhouse.
  • map data can be 2D or also 3D as a point cloud.
  • derived parameters may be calculated from measured values, for example the dew point, which is derived from temperature and humidity.
  • Another way to process the data could be to estimate the relative number of plants affected, like approx. 65 % of your plants are infected, e.g. by detecting a necrosis or the infection with a fungi.
  • the data can be stored in the "original" or “raw” format or in a processed format.
  • images can be stored (raw data) or the analyzed information retrieved by analyzing the image, e.g. that x% of the plants are affected by a certain disease (processed data).
  • Processed data Storing the original picture can be useful in case the algorithm is improved and certain values need to be re-calculated.
  • the data analysis may include calculating averages and relative values (percentages) and a combination of different sensor data (sensor fusion).
  • the data may also be manually and/or automatically annotated, e.g. what crop species was grown, when and where a disease/pest occurred. Then the controlled agricultural system may be configured to apply machine learning/AI to learn automatically the conditions for detection of the stressors or disease causing conditions.
  • Reference data for the comparison with measured data and subsequent analysis of the result can be generally available reference data (i.e. some generic data and not data generated at the particular agricultural system), particularly as an initial step.
  • the controlled agricultural system may be configured to start using historical on-site data, for example, data from one of the previous years.
  • the collection of reference data may not only include specific values, but also a range of values (min., max.) including a plausibility-check of the limit values. For example, a completely unrealistic value of 5 kg for the weight of a tomato would be excluded.
  • Update of new data which leads to a more precise calculation process, can be provided online via cloud. It can also be possible, that growers with the same crop actively decide to upload their data to the platform connected to the controlled agriculture system. The data may be used anonymously.
  • the measured data may be analyzed periodically, in real-time and/or dependent on what the customer is willing to pay.
  • the controlled agriculture system may be configured to inform the grower if a certain threshold (min. or max. value) of a critical parameter is reached or if, for example, a certain percentage of leaves is affected.
  • the trigger for detecting or verifying a disease may also be a certain combination of environmental factors like EC or pH values.
  • the trigger may also be provided by a trained Al system. The system might continue to learn while used, e.g. by supervised learning. In this case it may be beneficial to include a feedback-loop between the system and the operator to train and improve the system. To this purpose, the operator may feed back to the system whether he/she confirms or dismisses a potential issue flagged by the system.
  • Data storage, retrieval and processing may be managed on-site or by means of cloud-computing services, which enable on-demand access to a shared pool of computing resources (servers, applications, data, storage, processing) that can be rapidly provisioned and released via the Internet, e.g. Platform as a Service (PaaS), Software as a Service (SaaS).
  • PaaS Platform as a Service
  • SaaS Software as a Service
  • a vision behind the elements of the disclosure discussed in this chapter is a farm that manages the entire growth system. It can not only apply light recipes, namely a specific illumination based on the growth status, but also adjust further growth conditions. Apart from the illumination, respective control programs of the farm can for instance apply the required irrigation, fertilization, fertigation and/or plant movement.
  • illumination to a specific type of plant, in terms of the intensity and spectral composition.
  • Different illumination setups can stimulate or trigger a difference in growth or the creation of certain ingredients (for instance primary and secondary metabolites).
  • Even the taste or vitamin content of the crop can be influenced via the light recipe.
  • the light or growth recipe is for instance used for delaying or speeding up the harvesting time to meet new target values arising during the production of the plants, namely while plants are already growing.
  • target values can for instance be the growth rate, but also vitamin content, biomass or color of the plants.
  • a light recipe can also be about an intensity or spectral modulation over time.
  • the illumination can be adapted to different growth stages of the plants, for instance from germination over growth to fructification.
  • different illumination setups can be arranged at different locations or zones of the farm, allowing for light fixtures or arrangements to be customized regarding the specific recipe.
  • different lighting conditions can also be applied with an adjustable light fixture having tuneable light sources with different spectral properties.
  • the failure handling of the "Automatic failure compensation” may be of interest, since light recipes rely on working light fixtures. With this intrinsic compensation of a failing light source (e.g. LED), the functionality of the light recipe can be assured.
  • the light recipe can be part of a growth recipe comprising or defining further parameters, as for instance the temperature, humidity, CC>2-level, airspeed EC, pH-value or the like.
  • a growth recipe comprising or defining further parameters, as for instance the temperature, humidity, CC>2-level, airspeed EC, pH-value or the like.
  • Illumination the interaction of these parameters is discussed, in particular the interaction between temperature and illumination.
  • a different illumination can be applied at different height levels to counteract for example a spread in time to flower, which could result from a higher temperature at the upper shelves due to convection.
  • the ambient light can for instance be residual daylight, allowing for an overall energy efficient and still customized illumination.
  • a growth or in particular light recipe can be a fixed data set comprising spectral properties and also information on time intervals and the like. Even though the spectral data is available in this generic form, there can be a missing link to the actual control parameters for operating or controlling a specific light fixture or luminaire. "Spectrum Calculation" is about translating a generic recipe into
  • Such a translation may also be relevant for any change of the light recipe.
  • a change of the recipe may not only depend on a customer request ("Flexible Growth”), the ambient illumination ("Adaptive spectrum”) or a temperature gradient ("Temperature Dependent Illumination”), but might be also used to induce a "Plant Movement”.
  • Plant Movement the light intensity is moved above the plants to induce them to follow, comparable to sunflowers following the sun. The movement can strengthen the plants.
  • light recipes can also be implemented by other means to save energy, for instance by optics. Beyond illumination, plant growth can also be modified using a "Temperature control” or by adjusting other parameters, like for instance CO2, humidity or the like. Comparable to the light recipes, also the other parameters can be adapted to a specific growth stage of the plants and change over time.
  • Luminaire address an agricultural light fixture, particularly horticultural light fixture.
  • An agricultural light fixture or agricultural luminaire provides illumination for an agricultural arrangement, e.g. a cultivated area or any other target area or target space, in a controlled agricultural system.
  • the illumination may comprise light in the visible range (VIS), the ultraviolet range (UV) and infrared range (IR) of the electromagnetic spectrum.
  • Luminaires can contain a variety of light sources, sensors, actuators and heat dissipation elements, and may be connected to the controlled agricultural system. Furthermore, luminaires can have adaptable features like form change and change of optics.
  • the plants are typically illuminated by the sunlight, wherein artificial lighting can be a supplementation in terms of the spectral composition or amount of light.
  • the latter can be described by the daily light integral (DLI) describing the number of photosynthetically active photons delivered to a specific area over a 24 hour period.
  • DMI daily light integral
  • indoor farming is also possible without any natural light at all but artificial lighting only.
  • semiconductors produce significant heat, which may cause a local heating of the illuminated products in such vertical farms.
  • LED light-sources used in existing systems may cause irregular illumination, if the distances to the plants are too small, while higher distances may result in light intensities lower as desired, in particular for specific purposes, such as pest or disease control (see also element “Disease & Pest Control” in group “Plant Health & Growth”), or influencing plant growth morphology or the enrichment of enzymes in an illuminated plant, or such.
  • Light Guides of the disclosure describes a light module comprising at least one light guide, which enables improved illumination of plants (see below).
  • a failing light source in particular a failing light fixture or light fixture module, can lead to an insufficient illumination of the plants grown in a controlled agricultural system. This does not only relate to the intensity, for instance a reduced DLI (Daily Light Integral), but also to the spectral composition of the light.
  • DLI Dynamic Light Integral
  • Agricultural light fixtures used in greenhouses or indoor farms are increasingly LED-based as they can provide a more specific spectrum (light recipe) and use less energy.
  • the LEDs nevertheless produce a significant amount of heat, which is usually taken away from the agricultural light fixture using heat spreaders, heat pipes or other solutions to cool the LEDs and prevent an overheating of the LEDs and the surrounding electronics. The thus removed heat-energy is usually lost for further usage.
  • Agricultural Facilities for example, horticultural facilities in Controlled Environment Agriculture (CEA), such as greenhouses and vertical farms, need significant amounts of electrical energy.
  • CAA Controlled Environment Agriculture
  • vertical farms and similar devices are major electricity consumers for their illumination and further components (water supply, etc.).
  • the supply of electricity is determined by the consumption by consumers.
  • the consumption by the consumers can be determined by the supply of the grid power supply since the consumer obtains information about the availability of the electricity (as a rule, by way of the price, which drops when the supply is high) in this case.
  • Smart Grid describes a controlled agricultural system that is able to make an optimal use of cost-effective electricity.
  • Customer Interaction address communications and interactions between the controlled agricultural system and customers.
  • the controlled agricultural system may receive inputs from the customer and provide information to the customer.
  • Requirements of the disclosure describes a controlled agricultural system and a method for customized plant growth.
  • growers may want to share pictures and the overall plant growth results on social media platforms like Instagram. Therefore, growers may want to take pictures with their smartphones or any similar mobile device (e.g. tablet PC) or even a camera.
  • the element "Medical Certificates” of the disclosure proposes a method for agricultural management, which align to these customer demands by providing products grown under conditions tailored for a specific use, particularly medical use.
  • the intended use and/or content of active agents of the plants may also be certified.
  • a growth area having a plurality of growth locations, each growth location being provided for growing a plant respectively, wherein a distance between the growth locations is adjustable.
  • a plurality of growth locations are provided, for instance carriers like trays or shells or the like.
  • the growth area adjustable in size, the distance between the growth locations can be adjusted depending on the plant’s growth (morphology) or other needs.
  • the growth area can have a rather small size at the beginning of a growth cycle, for instance after seeding or bedding the plants. Having the plants close to each other can be advantageous in regard to the artificial lighting, as only a comparably small area has to be illuminated.
  • the distance between the growth locations can be increased, and the illuminated area can be adapted accordingly. In general, this can be an adaption in distance, size, inclination, spectrum, heating, etc.
  • the inclination of the light fixtures and/or light sources and/or the optics could be beneficial.
  • the spectrum and intensity could be changed.
  • each plant Due to the resizable growth area, each plant has sufficient space to allow a proper growth. On the other hand, the plants can be kept close to each other as far as possible, enabling an energy efficient artificial lighting. Assuring small or even no intermediate spaces between the plants prevents a waste of light there.
  • the foil can for instance be a plastic or synthetic film or foil. By stretching the foil, the size of the growth area and the distance between the growth locations can be increased.
  • the growth locations can for instance be plant pots or trays attached to the foil, for instance by gluing.
  • 3 rd aspect of the "Resizable Growth Area” The growth area according to the 1 st or 2 nd aspect of the "Resizable Growth Area”, comprising a plurality of bars that forms the growth locations, interconnected with each other in joints in an articulated manner allowing for folding, in particular lateral folding.
  • the distance between the growth locations can be decreased, unfolding the bars increases the size of the growth area and thus the distance between the growth locations.
  • “Lateral” refers to a direction parallel to the growth area. In a typical application, the growth area can lie horizontally so that the lateral directions are horizontal directions. However, in general, the growth area can also be oriented in the vertical direction, for instance in case of a vertical farm.
  • the growth locations are in some embodiments / implementations connected with each other such that they are linked independently of the size of the growth area.
  • a connecting means e.g. the foil or bars
  • the aforementioned foil can also be combined with the aforementioned foil
  • the bars form a plurality of parallelograms. At the crossing locations, the joints are provided, interconnecting the bars of the different groups with each other.
  • the growth area can be adjusted in size comparable to a vertical or stair-case like scissor lift.
  • a Hoberman-sphere is for instance described in US 5,024,031 , this sphere is assembled from a plurality of Hoberman-rings.
  • the Hoberman-ring is used as the growth area, enabling for instance a more or less rotationally symmetrical size adjustment.
  • Other geometrical shapes are also possible, for example trapezoidal and polygonal structures, foldable structures that form a tessellated area, rotational arrangements with coplanar sides, pivotably linked support brackets, scissor-like extendable structures and the like.
  • connection/disconnection of the subareas can be achieved by a reversible mechanism, for instance a form-fit or snap-in mechanism.
  • a reversible mechanism for instance a form-fit or snap-in mechanism.
  • an irreversible mechanism is possible as well, and the disconnection can be achieved by for instance scissors or saws.
  • the floating subareas can be designed as carriers penetrable for liquids like water, as described in the element "Aquaponics", they can float on a waterway described in this element of the disclosure.
  • An Agricultural System particularly for plant breeding, growing, cultivating and harvesting, comprising:
  • a light fixture for illuminating at least a part of the growth area
  • the Agricultural System is configured for adjusting the size of the growth area by adjusting the distance between the growth locations.
  • the size adjustment is motor-driven, the agricultural system comprising an actuator device with one or more actuators to adjust the size.
  • the actuators can stretch the foil or move the bars, as described above.
  • 10 th aspect of the "Resizable Growth Area” The Agricultural System according to the 9 th aspect of the "Resizable Growth Area", wherein the illumination areas of different sizes are arranged at the same location, wherein only some light sources or light fixtures are switched on for illuminating a small growth area and additional light sources or light fixtures are switched on for illuminating a larger growth area.
  • the small area(s) is/are contained in the larger one(s). Therein, only one or some light sources/light fixtures are switched on for illuminating a small growth area and additional light sources/light fixtures are switched on for illuminating larger growth areas.
  • the illumination and/or irradiance of the growth area can for instance be optimized.
  • either the growth area or the illumination setup can be moved for this optimization.
  • optimizing the overlap can support an efficient use of the light sources. For instance in case of elongated luminaires, the size adjustment of the growth area could result in a disadvantageous partial coverage. Adjusting the relative position of the growth area allows, as the case may be, also switching off some light sources or fixtures even though more light sources are used in total (in case of a larger growth area).
  • a length elongation of the carrier can lead to a width reduction, for instance in case of a scissor mechanism described above. There, light sources/fixtures that are no longer needed due to the width reduction can be switched off.
  • the adaptive illumination can be achieved by switching light fixtures on or off as a whole.
  • the illumination areas differing in size are not arranged at the same location, instead the growth area is moved to another part of the farm. In addition to the relocation, the growth area can be rotated to control an overlap.
  • the different illumination areas can be arranged side by side, but they can also be provided in different rooms of the farm.
  • a conveyer belt or roller mechanism can be used for instance.
  • a waterway can be used for transporting the growth area, in particular in case of the connected/disconnectable subareas.
  • the data measured by the sensor device can be processed by a computing device, which initiates the actuator device to adjust the size of the growth area as required.
  • the distance between the growth location can for instance be increased as the plants grow.
  • the image capture device can for example be a camera, it can be used to determine the actual size of the plants.
  • the images taken can be processed by a picture recognition. In a simple approach, for instance, the number of independent plants can be counted, and the size of the growth area can be increased if two plants become so close that they appear as one.
  • a growth area having a plurality of growth locations for growing plants, a sensor device for sensing growth data of the plants,
  • the actuator device is configured for adjusting a distance between the growth locations and hence a size of the growth area
  • the computing device is configured to process the growth data measured by the sensor device and initiate the actuator device to adjust the size of the growth area based on the growth data, namely to vary the distance between the growth locations and hence the size of the growth area as the plants grow.
  • a plurality of bars that forms the growth locations of the growth area, a plurality of light fixtures for illuminating illumination areas, and an image capture device comprised in the sensor device,
  • the bars form a flexible grid, interconnected with each other in joints in an articulated manner allowing for folding, the joints being operatively connected with each other forming a scissors mechanism
  • the bars extend parallel to each other in groups, the bars of the different groups crossing each other,
  • the illumination areas have different sizes and are arranged at different locations the different sizes of the illumination areas being adapted to different sizes of the growth area
  • the Agricultural System configured to move the growth area from one illumination area at one location to another illumination area at another location, based on the size of the growth area
  • the growth data based on which the size of the growth area is adjusted, comprises images, the processing by the computing device comprising an image recognition.
  • this adjustment is done or triggered automatically by a computing device connected with a sensor device for sensing the growth and an actuator device, triggered by the computing device (e.g. via a control unit), for adjusting the size of the growth area.
  • a Method for agriculture comprising at least one Agricultural System, and comprising:
  • a Computer program product comprising:
  • a plurality of program instructions which when executed by a computing device of an Agricultural System, cause the Agricultural System to apply defined growing conditions to the plants.
  • the application of "defined growing conditions” may for instance be: applying a light recipe, adjusting a temperature, and/or adjusting a CO2 content.
  • the defined growing conditions can for instance be comprised in growth recipe, see below for illustration.
  • the program instructions can be pre-programmed or calculated based on
  • Pre-programmed instructions can for instance follow linear or non-linear function over time (or over total Photon Flux or any other photometric value, or assumed plant leaf density index), or first exponential, then logarithmic etc.
  • This element of the disclosure relates in particular to floating grow fields and a respective waterway for moving them through the farm.
  • a Grow field for a hydroponic arrangement comprising a carrier for carrying plants, wherein the carrier is designed to be penetrable for liquids like water.
  • This element of the disclosure solves the problem of how to provide irrigation and nutrients to the plants and yet allows for transportation of the plants through the farm over the growth cycle. It is proposed that the plants grow in small grow fields filled with materials as used in hydroponics, e.g. expanded clay aggregates, growstones, perlite, pumice, rock wool etc.
  • the grow fields are surrounded by water. Each grow field may contain a single plant or several plants. The roots of the plants may be hanging in the water. The water is used to provide irrigation and nutrients.
  • the small grow fields are not fixed and surrounded by flowing water, but movable within the surrounding water.
  • the preferred design is thus not the form of a plant pot, but a form that resembles a raft and allows floating on the water without tilting over.
  • the sides of the small grow fields, especially the backside lying in the water, comprise a grid-like structure, so they hold back the plants and the grow materials but let in the water.
  • the shape of the grow fields can be quadratic, rectangular, hexagonal, circular, or freeform.
  • Grow fields for the same kind of plant can have the same form.
  • Grow fields for another kind of plant can have a different form.
  • different shapes may facilitate identification of various kinds of plants.
  • the shape can influence the drift velocity.
  • Grow fields may be connected with each other (e.g. by magnets or any other connecting means like for instance form-fit members, see also the 7 th aspect of "Resizable Growth Area"), so that they form a chain of grow fields and float collectively.
  • the grow fields may be equipped with a variety of sensors and e.g. RFID or WLAN chips that transpond/repeat/send information to a reading device for asset tracking.
  • a reading device for asset tracking e.g. RFID or WLAN chips that transpond/repeat/send information to a reading device for asset tracking.
  • Such reading devices may be arranged in light fixtures that may be arranged above the grow fields for irradiating/illuminating the plants.
  • the waterway, in which the small grow fields are floating may be an elongated water tank, which is open on its upper side, i.e. the water surface.
  • the small grow fields are floating on the water surface from one side of the water tank (start) to the other (end), along its long side.
  • start one side of the water tank
  • end the other (end), along its long side.
  • a distance between start and end can for instance be at least 2 m, 4 m, 6 m or 8 m (with possible upper limits of for instance not more than 500 m, 200 m or 100 m).
  • the water flow can move the grow fields along the waterway.
  • the inflowing water can generate the water flow for moving the grow fields.
  • the water inlets do not only provide fresh water, they also provide the nutrients needed for the plants (like phosphor, oxygen dissolved in the water, ). Therefore, in some embodiments/implementations, the water inlets are attached to the side of the water tank in any case, although they do not necessarily have to point to the final end of the water tank if the water flow is assured by the inclination of the tank.
  • the water tank also contains at least one sink to remove the water from the water tank as the water inlets add water. In this way, a flow on the surface of the water may be established without raising the level of the water in the water tank.
  • the floating speed of the small grow fields can be controlled by the inclination of the water tank and/or the speed with which the water inlets blow the water into the tank.
  • the water tank can be separated into several grow areas or zones. In each grow area (which is a growth sector or zone), the growth
  • parameters may be different, e.g. nutrients may be added in another concentration or the light intensity or the light spectrum of the light sources of the light fixtures might be different for each grow area. If the plants have reached the end of the growth cycle, the grid is moved aside (to a side, up or down, or open like a water lock) and the grow fields move on to the next growth area where they can be exposed to one or several different grow parameters like illumination, temperature or nutrients. Furthermore, the light intensity and/or light spectrum may also be adjusted to the water flow speed. The waterway at each station/grow area may have a different depth level and temperature than the previous or subsequent one.
  • a Controlled Agricultural System particularly for hydroponic growth, comprising at least one hydroponic arrangement according to any one of the 4 th to 8 th aspect of "Hydroponics", further comprising: an actuator device, comprising one or more actuators able to adjust parameters of the hydroponic arrangement, e.g., water inlet, water sink, water grid, nutrient dosing feeder, light fixture.
  • an actuator device comprising one or more actuators able to adjust parameters of the hydroponic arrangement, e.g., water inlet, water sink, water grid, nutrient dosing feeder, light fixture.
  • a data storage device for storing reference data of the parameters of the hydroponic arrangement
  • a computing device configured to control the parameters of the hydroponic arrangement by means of the actuator device and based on the data of the parameters stored on the data storage device.
  • the computing device can control the water flow, and the grow parameters like nutrient concentration and illumination by means of respective actuators.
  • the system may control these parameters based on fixed values provided in a database or based on sensor inputs e.g. from cameras, light sensors, temperature sensors or chemical sensors.
  • the database is stored in a data storage device that may be based locally, in a network or the cloud.
  • the sensor device can comprise one or more sensors able to sense/detect growth parameters of the plants on the grow fields and/or control parameters of the hydroponic arrangement, e.g. flow speed indicator, thermometer, photometer, color detector, camera.
  • sensors e.g. flow speed indicator, thermometer, photometer, color detector, camera.
  • the information about the growth status or the status of the grow system can be provided to customers, the farmer or other third parties.
  • adjusting the parameters of the hydroponic arrangement e.g. water flow, illumination, controlling of the grids, concentration of nutrients in the water of the waterway, temperature of water and/or ambient air, by means of the actuator device and based on respective data of the parameters retrieved from the data storage device, with the goal that the plants are ready for harvest when they arrive downstream at a final position,
  • This element of the disclosure describes an automated processing line for growing plants.
  • growth trays for respectively growing at least one plant, the growth trays being moveable along the processing line from a first growth zone to a last growth zone,
  • Controlled Agricultural System is configured for moving the growth trays along the processing line
  • defined growth conditions can be applied, for instance a defined illumination (intensity/spectral composition), temperature, humidity or the like.
  • the growth conditions can be optimized regarding a certain growth stage of the plants. In case of a Hydroponic arrangement (see
  • the growth zone can be a "grow area” discussed there (see in particular the 9 th aspect of "Hydroponics”).
  • the first growth zone can for instance be optimized for the sawing or seeding or for an early growth stage. After having reached a certain size or growth stage, the plants may require other growth conditions to maximize the yield. Humidity and temperature may for instance be lower than during early seeding.
  • the plants can be moved to the next growth zone of the processing line. After a further growth there, the plants are moved to the next growth zone until they reach their final growth stage, becoming ready for harvesting in the last growth zone.
  • the agricultural system is equipped with growth trays (e.g. grow fields in "Hydroponics").
  • the trays can receive a container or receptible or the like, they can also have a bowl- or receptible-like shape themselves.
  • a defined volume for receiving soil or hydroponics or any other matrix material for growing plants is provided at each growth tray.
  • at least one or also a plurality of plants can be grown at or on each growth tray.
  • the agricultural system is configured for moving the growth trays along the processing line, either continuously (like in a conveyer oven) or in steps from sector to sector. In the latter case, the growth trays are moved on further after having stayed a certain time at the respective growth zone, which can be predefined or depend from growth data measured with a sensor device, for instance a camera or the like.
  • a sensor device for instance a camera or the like.
  • the growth trays can also be connected in groups, each group forming or being a resizable growth area. Those growth areas can be moved through the farm as described in in "Resizable Growth Area".
  • the agricultural system additionally comprises a treatment location. There, defined treatment conditions can be applied, see in detail below. Therein, the agricultural system is configured to unloading one or some of the trays from the processing line prior to having reached the last growth zone, while other trays pass by on the processing line. In some embodiments/implementations, this is done automatically. The other trays are moved further from sector to sector along the processing line.
  • Agricultural System of the 1 st aspect of the "Horticulture Processing Line”, configured for reloading the at least one tray to the processing line after the treatment at the treatment location.
  • the at least one tray is reloaded to the processing line later on.
  • it might be reloaded via the first growth zone, even though the growth conditions there might not be appropriate for plants having been unloaded at a later stage.
  • a gap can be left between two trays fed one after the other to the first growth zone. Due to the clocked movement, the gap propagates along the processing line, until the tray of the treatment location is reloaded into the gap at the appropriate processing stage.
  • Agricultural System of the 3 rd and 4 th aspect of the "Horticulture Processing Line”, configured for leaving a gap between two trays fed one after the other to the first growth zone, wherein the at least one tray is reloaded to the processing line into the gap.
  • Providing a treatment location for selectively unloading trays from the processing line can enable a high output and good quality, while only a minor or even no human interaction or support is required. Since only a few trays are unloaded selectively, the overall throughput remains high. For instance, not more than 40 %,
  • Unloading individual trays from the processing line can also protect the trays remaining on the processing line, for instance in case of a pest or fungal infestation or other contamination. Further criteria for unloading plants from the processing line can be their size (too small/too large) or fruit yield (too few/too many fruits).
  • the growth conditions at each growth zone may be optimized regarding the respective growth stage.
  • the unloading of individual trays can also be used for a further optimization of the growth conditions applied at a specific sector.
  • the light recipe e.g. the spectral composition or intensity of the light, can be adapted, not only to the plant type, but even to individual lots.
  • the treatment applied in the treatment location can be an illumination treatment (specific lighting with VIS, UV and/or IR light), a low or a high temperature treatment, a gas absorption (for instance of ethylene), an insect attraction treatment (to treat a pest infestation) and/or a humidity treatment and/or a non-lighting treatment (OFF-period) in a dark environment.
  • an illumination treatment specifically lighting with VIS, UV and/or IR light
  • a low or a high temperature treatment for instance of ethylene
  • a gas absorption for instance of ethylene
  • an insect attraction treatment to treat a pest infestation
  • OFF-period non-lighting treatment
  • a target of the treatment applied may be to reduce or eliminate any deviation having been the reason for unloading the tray from the processing line.
  • a specific measurement may be performed in the treatment location, see above.
  • the tray unloaded from the processing line to the treatment location can be used for optimizing not only the light recipe but also other control parameters like temperature, humidity or the like.
  • the treatment conditions applied at the treatment location have a positive effect on the plant growth, they can be transferred to one or more growth zones of the processing line.
  • the treatment location can be a quarantine area.
  • the plants unloaded could be destroyed to prevent an infestation/contamination of the plants remaining on the processing line.
  • the tray or trays unloaded from the processing line might be predefined (e.g. every 10th tray) or chosen in a stochastic procedure.
  • the unloading from the processing line to the treatment location could be kind of a lot control, allowing a detailed inspection/monitoring of the plants.
  • the unloading is triggered by a sensor measurement.
  • Agricultural System of one of the preceding aspects comprising a sensor device for sensing plant growth, harvesting time, plant morphology and/or plant health and ripeness, the Controlled Agricultural System being configured for unloading the at least one tray based on a measurement by the sensor device.
  • the agricultural system comprises a plurality of sensor systems, which can be cameras, distance measuring devices or the like.
  • a sensor device can for instance be integrated into a light fixture comprising the light sources for the lighting.
  • a sensor device can be integrated into the tray.
  • a sensor device at the tray can monitor the conditions that have been applied to the plants so far (e.g. temperature, illumination and so on).
  • the data storage device can be an internal part of the agricultural system, connected or integrated into the computing device. However, the data storage device can also be provided externally, for instance in the cloud. Data storage and handling can be done using for example a distributed Blockchain ledger system that ensures accuracy and data permanency for each tray and/or sub-tray and/or plant thus allowing a producer or customer to track the history of a specific plant product.
  • a distributed Blockchain ledger system that ensures accuracy and data permanency for each tray and/or sub-tray and/or plant thus allowing a producer or customer to track the history of a specific plant product.
  • Agricultural System of the 10 th aspect of the "Horticulture Processing Line”, configured for storing the growth data individually for the trays, namely assigned to a respective tray.
  • the growth data can be data about growth conditions or sensor data measured, is stored individually for the trays. Accordingly, for some or all of the trays, the growth conditions which have been applied/measured for the specific tray are assigned to the tray.
  • the trays can be provided with a respective identifier, for instance a barcode, RFID-tag or the like. An identifier can simplify the correlation between the growth data and a specific tray.
  • 12 th aspect of the "Horticulture Processing Line" The Controlled Agricultural System of one of the preceding aspects, wherein the growth trays are provided with a respective identifier allowing an individualization of the trays.
  • a plurality of treatment locations are provided along the processing line so that the trays can be unloaded at different growth zones (in different growth stages) to a different treatment location respectively.
  • each of the treatment locations can be linked to the processing line solely.
  • the trays can be unloaded from and reloaded to the processing line, or be destroyed at the treatment location, but not transferred from one treatment location to the other directly.
  • the trays unloaded from the first processing line can also be moved along the second processing line from one treatment location to another.
  • the treatment location can be a "blind end" to which the plants are unloaded for treatment or destruction.
  • the growth trays can be transported by vehicles, in particular vehicles driving autonomously.
  • a conveyer belt can be used for a horizontal transportation.
  • the growth trays could also float along a waterway, see for instance "Hydroponics".
  • a vertical transportation can be achieved by an elevator, in particular of a paternoster-type elevator.
  • 16 th aspect of the "Horticulture Processing Line" The Controlled Agricultural System of one of the preceding aspects, wherein at least a section of the processing line extends vertically, the growth trays being transported vertically by an elevator.
  • Each growth zone can be equipped with a plurality of light fixtures, wherein each light fixture can comprise a plurality of light sources, like halogen lamps, discharge lamps, semiconductor LEDs, OLEDs and Laser, and the like.
  • light sources having different spectral properties can be mixed to adjust a spectral composition which is optimized regarding the specific plants or the growth status.
  • the light fixtures can be installed having a fixed distance to a processing line, for example, a conveyor belt, though the light fixtures in different sectors can have different distances.
  • the light fixtures can be installed in a flexible way, so that their distance to the plants can be varied over time, in some embodiments/implementations automatically.
  • At least one tray is automatically unloaded from the processing line to the treatment location prior to having reached the last growth zone.
  • a Computer program product comprising:
  • the program instructions can use the data to calculate/generate a ‘virtual twin facility’, see also "Measuring Patterns” below, of such a controlled agricultural system and show it graphically to a producer or customer for an informed interaction and control.
  • Demands may comprise plant quantity, plant quality, post-harvesting quality, and/or storage and delivery time.
  • Plant quality is mainly defined by primary and secondary metabolites as well as appearance (color, morphology).
  • Plant quantity is defined by yield (fresh or dry weight). Plants comprise herbs, vine crops, microgreens, leafy greens, fruits and the like.
  • a growth recipe comprises values for a light recipe (spectrum, intensity, photoperiod), content of CO2- and other gases in the air, temperature (air, soil), humidity, nutrients, EC (electrical conductivity), pH, H2O, chemical and biological composition of the soil, Hydroponics and aeroponics parameters, etc.
  • a light recipe may comprise a time-sequential set of individual light recipes.
  • the actual setup of the agricultural facility / plant growing facility (light sources, light fixtures, placement of light fixtures, actuators, sensors) will be different for almost every grower so that a pre-defined growth recipe might not provide the optimal result. New requirements that have not been tested before might also not lead to the desired results.
  • a recipe may be suited and applicable to generate desired growth conditions in a 2D and 3D agricultural environment.
  • a group of sensors or a sensor device system which comprises a plurality of sensors, for measuring the actual plant-growth relevant data can be of interest.
  • This data can for instance be used for triggering actuators and/or can be stored, for example in a general platform, e.g. a digital platform like an online-platform (e.g. in the cloud) or a local data storage device.
  • a general platform e.g. a digital platform like an online-platform (e.g. in the cloud) or a local data storage device.
  • 1 st aspect of "Measuring Patterns” A Method for arranging a plurality of sensors in an agricultural facility, e.g. in a plant growing facility,
  • the sensors being of the same type (first type), wherein
  • Re-positioning means that the sensors are placed at another location in the agricultural facility and/or are oriented in another direction. Thus, the sensors are re-located and/or re-oriented. For instance in case of optical sensors, e.g.
  • the re-orientation can change the detection field even without a re-location.
  • step iii From steps i)/ii) to step iii), the distribution of the sensors can be optimized. This means that the farm is covered sufficiently while the overall number of sensors is kept as low as possible. This can reduce the overall cost of the facility. With the approach described here, an optimum coverage can be achieved nevertheless.
  • the agricultural facility is measured with a high (global or local) sensor density initially (first relative arrangement, steps i/ii).
  • sensors are for instance placed only where the measured values differed significantly (in time or locally). In other words, sensors which do not provide new information, as the data that they are providing is almost identical to neighboring sensors, will be removed / re-positioned. Likewise, the overall number of sensors can be reduced.
  • the measurement of step ii can for instance cover a time interval of at least 1 hour, 2 hours, 4 hours, or 6 hours. Possible upper limits are for instance 8, 6, 4, or 2 weeks, further possible upper limits being for instance 10 days, 8 days, 6 days or 4 days.
  • a time interval covering one or more days can give an impression over the circadian rhythm of the plants (covering day/night cycles).
  • the sensors being "of the same type” are adapted for measuring the same physical quantity.
  • these sensors are identical in construction.
  • a physical quantity measured can for instance be the temperature, humidity, leaf temperature, VPD (vapor pressure deficit), substrate moisture, substrate temperature, or EC (electrical conductivity), further, the pH-value, wind/air velocity, or PAR (photosynthetically active radiating) can be measured. It is also possible to measure vibrations, or sound, but also camera imaging solutions (including hyperspectral imaging) can be implemented.
  • the sensor system may also be configured to measure the geometrical layout and texture of an agricultural environment, like a vertical farm or a greenhouse.
  • the "plurality" of sensors can for instance be at least 5, 10, 20, 30 or 40 sensors (with possible upper limits of for instance not more than 1000, 500 or 100).
  • the local areal density is taken locally, namely in a subarea of the growth area of the farm.
  • the growth area is the total area used for growing plants in the farm.
  • a subarea, in which the local aerial density is taken can for instance cover not more than 70 %, 50 % or 30 % of the growth area (possible lower limits are for instance at least 1 %, 5 % or 10 %).
  • the local areal density of the sensors being higher in the second relative arrangement than after step iii).
  • the first and the second relative arrangement differ at least partly. From the first to the second relative arrangement, at least some of the sensors are re- positioned.
  • step iii the growth area of the farm can be scanned successively.
  • the sensors form a scan field with a high local areal density of the sensors, these scan fields cover the growth area step by step. In a sense, the scan fields are moved across the farm.
  • 5 th aspect of "Measuring Patterns” The Method of the 3 rd or 4 th aspect of "Measuring Patterns", wherein the local areal density of the sensors is the same in the first and the second relative arrangement.
  • the measuring areas are respectively smaller than the overall growth area of the farm (the area of the farm used for growing plants).
  • the sensors are moved across the growth area in the high density arrangements (scan fields), thereafter the same sensors are placed with a smaller local aereal to monitor the farm during normal operation.
  • the measurements with the first sensors and the additional sensors can be performed one after the other or simultaneously.
  • steps ii) and v) can be performed at the same time or subsequently.
  • a local areal density of the first sensors is smaller in a region where a local areal density of the additional sensors is larger;
  • a local areal density of the additional sensors is smaller in a region where a local areal density of the first sensors is larger.
  • correlations or dependencies between the different sensor types are taken into account to reduce the overall number of sensors.
  • a correlation can for instance exist between temperature and humidity.
  • a computing device can be configured to render the digital facility twin.
  • the rendering can be performed based on the data stored in the data storage device.
  • the positions of the sensors for the first relative arrangement can be
  • step iii) the sensor positions for step iii) and/or step vi).
  • the Agricultural System can be configured to be able to manage the positioning (including orientation and inclination) and re-positioning of the sensors of the sensor devices for monitoring the plant growth and, optionally, the status of the plant growing facility (e.g. for the maintenance of the equipment used in the plant growing facility) based on the data stored in the data storage device.
  • this element of the disclosure can relate to a Controlled Agricultural System, particularly for breeding, growing, cultivating and harvesting in an agricultural facility, e.g. in a plant growing facility, comprising at least one sensor device, comprising a group of sensors able to measure environmental parameters (e.g. temperature, light intensity, etc.).
  • the Agricultural System can comprise a computing device, configured to be able to access and control the at least one sensor device and the data storage device.
  • the computing device is configured for the positioning and re-positioning of the sensor.
  • the Agricultural System can also comprise a data storage device (e.g. platform/cloud) for storing data about the plant growing facility (e.g. layout, size, placement of light fixtures, actuators, etc.) and the at least one sensor device (e.g. types of sensors in the groups, number of sensors per group, range of sensors, etc.), The positioning and/or re-positioning can be managed based on the data stored in the cloud.
  • a data storage device e.g. platform/cloud
  • data about the plant growing facility e.g. layout, size, placement of light fixtures, actuators, etc.
  • the at least one sensor device e.g. types of sensors in the groups, number of sensors per group, range of sensors, etc.
  • the computing device can be configured to access and control the sensor device system and the data storage device/platform. Furthermore, the computing device is configured to access the set of measurement data, to analyze them and to compare them with other data sets, for example, stemming from other controlled agricultural systems, or from standardized or ideal data sets or current or historical user data sets.
  • the sensor device system may comprise a variety of different sensor types in order to measure a variety of relevant plant growth data as well as post- harvest plant data, like the concentration of certain enzymes or the concentration of vitamins and glucose.
  • the sensor device system may comprise a variety of different sensor types in order to measure and recognize pest-related and/or disease related parameters.
  • the sensor device may be configured to establish a communication and/or data processing and analyzing network between themselves.
  • the status of the plant growth and of the plant growth facility needs to be understood.
  • a set of sensors need to be deployed in the facility. These sensors may include ambient sensors.
  • the group of sensors can be able to measure one or more of the following parameters: temperature, humidity, leaf temperature, VPD (vapor pressure deficit), substrate moisture, substrate temperature, EC (electrical conductivity) and pH-value, velocity, PAR (photosynthetically active radiating).
  • VPD vapor pressure deficit
  • substrate moisture substrate temperature
  • EC electrical conductivity
  • pH-value pH-value
  • velocity PAR
  • PAR photosynthetically active radiating
  • sensors measuring vibrations, sound but also camera imaging solutions including hyperspectral imaging may be used.
  • the sensor system may also be configured to measure the geometrical layout and texture of an agricultural environment, like a vertical farm or a greenhouse.
  • a sensor can also be an optical detection device, particularly for imaging methods, e.g. a camera.
  • the sensors are able to communicate with each other or with a control unit.
  • the sensors may form local sub- systems with a respective control unit.
  • the local sub-systems can be adaptively reconfigured based on output data from an artificial intelligence network system.
  • An overall control unit e.g. the computing device
  • the sensor data may be fed into an Artificial Intelligence system that, after calculating, outputs data that can be used for plant modelling and the steering of actuators.
  • the computing device is configured to manage the positioning and re-positioning of sensors for monitoring plant growth, plant harvesting, plant placement on empty places and, optionally, the status of the plant growth facility (e.g. for the maintenance of the equipment used in the plant growth facility).
  • the Controlled Agricultural System can comprise an actuator device able to adjust growth parameters of plants (e.g. water, nutrient, light (intensity, spectrum), humidity, temperature, air ventilation, water circulation, pesticides) and/or to adjust the position and shape of a light fixture and/or to change the layout of variable building design parameters of a building or housing or cabinet of the agricultural facility and/or to close or open the roof of the agricultural facility (e.g. greenhouse) and/or to change a location of a moveable agricultural growth cabinet inside the agricultural facility.
  • growth parameters of plants e.g. water, nutrient, light (intensity, spectrum), humidity, temperature, air ventilation, water circulation, pesticides
  • Rendering a digital model of the plant growing facility including indicating the positions of the sensors by means of the computing device based on the data stored in the data storage device;
  • a layout of the plant growth facility is uploaded to the platform (data storage device) incl. all relevant dimensions of the facility and growing zones (length, height, height of traces, distance between rows, amount of rows, etc.). Additionally, the available amount of sensor types and amount of different sensors is entered into the controlled agricultural system, e.g. via a user dashboard.
  • the computing device of the controlled agricultural system is configured to generate a 3D model and/or a texture map of the facility, in other words a digital facility twin, and suggests where to position and how to orientate the sensors (e.g. horizontally and vertically in the facility, angle and direction of orientation), based on the input data.
  • 12 th aspect of "Measuring Patterns” The Method of any of the preceding aspect of "Measuring Patterns", comprising
  • the computing device may be configured to suggest, how long to keep the sensors at the respective places/positions and (if needed/optionally) where to put them for a 2 nd or 3 rd time period to generate a (even more) complete overview of the facility and microclimate within the facility.
  • the suggestions may be based on experience with similar facilities or a calculation how the sensors should be placed on a 3D-grid in the facility to collect data (based on the (spatial) range of the specific sensor).
  • a goal may be to leave a respective sensor as short as possible at a specific location to gather the required data.
  • the computing device is configured to suggest the positioning for the sensors based on the farm layout and size. It is also configured to suggest the minimum requirements of sensor data acquisition for a given agricultural system so that for each growth stage and plant maturity the acquired farm data can be considered representative for the digital facility twin database. It is preferred to collect the relevant data as fast as possible so that the farm is "understood and approved" by and for the controlled agricultural system (i.e. as a first/initial setup).
  • this element of the disclosure also relates to a Controlled Agricultural System: [000269] 15 th aspect of "Measuring Patterns”: A Controlled Agricultural System configured for performing a method according to any of the preceding aspects of "Measuring Patterns”.
  • the controlled agricultural system may be configured to automatically suggest missing sensors or how additional sensors could help to
  • the controlled agricultural system may also be configured to show tutorials how to correctly install and use the different sensors. Once the data of a location (of a sensor) is collected, the system/platform informs the grower and suggests the next possible sensor location.
  • drones For short or temporary measurements, drones, other mobile robots (automated agricultural vehicles AGV) or humans may also be used to create/collect the sensor data.
  • AGV automated agricultural vehicles
  • the controlled agricultural system When the initial setup is done, i.e. the controlled agricultural system "understands" the different regions of the facility, the controlled agricultural system is able to suggest placing/permanently installing the sensors in relevant/problematic zones.
  • the computing device of the controlled agricultural system may also be configured to show the locations of the sensors in a kind of "heat maps" to constantly monitor these locations. If different seasons are to be considered, seasonal maps may be relevant/comprised.
  • the sensors can also be used to assess the status of the equipment of the plant growing facility and to plan for maintenance or crop rotation (bringing the plants in another area of the facility if the maintenance can affect the yield).
  • the Sensors may also be able to change their position during the growth of the plants (e.g. angle of inclination or height above ground for positioning).
  • the sensors may also be included in the earth / ground or in the water.
  • the sensors may also be configured to provide real-time data about growth - for example growth of fruits in kg/m 2 / day.
  • the controlled agricultural system may establish all relevant documentation, e.g. in a project report, regarding input factors used and results achieved, including for example agricultural risk
  • post-harvest measurements and risk assessments may be executed and documented as well.
  • the data may be transferred via software interfaces automatically, e.g. stored on the platform.
  • a computer program product comprising:
  • Horticulture facilities like greenhouses and vertical farms are getting more and more automated.
  • An interesting approach discussed here is to move the plants through the horticulture/agricultural facility, namely to transport them through the facility depending on the specific growth phase. Therein, different light or growth recipes can be applied in different zones along the workflow (and thus plant flow).
  • the recipes can be pre-designed in terms of the respective plant type and growth phase.
  • Each recipe can define a specific lighting scenario, e.g. regarding the intensity and spectral composition, adapted to the growth phase of the plants in the respective zone.
  • other parameters like temperature, humidity, air flow, etc., can be customized in the different zones.
  • a sum, derived from summing up all areas A n is smaller than or equal to a total growth area available in the agricultural facility ( ⁇ A n £ total growth area);
  • the size of the areas A n is increasing with increasing number n (A n ⁇ A n+i ).
  • the growth of the plants proceeds with increasing number n, so that GP n+i is the growth phase subsequent to the growth phase GP n (which applies for all values of n from 1 to N).
  • the plants can be moved through the facility from zone to zone, namely from Z n to Z n+i , see in detail below.
  • the grow scenarios can in particular be derived from a growth recipe. These can for instance be or comprise lighting scenarios.
  • light fixtures can be adapted to emit light with a spectral composition required by the plants in the actual growth phase. Therein, the light fixtures can be fixed or pre- defined in their respective spectral properties, which can give a cost benefit in comparison to providing light fixtures with adaptable spectral properties, even though a conveyer mechanism or the like is may be required for transporting the plants.
  • other actuators for adjusting other environmental conditions can be provided in the different zones.
  • Typical growth phases can for instance be germination, growth and maturation.
  • some of the zones Z n can have the same area A n .
  • the zones for maturation and growth can have the same size.
  • providing different zones while the size remains unchanged can be advantageous, as for instance growth and maturation may require a different illumination.
  • LRDT light recipe design tool
  • agricultural facility particularly a plant growing facility and/or an hydroponics facility, comprising:
  • an actuator device for moving plants along a plant production line according to a workflow the plant production line being grouped into a number of N zones Z n (Z I ... Z N ), wherein N is an integral number greater than 1 (i.e. 2, 3, 4, ...);
  • each one of the groups being arranged to illuminate a dedicated zone, respectively (i.e. group 1 illuminates zone 1 , group 2 illuminates zone 2, etc.);
  • a data storage device e.g. platform/cloud for storing data comprising the agricultural light fixtures, light recipes for the plants and the workflow,
  • a computing device configured to control the agricultural facility by means of the actuator device and agricultural light fixtures according to the workflow.
  • the various fixed spectra may be selected based on plant need, environmental conditions and other requirements (user demands, Bio-mass to be produced, time to harvest, etc.).
  • An agricultural/horticultural light fixture that offers a fixed spectrum may include means for dimming the spectral intensity.
  • the geometrical layout of an agricultural light fixture and beam spread are important features. All these features of those light fixtures may be stored in a database (local, cloud) available to users via a platform. The distance between light fixtures and plants (canopy) may also be taken into consideration for a suited agricultural/horticultural facility layout/setup.
  • the controlled agricultural system further comprises a computing device, which may be based locally (on- site) or in a (centralized) network or the cloud.
  • the computing device may be configured to be able to run the LRDT software program.
  • the computing device may also have access to the database containing data about the features of the agricultural light fixtures of the controlled agricultural system.
  • 10 th aspect of "Light Recipes & Workflow” The Controlled Agricultural System according to any of 6 th to 9 th aspect of "Light Recipes & Workflow", wherein the actuator device further comprises actuators able to adjust growth parameters of the plants, e.g. water, nutrients, light (intensity, spectrum), humidity, temperature, air ventilation, water circulation, pesticides.
  • the actuator device further comprises actuators able to adjust growth parameters of the plants, e.g. water, nutrients, light (intensity, spectrum), humidity, temperature, air ventilation, water circulation, pesticides.
  • the Agricultural System being configured to provide, in addition to the illumination with the light fixtures, in at least one of the Zones Z n a defined temperature, humidity and/or CO2 level.
  • the computing device may be configured to control the agricultural facility by means of the actuators, light fixtures, etc., according to the workflow.
  • the sensors may sense the growth status / growth phase of the plants as well as a health status of the plants.
  • Health checkpoints along the flow paths can be planned / suggested by the LDRT and implemented into the horticultural plant layout and function.
  • the entire through-put cycle of a specific plant can be monitored and correlated with this specific plant, for example by using a distributed Blockchain ledger method thus allowing that the entire treatment cycle of each (individual) plant as well as the plant’s health condition is documented accurately and permanently.
  • the data storage device further comprises data about the agricultural facility including its equipment (e.g. layout, size, placement of light fixtures, actuators, sensors, etc.) and growth recipes for the plants.
  • equipment e.g. layout, size, placement of light fixtures, actuators, sensors, etc.
  • the present element of the disclosure further proposes a Light Recipe Design Tool (LRDT), which is a software program with executable program steps.
  • LRDT Light Recipe Design Tool
  • the facility layout and the workflow are inserted (per upload of layouts or pictures, grouping of zones/production stages, insert of dwell (delay or rest)) times, etc.).
  • user demand bio-mass
  • post-harvesting treatment environmental conditions
  • the LRDT is connected to a database of light recipes for a variety of plants, growth stages, dwell times, On-Off-cycle, and the like, including the light fixture-related and light fixture- plant related data sets (see above).
  • a method for agricultural management particularly for breeding, growing, cultivating and harvesting in an agricultural facility, e.g. a plant growing facility, comprising:
  • At least one controlled agricultural system according to one or more of the 6 th to 15 th aspect of "Light Recipes & Workflow", and the steps of
  • Rendering a light recipe design by proposing a setup of the facility including its equipment (light fixtures, actuators, sensors, etc.), which setup is adapted to the fetched light recipe and the workflow, by means of the computing device based on the data of the previous steps.
  • the LRDT is configured to develop a light recipe design (LRD) for the entire plant treatment time for the grower’s facility.
  • LPD light recipe design
  • This means the LRDT is configured to take into account the size of the facility, the size of the plants in each growth stage (growth phase), the time the plants remain in each grow stage, the number of grow stages and the like (see above). Based on this, the LRDT proposes a setup for the facility, indicating the space required for each grow stage, where to put light fixtures and which types of light fixtures including the respective
  • the LRD may be uploaded to the grower’s horticulture Internet of Things (loT) platform.
  • the horticulture loT platform may be configured to do real-time asset tracking to monitor the plant movement along the workflow and match it to the LRD and give feedback to the grower if it matches or if something needs to be adjusted to the LRD. Adjustment to the workflow could be for example slowing down/speeding up the process time at a growth stage.
  • Asset tracking may be done for example by sensors and radio frequency (RF), RFID or barcode and QR-code scanning of plant trays or plant pots during the production/workflow.
  • RF radio frequency
  • 19 th aspect of "Light Recipes & Workflow” The method for agricultural management according to the 18 th aspect of "Light Recipes & Workflow", whereby moving of the plants is conducted in order to cross each zone (Z1 ; Z2; Z3) according to a pre-defined schedule.
  • 20 th aspect of "Light Recipes & Workflow” The method for agricultural management according to any one of the 16 th to 19 th aspect of "Light Recipes & Workflow", further comprising the steps of
  • Measuring and collecting data by means of the sensor device comprising data for detecting the growth status of the plants, and adapting the timing for moving the plants to the detected status of the plants.
  • a computer program product comprising:
  • the LRDT software program may be configured to improve itself based on feedback (customer, produces) by executing Deep Learning or Al methods.
  • the LRDT software program may be licensed to other parties.
  • a horticultural plant facility can have many flow paths, it is preferred to keep it as simple as possible, e.g. following a straight line through the horticultural plant facility (see FIG. 19).
  • Plant factories can have a larger number of plant rows and paths, probably many hundred.
  • Flow paths can be stacked upon each other in vertical layers thus providing a 3D path system. In principle, the flow paths could reverse their direction so that the plants move again through a previous light setting (but in opposite direction).
  • PROPHYLAXIS PROPHYLAXIS
  • a controlled agricultural system comprises a first sensor device for acquiring data in relation to environmental parameters (environmental data), a data storage device for storing the data from the first sensor device and a computing device configured to analyze the data of the data storage device in order to identify critical situations and propose suitable
  • Parts of the controlled agricultural system for example the computing device or the data storage device may be based locally or else on a network or cloud.
  • the term environment in this case comprises the greenhouse per se and the region outside of the greenhouse.
  • the environmental parameters include data (environmental data) such as temperature, wind speed, humidity, light factor, ozone content of the air, UV radiation, but also season or amount of pollen or number of insects in the air. It is likewise possible to monitor the temperature at the leaves of the plant. If the temperature there falls below the dew point, there is then an increased risk of fungi afflicting the plants. As to appropriate measures against such risk, see also the element "Fungi Growth Inhibition" of the group "Plant Health & Growth” of the disclosure.
  • the controlled agricultural system is equipped with sensors that are able to acquire the aforementioned abiotic and biotic environmental parameters at adjustable time intervals or else continuously.
  • a controlled agricultural system comprises a first sensor device for acquiring data in relation to environmental parameters (environmental data), an actuator device, a data storage device for storing the data from the first sensor device and a computing device configured to analyze the data of the data storage device in order to identify critical situations and introduce suitable countermeasures with the aid of the actuator device where applicable.
  • the controlled agricultural system may also be configured to just giving notifications/recommendations in the beginning or wait for confirmation by the operator before executing the recommended countermeasures. For instance, the system may recommend a countermeasure like "In the current situation, grower XYZ implemented this or that countermeasure with the effect of ABC. Do you want to execute?" Alternatively or in an advanced stage of the project the system may be configured to decide and implement countermeasures autonomously.
  • the measure using pesticides should be execute as described in the user manuals.
  • the user manuals of seed-, substrate-, fertilizer-, pesticide- and equipment companies should be stored and continuously updated in the system.
  • the countermeasures should also take into account the expected yield / harvesting time (and acceptable reductions or delays). Afterwards the system can give suggestions how often we increase the light intensity, or change the spectrum.
  • 3 rd aspect of "Prophylaxis” The controlled agricultural system according to the 1 st or 2 nd aspect of "Prophylaxis", comprising a second sensor device for acquiring data in relation to the state of the plants (plant data), wherein the data storage device is configured to store the data from the second sensor device.
  • the controlled agricultural system is equipped with sensors that are able to acquire the state of the plants, in some embodiments /
  • implementations the stress state thereof.
  • these may be optical, chemical or electrical sensors, which identify, for example, the growth of the plants (density of the leaves, height of the plants, plant morphology, leaf area index), measure the color and reflectivity of the leaves, the thermo-luminescence thereof or the chlorophyll fluorescence thereof or the abscisic acid (ABA) luminescence thereof and hence the health of the plants and in particular the stress state thereof, and are able to supply these (plant data) to an evaluation.
  • ABA abscisic acid
  • the controlled agricultural system is configured to measure the recovery state, i.e., the reduction of stress parameters, of the plants.
  • These information items are stored, in some embodiments/implementations in a plant-specific manner, in a data storage device and are analyzed by a computing device.
  • the analysis can be performed using methods connected to artificial intelligence (Al), such as deep learning.
  • Al artificial intelligence
  • the goal of the analysis is to identify and/or predict an environmental situation that is accompanied by an increased occurrence of diseases or pest infestation.
  • the analysis may also include the plant state, for example the degree of maturity. This is because, as a rule, the risk of pest infestation also increases with advancing degree of maturity.
  • the system may also be able to take up these measures in preventative fashion, with care having to be taken that the plants are not adversely affected, or not adversely affected too much, by the measures.
  • the measure there is a trade- off between potential use and possible damage by the measure. This is weighted with the probability of the occurrence of the disease or the pests and the probability of damage by the measure, and hence a decision is made as to whether or to what extent, the measure is carried out.
  • 5 th aspect of “Prophylaxis” The controlled agricultural system according to any one of the 1 st to 4 th aspect of “Prophylaxis", wherein the first sensor device comprises one or more sensors for one of the following environmental parameters or a combination thereof: air composition, air temperature, air humidity, wind speed, light intensity, light spectrum, number of pests, light factor, ozone content of the air, UV radiation.
  • 6 th aspect of "Prophylaxis” The controlled agricultural system according to any one of the 1 st to 5 th aspect of "Prophylaxis", wherein the second sensor device comprises one of the following sensors or a combination thereof: temperature sensor, gas analyzer, photodiode, spectrometer, camera.
  • the actuator device comprises one or more of the following actuators or a combination thereof: plant light fixture with various light sources, UV radiation source, applicator for pesticides, applicator for herbicides, applicator for fungicides, applicator for useful creatures, mobile robot unit, drone, heater or cooler, ventilator.
  • Measures may include the irradiation with UV (or generally a change in the light recipe), the closing of windows and doors, the reduction of the humidity or the temperature, or else the automatic release of useful creatures or the automatic application of correspondingly licensed pesticides by means of a mobile robot unit, etc.
  • Placing UV-reflecting mats below the plants may be a passive measure for reducing the infestation of pests. Thus, pests can no longer distinguish leaves from the ground, and so they settle less frequently on the plants.
  • Effects on the plants can be monitored by controlling the plant stress (but also by checking the growth with cameras, etc.). If need be, preventative measures may be terminated if the stress on the plants becomes too large. At the same time, environmental parameters are checked regularly to see when the risk of an outbreak of disease or infestation of pests has abated.
  • a method for agricultural management comprising the following method steps: measuring relevant environmental parameters in a target area, analyzing the measurement data and identifying critical situations such as environmental conditions that are inexpedient for plants, for example, with an (elevated) risk of the plants being afflicted by disease or infested by pests or whether the plants are already afflicted by disease or infested by pests, proposing, or alternatively automatically introducing, countermeasures.
  • the method enables to identify critical situations such as environmental conditions that are inexpedient for plants, and optionally taking countermeasures. Furthermore, reference is made to the description above, the features described there shall also be disclosed in terms of the method.
  • 1 1 th aspect of "Prophylaxis” A machine-readable computer product, comprising a multiplicity of program instructions which, when executed on the computing device of the controlled agricultural system according to any one of the 1 st to 7 th aspect of "Prophylaxis", cause the Controlled Agricultural System to execute the method according to the 8 th or 9 th aspect of "Prophylaxis”.
  • a controlled agricultural system comprises a sensor device able to measure distinctive
  • a data storage device for storing reference data of plants
  • a computing device configured to compare the data measured by the sensor device with the respective reference data stored on the data storage device and to identify stress, diseases, pests or any other critical condition of the plants from the result of the comparison.
  • the controlled agricultural system comprises a computing unit configured to identify stress or disease from the data measured by the sensors. For instance, if the computing unit detects morphological changes of the leaves, the controlled agricultural system delivers a warning to the user (farmer).
  • the controlled agricultural system comprises sensors, which are able to measure distinctive characteristics of plants, e.g. color changes of the leaves by means of optical sensors (e.g. sensors for spectral measurements), plant morphology by means of a camera, etc.
  • sensors which are able to measure distinctive characteristics of plants, e.g. color changes of the leaves by means of optical sensors (e.g. sensors for spectral measurements), plant morphology by means of a camera, etc.
  • a camera is configured to take pictures of the leaves in regular intervals (e.g. minutes, hours, days). The pictures are then compared to earlier pictures or pictures of a healthy plant. "Earlier pictures” can mean that one or a sample of earlier pictures have been taken for the purpose of later comparison or that an average of the earlier pictures has been calculated with respect to the respective parameters like inclination, leaf-size, roll-up, etc.
  • a stress situation can be detected, if the picture analysis shows a difference in certain morphology parameters which are larger than a certain threshold, for instance:
  • UV radiation source able to emit ultraviolet (UV) radiation
  • irrigation system able to emit ultraviolet (UV) radiation
  • ventilation system able to emit ultraviolet (UV) radiation
  • heating/cooling system able to emit ultraviolet (UV) radiation
  • feeder for dosing fertilizer and/or pesticides.
  • the system may optionally initiate a counter-measure (e.g. irrigation).
  • a counter-measure e.g. irrigation
  • the controlled agricultural system further comprises respective actuators (e.g. irrigation system).
  • the pictures currently taken may be compared with existing reference pictures of corresponding plants in good health and condition retrieved from a database.
  • the database is stored in a data storage device that may be based locally, in a network or the cloud.
  • the identification process may be performed by using picture recognition algorithms, e.g. deep learning.
  • picture recognition algorithms e.g. deep learning.
  • the different morphological changes can be linked to other causes (e.g. hanging leafs due to not enough water or due to other environmental parameters such as a too high salt concentration). These causes may also depend on the specific kind of plant, which may also be taken into consideration when analyzing the pictures. Artificial
  • Intelligence programs may be used to monitor, collect and interpret such sensor generated data and calculate forecast or prediction models in order identify and reduce plant stress.
  • 6 th aspect of “Stress Detection” The controlled agricultural system according to any one of the 1 st to 5 th aspect of “Stress Detection", wherein the sensor device comprises one or more of the following sensors or a combination thereof: imaging system, e.g. still or video camera, in some embodiments/implementations TOF camera or stereo camera, LIDAR system, environmental sensor, e.g.
  • sensors for measuring temperature, humidity and/or chemical composition of the air or soil sensors for detecting color changes of the plant, particularly of the leaves, sensors for detecting specific gases exhaled by the plants, sensors for detecting the fluorescence emitted by the plants after activation with dedicated radiation.
  • the measurement system is capable to create a 3D-representation of the leaves (e.g. by using time of flight (TOF-) cameras, stereo cameras, or LIDAR (light detection and ranging)). If the picture is only available in two dimensions, the angle of inclination or the symmetry of the leaf might be misinterpreted, as the cameras cannot look perpendicularly on each leaf. A 3D- representation helps to avoid this mistake.
  • TOF- time of flight
  • stereo cameras stereo cameras
  • LIDAR light detection and ranging
  • root morphology e.g. in hydroponics systems
  • root morphology can be measured and analyzed with regard to pests or diseases.
  • a method for agricultural management detects pests, diseases and stress of plants based on leaf characteristics as described above.
  • Stress Detection A method for agricultural management, comprising at least one controlled agricultural system as described above and the following method steps: measuring distinctive characteristics of plants in a target area by means of the sensor device and collecting these measured data of the plants, storing reference data of plants, comparing the measured data with the reference data by means of the computing device and identifying stress, diseases, pests or any other critical condition of the plants from the result of the comparison by means of the computing device.
  • Stress Detection further comprising the step of counteracting automatically by means of the actuator device, if stress, diseases, pests or any other critical condition of the plants is identified.
  • 10 th aspect of "Stress Detection” The method for agricultural management according to any one of the 7 th to 9 th aspect of “Stress Detection”, further comprising the step of establishing reference conditions before measuring distinctive characteristics of the plants in the target area, in some
  • the measurement should be conducted under standardized conditions, as different illumination (color or intensity) may affect the leaf morphology as well.
  • different measurement conditions e.g. colors of the luminaires
  • well-defined changes of illumination parameters can be used to analyze the plant stress, as it might induce changes in the leaves. This change, especially the reaction time for a respective change, can be measured and the measurement result may provide an indication about the stress.
  • 1 1 th aspect of "Stress Detection” A machine-readable computer product, comprising a multiplicity of program instructions which, when executed on the computing device of the controlled agricultural system according to the 1 st to 6 th aspect of "Stress Detection” causes the controlled agricultural system to execute the method according to any of the 7 th to 10 th aspect of “Stress Detection”.
  • a controlled agricultural system particularly for detection of plant diseases and various stages of ripening, comprising a data storage device comprising data, which are related to spectra of light, particularly of light with colors complementary to colors of parts of plants (Complementary Color Spectrum CCS), for example, complementary to discolored areas or parts of plants, an illumination device able to emit light with a color spectrum according to the data stored in the data storage device and illuminate plants, a sensor device able to detect the light reflected by the illuminated plants, and a computing device, configured to control the illumination device based on the data of the database, and further configured to analyze the data from the sensor device and detect dark areas on the plants.
  • CCS Complementary Color Spectrum CCS
  • a controlled agricultural system particularly for detection of plant diseases and various stages of ripening, comprising an illumination device able to emit light, perform a spectral light scan, comprising Complementary Color Spectra, particularly with regard to discoloration of plants or plant parts, and illuminate plants a sensor device able to detect the light reflected by the illuminated plants, a computing device, configured to control the illumination device for performing a spectral light scan, and further configured to analyze the data from the sensor device and detect dark areas on the plants.
  • DSi Discolored Spots
  • CCSi Complementary Color Spectra
  • CCSi Complementary Color Spectra
  • the detection of discoloration may be used for various tasks of cultivating plants. For instance, it may be used to track changes in plants or part of the plants, e.g. flowering, changing colors due to ripening etc.
  • the complementary color of the state of the plant is applied (either the previous state to see if it is still there or the expected state to see if it has been realized).
  • the tomatoes may be illuminated with light of the color complemental to green (i.e. reddish light (magenta)). If such illuminated tomatoes appear dark, their color is still green. Otherwise, they have already changed their color to red and may be ready for harvesting.
  • the tomatoes may be illuminated with light of the color complemental to red (i.e. cyan). If the illuminated tomatoes appear dark, their color is already red. Otherwise, their color is still green.
  • white light e.g. a white light source with a reference light spectrum that shows a good Color Rendering Index CRI (in some embodiments/implementations higher than 90), or a standardized light source with a specific reference light spectrum
  • any Discolored Spot DSi does not reflect light with a complementary spectrum (called: Complementary Color Spectrum CCSi)
  • CCSi Complementary Color Spectrum
  • FWHM Full Width at Half Maximum
  • complementary spectrum is rather narrow, in some embodiments/implementations in the range between 1 nm, and 50 nm.
  • Discoloration Color DCi The current (local) color of a discolored spot DSi is termed Discoloration Color DCi.
  • a plant can have several (local) Discoloration Colors DC1 , DC2... DCN., one Discoloration Color DCi for each Discolored Spot (DSi).
  • Complementary Color Spectrum CCSi of each of the respective Discolored Spots DSi are provided by a Complementary Light Source CLSi.
  • a horticulture light fixture may comprise several Complementary Light Sources CLSi.
  • the illumination system e.g. horticulture light fixture
  • emits only one Complementary Color Spectrum CCSi at a given time thus making it easier for sensor systems to differentiate between various reflected light colors.
  • 3 rd aspect of "Discolored Spots Detection” A controlled agricultural system, according to the 1 st or 2 nd aspect of "Discolored Spots Detection", wherein the illumination device comprises light sources, which are able to emit light of at least three different colors, in some embodiments/implementations red, green and blue.
  • the illumination unit comprises at least three light colors, e.g. red, green and blue for the RGB color-space.
  • Such illumination units are more and more frequently used in agricultural systems for illumination purposes, but the illumination unit to detect the discolored spots/disease may also be added as an independent light source.
  • the illumination unit may be arranged in the agricultural lighting system or a separate fixed installation, or in a moveable installation, e.g. on tracks or in in automated guided vehicle (AGV) or even inside a flying drone.
  • AGV automated guided vehicle
  • discolored spots DSi do not reflect (or only very minimally) light with their respective Complementary Color Spectrum CCi, discolored spots are visualized as dark spots that can be recognized and measured easily by a camera or other sensor systems (Photodiode, CCD chips with filters etc.).
  • the controlled agricultural system comprises a (scientific) database with a mapping of plant diseases, diseases-typical discolorations of the plants (for every growth stage), and the respective complementary light (Complementary Color Spectrum CCSi).
  • the database is part of a computing device.
  • the controlled agricultural system further comprises an illumination unit (lighting fixture), which may be based on LEDs (with or without phosphor
  • the illumination unit is suited to apply the complementary light
  • the controlled agricultural system is configured to control the illumination unit based on the data of the database.
  • the controlled agricultural system further comprises a sensor system (controlled by its control unit).
  • This may be a (still or video) camera.
  • the camera takes a picture of the plants (probe picture).
  • the computing device analyses each picture and looks for dark spots.
  • the computing device may also store a picture taken with normal/reference illumination, e.g. white light, which shows the leaves and other parts of the plants (reference picture).
  • the computing device compares each probe picture with the respective reference pictures. If dark spots are detected in an area, which comprises parts of the plants, this may indicate a discoloration due to a disease.
  • the controlled agricultural system applies to the plants complementary light through the illumination unit. For instance, red discolorations (with an RGB-code of e.g. #FF0000) will appear dark when illuminated with a cyan color (with the RGB-code of #00FFFF). Therefore, this measure according to the disclosure intensifies the contrast between the discolored part and the normal-colored part and makes it easier to detect a discolored part/spot (disease).
  • the controlled agricultural system applies normal (reference), non-complementary light of the discoloration to the plant in a first step and then the complementary light in a second step. This will lead to a very pronounced color difference between the first (reference) and the second (probe) picture taken with the camera, making it even easier to detect discolored parts.
  • the illumination unit illuminates the plants at different wavelengths (each wavelength range is emitted consecutively, i.e. only during separate time intervals), i.e. performs a spectral scan mode for probing discolored areas, and the computing device analyses the pictures.
  • This approach can be advantageous to detect possible diseases if the database, particularly the data for specific Complementary Color Spectra, is not available or incomplete.
  • the computing device is configured to trigger measurements of discolorations in regular or irregular time intervals or even stochastically.
  • the measurement data may be analyzed by means of deep learning algorithms.
  • the results of the analysis may be represented graphically. Such procedure may be used to monitor curing of plant diseases.
  • the computing device may also comprise a user interface, which provides feedback of the measurements to the user. Via the interface the user can also schedule measurements (once a day, once a week, during the day, at the end of the day, irregular intervals, stochastically within time intervals, etc.). The computing device then interrupts the normal illumination mode and switches the illumination to detect mode.
  • the plants are illuminated with complementary light, either applying the lights (Complementary Color Spectra) stored in the database or providing a spectral light scan, as described above.
  • the controlled agricultural system may draw down shutters or blinds prior to starting the detection mode.
  • the controlled agricultural system may further comprise an appropriate actuator.
  • the controlled agricultural system according to the disclosure that applies to the plants complementary light by means of the illumination unit can be used in a greenhouse that is with the presence of natural sun light, as well as in a completely enclosed farming environment (controlled agricultural environment).
  • illumination with complementary light will still increase the color difference between healthy and discolored unhealthy plant parts.
  • the regular illumination light is on (ON-Lighting-cycle) or OFF (OFF-lighting-cycle). If measurements are performed in the ON-Lighting-cycle, the regular illumination can be temporarily switched off during the measurement cycles and switched on afterwards.
  • Agricultural lighting fixtures may comprise artificial light sources like Light Emitting Diodes (LED) with or without conversion by using a fluorescent substance, commonly referred to as phosphor, monochromatic Laser diodes, OLED light emitting material on organic basis, Quantum Dot light emitters, Fluorescent lamps, Sodium low and high pressure lamps, Xenon and Mercury Short Arc lamps, Halogen lamps, and the like.
  • LED Light Emitting Diodes
  • phosphor monochromatic Laser diodes
  • OLED light emitting material on organic basis Quantum Dot light emitters
  • Fluorescent lamps Sodium low and high pressure lamps
  • Xenon and Mercury Short Arc lamps Halogen lamps, and the like.
  • All of the plants arranged in an agricultural or horticultural facility and managed by means of a controlled agricultural system according to the disclosure need not be illuminated with the Complementary Color Spectra (CCSi) at the same time, but can be illuminated sequentially.
  • CCSi Complementary Color Spectra
  • Complementary Color Spectrum may be directed onto a scanning device, like a moving MEMS-mirror, and then reflected onto the various parts of an agricultural plant environment (plant cultivating area) in a time sequential manner.
  • 6 th aspect of "Discolored Spots Detection” A controlled agricultural system, according to any one of the 1 st to 5 th aspect of "Discolored Spots Detection", further comprising an actuator device able to treat the plants, e.g. with water, UV- light, IR-light, nutrition, medication, fungicides, pesticides.
  • the computing device may comprise an object recognition program that determines the location of the affected plant and can then send command controls to a health sustaining system.
  • the health sustaining system may comprise appropriate actuators, e.g. by means of automated guided vehicles (AGV), which then treat the affected plant(s) (or plant area) with e.g. UV-light, IR-light, nutrition, medication, pesticides, etc.
  • AGV automated guided vehicles
  • Discolored Spots Detection a method for detecting/verifying colors or discoloration of plants, or discolored spots on plants, or discoloration of plant parts, like fruits or flowers, is proposed by providing an illumination system that illuminates the plants or parts of the plants with light with Complementary Color Spectra (CCSi) with regard to the anticipated color or discoloration of the plant, plant part or plant spot.
  • CCSi Complementary Color Spectra
  • the controlled agricultural system is configured to be able to execute the method.
  • the method for agricultural management comprises at least one controlled agricultural system and the steps of starting the detect mode of the controlled agricultural system, illuminating plants with complementary light by means of the illumination device, and screening/detecting the plants for dark areas, e.g. discolored spots that appear as dark spots when illuminated by the complementary light, by means of the sensor device.
  • 9 th aspect of "Discolored Spots Detection” The method for agricultural management according to the 8 th aspect of "Discolored Spots Detection”, further comprising the step of analyzing and identifying the cause of a detected dark area, e.g. a discolored spot, flower, fruit, etc., by means of the computing device based on data store on the data storage device.
  • a detected dark area e.g. a discolored spot, flower, fruit, etc.
  • 10 th aspect of "Discolored Spots Detection” The method for agricultural management according to the 8 th or 9 th aspect of "Discolored Spots Detection", further comprising the step of identifying the disease associated with a detected discolored spot by means of the computing device.
  • 15 th aspect of "Discolored Spots Detection” The method for agricultural management according to any one of the 10 th to 13 th aspect of "Discolored Spots Detection", further comprising the step of taking countermeasures against the identified disease, e.g. treating the affected plant(s) with e.g. UV-light, nutrition, medication, fungicides, pesticides, etc., by means of the actuator device.
  • the affected plant(s) e.g. UV-light, nutrition, medication, fungicides, pesticides, etc.
  • 16 th aspect of "Discolored Spots Detection” The method for agricultural management according to any one of the 10 th to 13 th aspect of "Discolored Spots Detection", further comprising the step of informing the user by means of the user interface that discolored spots have been identified and about the diagnosed disease.
  • 17 th aspect of "Discolored Spots Detection” A machine-readable computer product, comprising a multiplicity of program instructions which, when executed on the computing device of the controllable agricultural system according to any one of the 1 st to 7 th aspects, cause the Controlled Agricultural System to execute the method for agricultural management according to any one of the 8 th to 16 th aspects of "Discolored Spots Detection".
  • a complementary light source for emitting a first complementary light with a first complementary color spectrum, which has a first spectral gap in comparison to a white light spectrum
  • 19 th aspect of "Discolored Spots” The method of the 18 th aspect of "Discolored Spots", wherein the first spectral gap of the first complementary color spectrum lies in the green and/or yellow spectral range, the plant illuminated by the first complementary light being screened for bright areas.
  • Typical leaves having a green color show no absorption in the green/yellow spectral range. Consequently, they reflect green/yellow light, which is the reason for their green appearance.
  • the focus is not on the green leaves themselves but on any discoloration of the leaves, for instance discolored spots. Those can indicate an insufficient supply or a disease of the plant.
  • any discoloration of the leaves can be detected better. Any discoloration appears brighter as the green background is reduced.
  • Bluetooth spectral range can for instance mean a spectral range from 400 nm to 490 nm.
  • Green spectral range can for instance mean a spectral range from 490 nm to 575 nm.
  • Red spectral range can for instance mean a spectral range from 575 nm to 600 nm.
  • Range spectral range can for instance mean a spectral range from 600 nm to 650 nm.
  • Red spectral range can for instance mean a spectral range from 650 nm to 800 nm.
  • spectral gap does not necessarily mean that there is no intensity at all in the respective spectral range.
  • the intensity shall be at least reduced, it can for instance amount to not more than 30 %, 20 % or 10 % of a maximum intensity of the complementary color spectrum (comparing for instance the spectral irradiance). This can apply for an average intensity in the spectral range with the spectral gap and/or for a maximum intensity in the spectral range with the spectral gap. Nevertheless, it is also possible that there is no intensity at all in the spectral range with the gap.
  • the screening of bright areas can also allow for a tracking of a growth stage, for instance the flowering or ripening (e.g. change from green to red color).
  • a tracking of a growth stage for instance the flowering or ripening (e.g. change from green to red color).
  • 21 st aspect of "Discolored Spots” The method of the 18 th aspect of "Discolored Spots", wherein the first spectral gap of the first complementary color spectrum lies outside the green spectral range, the plant illuminated by the first complementary light being screened for dark areas.
  • the green leave itself will reflect the green light and appear bright, whereas a discolored area will appear dark when the spectral gap lies outside the spectral range reflected by the discoloration.
  • 22 nd aspect of "Discolored Spots” The method of the 21 st aspect of "Discolored Spots", wherein the first spectral gap of the first complementary color spectrum lies in the blue spectral range.
  • the first complementary color spectrum can have intensities in the green and/or yellow and/or orange and/or red spectral range.
  • 23 rd aspect of "Discolored Spots” The method of the 22 nd aspect of "Discolored Spots", wherein leaves of the plant illuminated by the first complementary light are screened for the dark areas for detecting a plant disease or inappropriate plant treatment causing yellow coloring of the leaves.
  • the first complementary color spectrum has a spectral intensity in the green spectral range only.
  • 25 th aspect of "Discolored Spots” The method of any of 18 th to 24 th aspects of "Discolored Spots", wherein a first image of the plant illuminated by the first complementary light is captured and screened for dark and/or bright areas by digital image evaluation.
  • 26 th aspect of "Discolored Spots” The method of the 25 th aspect of "Discolored Spots", wherein the number of dark areas in the first image is counted and/or the number of bright areas in the first image is counted.
  • the dark and/or bright area screening can for instance be used for evaluating the number of flowers or fruits (an
  • 27 th aspect of "Discolored Spots” The method of any of the 18 th to 26 th aspects of "Discolored Spots", wherein the plant is, subsequently to the illumination with the first complementary light, illuminated by a second complementary light with a second complementary color spectrum, which has a second spectral gap in comparison to a white light spectrum, the second spectral gap lying in another spectral region than the first spectral gap.
  • the complementary light source can be adjustable to emit the different complementary light spectra subsequently, or a plurality of complementary light sources can be provided.
  • 29 th aspect of "Discolored Spots” The method of the 27 th aspect of "Discolored Spots", wherein a first image of the plant illuminated by the first complementary light is captured and a second image of the plant illuminated by the second complementary light is captured, wherein an image comparison of the first and the second image is performed.
  • the actuator device can for instance perform a pest control or crop spraying, or a targeted fertilization. It can also unload the plant to a separate treatment location, see "Horticulture Processing Line" in detail.
  • the agricultural system can in particular be configured to illuminate the plant with the first complementary light.
  • the image of the plant is captured when the plant is illuminated by the first complementary light.
  • 31 st aspect of "Discolored Spots” The agricultural system of the 29 th aspect of "Discolored Spots", wherein the computing device is configured to screen the image captured by the image capture device for dark areas and/or for bright areas.
  • 32 nd aspect of "Discolored Spots” The agricultural system of the 29 th or 31 st aspect of "Discolored Spots", wherein the computing device is configured to access a database, which comprises data on plant diseases and disease-related discolorations, wherein the image processing comprises a matching with the data comprised in the database.
  • 33 rd aspect of "Discolored Spots” The agricultural system of any of the 29 th to 32 nd aspects of "Discolored Spots", wherein the complementary light source for emitting the first complementary light is comprised in the light fixture, the computing device being configured to switch between an investigation mode, in which the complementary light source emits the first complementary light, and an agricultural lighting mode, in which the complementary light source is switched off and another light source of the light fixture emits light to assist a growth of the plant.
  • the light emitted by the other light source can for instance be defined in a light recipe.
  • 34 rd aspect of "Discolored Spots” The agricultural system of any of the 29 th to 33 rd aspects of "Discolored Spots", comprising a light guide coupled to the complementary light source, wherein the light guide is provided for guiding the first complementary light to the plant.
  • the light guide can for instance be a fiber optic cable, see the element "Light Guides" in detail.
  • diseases and pests are identified based on collecting data about the plants and, optionally, about the ambient conditions in the target area as well. Then, a probability for the presence of a disease or the occurrence of pests is determined by comparing the collected data with reference data. Depending on the probability, and if need be, appropriate measures are proposed or introduced automatically.
  • the controlled agricultural system comprises a sensor device for acquiring data in a target area, a computing device connected to the sensor device, a data storage device connected to the computing device, wherein the computing device is configured to compare the data of the sensor device with the data stored in the data storage device and detect deviations between the two sets of data, a control unit connected to the computing device, wherein the computing device is configured to output control commands to the control unit depending on the detected deviations, a light fixture connected to the control unit, wherein the control unit is configured to convert the control commands of the computing device into control signals for the light fixture.
  • the controlled agricultural system is configured to allow the
  • the controlled agricultural system may comprise at least one light fixture (agricultural light fixture) with corresponding light sources.
  • the countermeasures may contain light recipes, which reduce the plant stress (biotic stress).
  • illumination parameters may be adapted, for example, the illumination duration and/or the illuminance may be changed, e.g. reduced, and/or the light spectrum of the illumination may be changed.
  • a controlled agricultural system comprising a sensor device for acquiring data in a target area, a computing device connected to the sensor device, a data storage device connected to the computing device, wherein the computing device is configured to compare the data of the sensor device with the data stored in the data storage device and detect deviations between the two sets of data, an actuator device connected to the control unit, wherein the control unit is configured to convert the control commands of the computing device into control signals for the actuator device.
  • measures may also comprise a change in the room temperature, the humidity, ventilation, the addition of nutrients, fertilizer, pesticides, pheromones, and the addition of medicine such as systemically acting pesticides to the nutrients.
  • Further measures may contain a geometric modification of the light fixture position, the light fixture configuration and the light fixture emission
  • the controlled agricultural system may comprise corresponding actuators, which carry out these measures.
  • a further measure in the case of an infestation by pests may include traps being illuminated in such a way that insects are attracted thereby, said insects leaving the plants and being locked in the traps or adhering thereto (sticky traps). This can be assisted by pheromones, etc. Likewise, it is possible to attract predators for the pests, such as mesostigmata, which attack spider mites. To this end, the plants can be illuminated with light in the UV range (250-380 nm) and/or in the blue- green range (500-550 nm).
  • the controlled agricultural system is equipped with sensors, for example optical sensors, which identify the growth of the plants, the reflectivity of the leaves or the stress of the plants, for example.
  • the growth of the plants may be detected by the density of the leaves, plant morphology, and leaf area index.
  • sensors may also identify the fluorescence radiation emitted by plants (after irradiation with excitation radiation). Chlorophyll fluorescence may be a particularly suitable option since the photosystem or the respiration changes in the case of disease or environmental conditions. Sensors may also measure a change in color of the plant (see e.g. the element ..Discolored Spots Detection"), in particular of the leaves. Sensors may also determine gases released by plants or determine the concentration of said gases in the ground.
  • Sensors may also identify the pests directly or typical damage to the plants, which indicates infestation by pests (stunted growth, deformations or other malformations).
  • optical sensors such as cameras with image recognition, LiDAR systems for acquiring the plant morphology (see e.g. the element "LiDAR Plant Surveillance"), spectroscopic measurement appliances, which analyze spectral properties of the irradiation light, reflected from the infested plants, but also acoustic sensors which register characteristic noises from the pests.
  • Deviations that indicate a disease or negative change in the plant health or an infestation by pests are determined by the comparison with a characteristic growth behavior or characteristic physical or chemical properties.
  • the type of disease or pest infestation can be determined by exact analysis of the leaf colors or plant forms, for example.
  • the comparison data are stored in a database and can be evaluated by an evaluation unit, in particular also in statistical fashion.
  • the comparison with an intended value can be implemented by direct parameter comparison; however, artificial intelligence methods (such as deep learning) can also be used for the comparison of statistical evaluation.
  • a parameter comparison can include an analysis and comparison of the plant morphology and a prediction, derived therefrom, for the further growth or the morphological embodiment of the plants. From the comparison of the measured data with the characteristic values (intended values), the system establishes a probability for the occurrence of the disease or the infestation by pests.
  • an evaluation unit can be provided to create predictions of a possible outbreak of disease and thereupon output a corresponding warning to the operator and/or customer of an agricultural system controlled in this fashion.
  • the system informs the operator or planter, for example, about the discovery of the possible disease or infestation by pests and proposes a further analysis in order to determine the disease or the infestation by pests more accurately.
  • the system can propose a specific countermeasure for the disease or the infestation by pests or (in a further embodiment) autonomously carry out the countermeasures described above in exemplary fashion.
  • the controlled agricultural system comprises a computing device.
  • the sensor data and the comparison data are supplied to the computing device.
  • the computing device via a control unit, actuates the light sources in the respective light fixture or, optionally, the actuators as well.
  • Part of the controlled agricultural system for example the computing device or the data storage device, may be local, but it may also be network-based or cloud-based.
  • the method for agricultural management comprises the steps of monitoring the plants in a target area by collecting data about the plants and/or the ambient conditions in the target area, comparing the collected data to corresponding intended data, determining whether deviations have occurred during the comparison, determining the probability for the occurrence of a disease or pest infestation on account of the determined deviations and if the probability lies under a first threshold then no further measures are introduced, if the probability lies between a first threshold and a second threshold then an information item that a disease or a pest infestation may be present is output and/or further analyses proposed, if the probability lies over a second threshold then countermeasures are propose or, alternatively, countermeasures are independently introduced.
  • the probability for the occurrence of a disease or pest infestation may be determined as follows. First, the difference values of the relevant parameters that deviate from the respective intended parameter values are calculated, i.e. in each case the absolute value of the measured value minus the intended value of a parameter. Relevant parameters in this context are parameters that indicate or influence the growth and/or health status of the plants, for example, the color of the plants, temperature and humidity of the environment, etc. Then each difference value is multiplied with a respective weighting factor, and the products are, finally, accumulated. The resulting sum is a measure of the probability. The respective weighting factors depend, amongst others, on the number of measured parameters.
  • the partial probabilities have to be normalized to result in a total sum of the value 1 if the plants are actually infected by a specific disease.
  • a weighting factor may be the smaller the less relevant the respective parameter is for determining a disease.
  • the weighting factors may be modified with the help of learning algorithm (Al, deep learning) based on empirical data to improve the reliability of the detection of a disease or infection with pest.
  • yield Prediction an automated yield forecast for flowering plants like tomatoes or strawberries is proposed.
  • a controlled agricultural system is configured to be able to predict the yield of flowering plants growing in a target area (cultivated area).
  • a controlled agricultural system comprising a sensor device, comprising sensors able to detect flowers and/or buds of plants, a data storage device, wherein conversion rates of flower to fruit of plants are stored, a computing device, configured to identify and count the flowers/buds from the data of the sensor device, and further configured to predict the yield based on the number of the flowers/buds and the respective conversion rate retrieved from the data storage device.
  • a controlled agricultural system comprises a sensor device, comprising sensors able to measure the biomass of plants, and further comprising sensors able to measure environmental parameters like light intensity, light spectrum, temperature, air movement, humidity, chemical composition of soil, air, fluids, a computing device, configured to predict the yield based on the biomass of the plants and current and/or future environmental data measured by means of the sensor device.
  • Biomass in this context refers to the mass of the plants, e.g. deduced from the number of plants and their size, i.e. the size of their stems and/or leaves.
  • 3 rd aspect of "Yield Prediction” The controlled agricultural system according to the 1 st or 2 nd aspect of "Yield Prediction", further comprising a user interface configured to deliver the result of the prediction.
  • 4 th aspect of "Yield Prediction” The controlled agricultural system according to any one of the 1 st to 3 rd aspect of "Yield Prediction", wherein the sensor device comprises one or more of the following sensors or a combination thereof: imaging system, e.g. still or video camera, in some embodiments/implementations TOF camera or stereo camera, LIDAR system, color sensor.
  • imaging system e.g. still or video camera, in some embodiments/implementations TOF camera or stereo camera, LIDAR system, color sensor.
  • the controlled agricultural system comprises at least one sensor, which is able to detect the flowers (or buds) at a plant or to measure the biomass of the plants in the target area.
  • the at least one sensor may comprise a camera and an image recognition system (object recognition and classification) to detect flowers (or buds) at a plant.
  • the controlled agricultural system comprises a computing device configured to identify the flowers (or buds) from the data measured by the at least one sensor.
  • the computing device may host the image recognition system. It may use machine learning / deep learning algorithms to detect flowers. Alternatively or in combination, it may also detect the flowers directly based on the color of the flower (e.g. yellow for tomatoes) and/or the typical size derived from the picture, either as an absolute value or relative to the size of other parts of the plant (e.g. leaves).
  • 6 th aspect of "Yield Prediction” The controlled agricultural system according to the 5 th aspect of "Yield Prediction", wherein typical time schedules of ripening of the fruits are stored in the data storage device, and wherein the computing device is configured to calculate a prediction for the harvesting time of the fruits based on the currently detected status of growth/ripening of the plants/fruits and the typical time left until ripeness of the fruits according to the time schedules.
  • sensors e.g. cameras
  • the sensors/cameras may be attached fixedly in the greenhouse or at posts in the field.
  • Some or all of the sensors (cameras) may be attached movably at a drone or robot and move through the greenhouse or the field, in some embodiments/implementations autonomously.
  • the number of flowers or buds per plants may be assessed individually for each plant in the cultivated area of the field or the greenhouse to obtain the overall number of flowers or buds in the cultivated area. If the number of plants is too large, a statistical approach can be chosen, i.e. limiting the measurements to a representative selection of plants (sub group). For instance, only every nth plant is measured, plants in a certain distance from the next plant are measured or plants in areas of the field that are known to be representative for the whole field (cultivated area) are measured. The number of flowers for the whole field (cultivated area) is then extrapolated from the measured number of flowers of this subgroup of plants.
  • a method for agricultural management comprises at least one controlled agricultural system and the steps of detecting the flowers or buds of the plants by means of the sensor device and the computing device assessing the number of flowers/buds by means of the computing device and based on the data measured by the sensor device predicting the yield by retrieving the respective conversion rate of the plant species from the data storage device and weighing the number of flowers assessed in the previous step with the conversion rate by means of the computing device.
  • a method for agricultural management comprises at least one controlled agricultural system and the steps of measuring the biomass of the plants by means of the sensor device and the computing device, measuring environmental parameters by means of the sensor device and the computing device, predicting the yield based on the biomass and the environmental data by means of the computing device.
  • the method for agricultural management uses a typical conversion rate from flowers to fruits available for each plant species in a database, which is stored in a data storage device of the computing device.
  • the conversion rate is the rate at which flowers result in fruits. For instance, a conversion rate of 0.5 means that only half of the flowers eventually result in fruits (e.g. 10 flowers would result in 5 fruits).
  • the typical conversion rate may be the average value of conversion rates observed in the past.
  • the conversion rate may depend on additional parameters like the temperature, humidity, etc., which may be measured as well, in order to improve the accuracy of the prediction.
  • the pollination performance of the bumble-bees is different, depending on the species, temperature, air movement, day length, humidity, etc. It also depends on the amount of bumble-bees the grower is using. All these parameters have to be considered to derive a correct yield prediction.
  • the calculated conversion rate can also take into consideration future changes in parameters like temperature, humidity or illumination. The parameters may be checked regularly. In case deviations are observed, the conversion rate and with it the predicted yield may be updated.
  • yield Prediction further comprising the step of delivering the result of the prediction to the user (e.g. farmer or customer) by means of the user interface.
  • the expected number of fruits is calculated by the computing device, taking into consideration that not all fruits will "survive” until harvesting, as they may drop, for example, due to low water, bad nutrition or something else.
  • the expected total yield can be calculated, e.g. in kilograms.
  • the harvesting time may be forecasted as well.
  • the system recognizes the state of the ripeness, e.g. the development of the flowers, the withering of the flowers, the creation of the fruits, and the different state of its ripeness. It can predict the expected harvesting time for each fruit based on average times stored in a database.
  • the prediction does not only include the final harvesting time but also the time when the next stage in the ripening process will be accomplished.
  • the predicted time is compared regularly with the actual time.
  • the forecast will be adjusted accordingly in case there should be a difference between actual and predicted time (e.g. a faster or slower ripening).
  • the average ripening time stored in the database will be updated.
  • the average ripening time for each stage is in some embodiments/implementations stored with corresponding environmental data like humidity, nutrition, illumination, temperature, and others.
  • the computing device may then present the calculated result
  • the result may comprise a set of data, including the forecasted yield and, optionally, harvesting time, images (shot by still or video camera) or other graphical representation such as virtual or augmented reality of the plants.
  • 1 1 th aspect of "Yield Prediction” A computer program product, comprising a plurality of program instructions, which when executed by a computer system of an Controlled Agricultural System according to any one of the 1 st to 6 th aspect of "Yield Prediction", cause the Controlled Agricultural System to execute the method for Agricultural Management according to any one of the 7 th to 10 th aspect of Yield Prediction".
  • a Controlled Agricultural System for growing plants comprising a lighting fixture for providing agricultural lighting, a fungi prevention light source for emitting light with a wavelength in a spectral range between 380 nm and 800 nm, wherein the Agricultural System is configured for applying the agricultural lighting to the plants during a day phase; and illuminating the plants with the fungi prevention light source at least temporarily during a night phase.
  • the agricultural system comprises a fungi prevention light source for emitting light with a wavelength in a spectral range between 380 nm and 800 nm.
  • the plants are illuminated at least temporarily with the fungi prevention light source.
  • the duration of the additional illumination may vary from 1 minute to several hours, for instance 8 hours.
  • sporulation By illuminating the plants during the night phase, the germination or sporulation of a fungi spore can be prevented or growth can be suppressed. Also sporulation can be slowed down, depending on temperature and light conditions. The fungi growth or germination/sporulation is affected by illumination, which means that a certain dark period duration is usually required to trigger the
  • the germination/sporulation of the spores In simple words, it germinates and sporulates generally during the night. With the fungi prevention light source, the night or night phase is interrupted, preventing the germination. In other words, day conditions are simulated for the fungus during the night phase.
  • the fungi prevention lighting has a reduced intensity and/or reduced spectral range.
  • the wavelength of the fungi prevention light is at least 400 nm, particularly preferred at least 600 nm.
  • An advantageous upper limit can for instance be 700 nm. Using red light is presumably advantageous in view the absorption behavior of the fungi.
  • red light can be a certain penetration through the leaves of the plants.
  • the fungi prevention lighting can be applied from any direction, from above, from the side, and/or from below.
  • the light can be brought to any location of the plants by using for instance a light guide or optical fiber (see also "Light Guides").
  • Treating fungus diseases with light furthermore allows to grow plants organically and reduces crop loss.
  • the concept of using a night interrupting light treatment can be used for any plants, in particular for fungi infecting herbs, medical plants or ornamental plants.
  • the plants are only illuminated temporarily during the night phase, namely during at least one interval. This can reduce a negative impact or influence on the plants themselves, which require the dormant period.
  • the additional illumination may be provided once during the night (e.g. in the middle of the night) or at regular intervals (e.g. every hour) or randomly during the night.
  • the fungi prevention illumination is applied during a plurality of intervals during the night phase. Therein, the duration of the intervals themselves and/or the Off-time between the intervals can be constant or can vary. Any variation can be distributed regularly or randomly. Constant intervals are possible as well, the fungi prevention illumination might for instance be applied every 2 hours for one hour during night time.
  • the fungi prevention light source is dimmable, for instance in the range of 3-100 mmol/(m 2 s).
  • the intensity of the fungi prevention illumination can be varied, so that a different intensity is applied in different intervals.
  • a different intensity can be applied for each interval, or the intensity can vary in groups.
  • the intensity can also vary within an interval. In particular, the intensity may be higher in the intervals at the beginning of the night phase than at the end of the night phase, or it may be higher at the end of the night phase than at the beginning of the night phase. The maximum intensity may also be reached in the middle of the night phase.
  • a dark period with no illumination is applied at the beginning of the night phase.
  • the duration of the first dark period is at least 1 hour and not more than 6 hours.
  • the duration of the night phase itself is at least 2 hours, further preferred at least 4 hours. Possible upper limits are 10 hours, in some embodiments/implementations 8 hours at maximum. Together, the day and the night phase add up to 24 hours.
  • a total fungi prevention illumination time amounts to not more than 2/3 of the duration of the night phase. Possible lower limits can for instance be at least 1/100, 1/50, or 1/10 of the duration of the night phase. In case of an illumination in intervals, the total illumination time is obtained by summing up the duration of the intervals.
  • 10 th aspect of "Fungi Growth Inhibition” The Controlled Agricultural System according to any one of the 1 st to the 9 th aspect of "Fungi Growth Inhibition", comprising a sensor device, the Controlled Agricultural System being configured for illuminating the plants with the fungi prevention light source depending on a measurement with the sensor device.
  • the fungi prevention illumination is applied based on a measurement performed with a sensor device.
  • Infested plants can for instance be detected using a sensor like a camera or the like, for instance in combination with a picture recognition.
  • Controlled Agricultural System according to any one of the 1 st to the 10 th aspect of "Fungi Growth Inhibition", comprising an additional fungi prevention UV light source for emitting UV light, wherein the Controlled Agricultural System is configured for illuminating the plants with the fungi prevention UV light source at least temporarily during the night phase.
  • an additional fungi prevention UV light source is provided.
  • the plants are illuminated with UV light at least temporarily during the night phase.
  • the UV light can for instance be UV-A light (380 - 315nm), UV-B light (280 - 315nm) and/or UV-C light (200 - 280nm).
  • the plants are not illuminated with the UV light source between two intervals, in which the fungi prevention illumination is applied (e.g. red light). Particularly preferred, no illumination at all is applied between the intervals of the fungi prevention / UV illumination.
  • the fungi prevention illumination e.g. red light
  • no illumination at all is applied between the intervals of the fungi prevention / UV illumination.
  • the fungi prevention illumination (in particular red light) during the night phase has the goal to disturb the growth cycle of the fungus, preventing it from growing and spreading.
  • the additional UV illumination may even destroy already existing fungi.
  • the fungi prevention time which uses UV-C-light may have a duration of 15 seconds to 1 minutes, whereas illumination with UV-A-light may last from 5 minutes to 5 hours to inactivate spores. UV-light could also be applied simultaneously with the red light. In some embodiments/implementations, the total duration of the fungi prevention illumination is longer than the duration of the UV illumination.
  • the fungi prevention light source and/or the additional UV light source are integrated into the lighting fixture for the agricultural lighting.
  • the light source(s) can be controlled by a control unit, which controls for instance the intensity, the illumination duration of the light, and the dark periods between the illuminations.
  • the intensities and durations can be controlled independently for the fungi prevention and the UV light source.
  • an additional environmental parameter is varied during the night phase, in some embodiments/implementations temperature and/or humidity.
  • the risk of infestation of the plants by fungi is usually reduced by reducing the population density, using a dry cultivation (i.e. irrigation from below and possibly in the morning or only a few larger water sprayings), introducing a time interval to a subsequent crop cycle, preventing dew formation in the greenhouse (emergency dry heating, use of fans), using hygienic measures, or supplying balanced nutrients (e.g. avoiding nitrogen stress). If the fungus has infested the plants, the infested plants are usually removed immediately or treated with fungicides (if available).
  • Agricultural System according to any one of the 1 st to 14 th aspect of "Fungi Growth Inhibition", comprising the steps of applying agricultural lighting to the plants during a day phase; and illuminating the plants with the fungi prevention light source (200) at least temporarily during a night phase.
  • 16 th aspect of "Fungi Growth Inhibition” Computer program product, comprising a plurality of program instructions, which when executed by a computing device of a Controlled Agricultural System according to any one of the 1 st to 14 th aspect of "Fungi Growth Inhibition", cause the Controlled Agricultural System to execute the Method for Controlling a Controlled Agricultural System according to the 15 th aspect of "Fungi Growth Inhibition".
  • the light treatment will be triggered by a control unit.
  • the command for the control unit can be given by a computing device which either receives the command from the grower, or which triggers the treatment automatically based on the detection of the fungi by a sensor device.
  • the treatment can also be initiated prophylactically to avoid the growth of fungi on the plants (e.g. every night, once a week or once a month).
  • the respective illumination durations, dark periods and illumination intensities are stored in a database connected to or integrated into the computing device.
  • the treatment method described here might be part of the overall control program of the light source.
  • a moveable irrigation device is equipped with a sensor device for measuring plant parameters, particularly parameters indicating health and growth stage of the plants.
  • the Agricultural System according to "Sensor Retrofit” comprises an irrigation device moveable with respect to a growth area (or cultivated area) and a sensor device mounted at the irrigation device.
  • the sensor device is moved along with the irrigation device. Therefore, plants grown at different locations or regions of the growth area can be measured with the same sensor device.
  • irrigation device may not only be used for watering purposes but also for a treatment with fertilizers/pesticides or the like.
  • the trolley usually includes a bar or rail or arrangement of levers, which contains nozzles for irrigation or spraying of fertilizers/pesticides.
  • the sensor device can for instance be a camera mounted to such a trolley, taking pictures of the plant surface while the trolley is turning back and forth over growth area, or other optical devices like a LiDAR (light detection and ranging) Time-of-Flight measuring and sensor device (see also "LiDAR Plant Surveillance", below). These pictures can help the grower to find regularities and irregularities in the plant population (growth, morphology, fruition, health condition).
  • the irrigation device can also be an irrigation robot, which can be used indoors or outside in open fields or vineyards, or a vehicle driving autonomously.
  • Plant growth can be influenced by several parameters like light intensity (photon flux), light spectrum, nutrients or temperature. Especially when photon flux), light spectrum, nutrients or temperature. Especially when photon flux, light spectrum, nutrients or temperature. Especially when photon flux, light spectrum, nutrients or temperature. Especially when photon flux, light spectrum, nutrients or temperature. Especially when photon flux, light spectrum, nutrients or temperature. Especially when photon flux, light spectrum, nutrients or temperature.
  • Plant growth can mean the height of the plant, the size and number and orientation of the leaves, the diameter of the plant, the plant morphology, etc.
  • an irrigation device or trolley is made of metal profiles, in particular aluminum profiles.
  • the irrigation device is mounted below a ceiling above the growth area. It can for instance hang on a rod from the roof top.
  • the sensor can be mounted to the irrigation device with clips or clamps (by a form-fit or with screws or the like).
  • the irrigation device comprises a horizontal rail with a plurality of nozzles provided along the rail.
  • the sensor device is an optical sensor, in particular a camera.
  • optical sensor in particular a camera.
  • other sensors are possible, for example temperature sensors (creating for instance a heat map) or ultrasound or LiDAR sensors to measure distances.
  • a plurality of sensor devices are mounted at the irrigation device.
  • a distance between neighboring sensor along the trolley bar devices can for instance be at least 0.1 m and in some embodiments / implementations not more than 1 m.
  • the spacing between the sensor devices or cameras will for instance depend on the mounting height and the angle of the lens of the camera.
  • the sensor devices differ in the parameter measured respectively, for instance cameras in their spectral sensitivity.
  • the cameras can be equipped with same or different lens systems in order to cover various field-of-view settings and therefore different sensed or surveilled plant areas.
  • the lens systems can be adjustable, in some embodiments / implementations by remote control.
  • the cameras can be equipped for daylight picture taking and/or for nighttime picture taking using infrared-sensitive sensors. They can also be equipped with an UV-protective cover that is transparent for visual and/or infrared radiation.
  • the cameras can also be equipped with cleaning devices or with removable optically transparent protective covers that can be cleaned, refurbished and so on.
  • RGB cameras can be provided to create a general overview of the entire plant canopy.
  • multispectral or hyperspectral cameras can be used to measure e.g. the chlorophyll fluorescence or the fertilization status.
  • pictures can be taken in different wavelength ranges, e.g. in the IR, visible range (whole spectrum or monochromatic) or UV.
  • neighboring cameras take pictures at different wavelengths during one passing, e.g. the 1 st, 4th, 7th,... camera takes pictures in the visible range, the 2nd, 5th, 8th,... camera takes pictures in the IR and the 3rd, 6th, 9th,... camera takes pictures at a certain wavelength (monochromatic).
  • Pictures can be taken at regular time intervals or as a function of trolley speed that can for example be in the range between 5 and 25 km/h.
  • a camera can also take videos for continuous surveillance and plant tracking.
  • the sensor devices are provided along a rail.
  • This inventive rail equipped with the sensor devices / cameras can be mounted to the existing structure (e.g. in parallel to the existing rail with nozzles) in different ways. For example, depending on the system, simple clips or clamps can be used. If necessary, a crossbar (or a steel rope) can be used to stabilize the structure.
  • 1 1 th aspect of "Sensor Retrofit” The Agricultural System according to any one of the 5 th to 10 th aspect of "Sensor Retrofit", comprising a computing device configured for collecting and merging the parameters measured by the sensor devices, a parameter map of the growth area being generated by merging the parameters.
  • a preferred Agricultural System comprises a computing device configured for collecting and in some embodiments/implementations merging the parameters measured by the sensor devices. Likewise, a parameter map of the growth area can be generated.
  • the position of the trolley will be measured to provide coordinates for the measured values, in particular for the pictures taken.
  • the calculation of the position can be done using an (indoor) positioning system e.g. based on Bluetooth beacons, or knowing a start-point of the trolley and calculating the actual position based on the speed of the trolley and the time passed and/or using marks (like an QR-code) along the rail for positional checking and information input.
  • the measured parameters in particular the picture data, will be sent to a general control unit.
  • the data transfer can be wire-based (LAN, 5-core cable, or other existing cables for data transfer) or wire-less.
  • the data can be transferred to the computing device via a control box or control unit.
  • the latter can be equipped with a Wi-Fi module, which transfers the data to the computing device.
  • a wire-based connection to the computing device is possible as well.
  • the computing device can be linked to or comprise a climate control computer.
  • the computing device can be local (edge computing) or in the cloud.
  • the data will be processed and alerts, growth status, etc. can be provided for instance on a dashboard to the grower. In case of deviation from the predicted or expected growth, an alert will be given.
  • the alert can contain the kind of abnormality and where the abnormality was detected, for example with positional coordinates.
  • Data distribution and analysis can be performed using Artificial Intelligence and Deep Learning methods.
  • Data distribution, analysis and data handling can use a blockchain technology in order to generate a tamper-proof distributed ledger-system. It is also possible to provide a low-resolution picture, with the software marking the area in a specific color where something is out of a normal behavioral condition or biological setting. Depending on the intelligence of the system, it can also give a recommendation to the user how to treat the plants.
  • 15 th aspect of "Sensor Retrofit” The Agricultural System according to the 14 th aspect of "Sensor Retrofit", wherein the irrigation device is moved forth in a first pass and back in a second pass, and wherein the sensor device is moved ahead the irrigation device during the first pass and the measurement is performed during the first pass.
  • the irrigation device is moved forth and back over the growth area. Therein, a measurement with the sensor device is only performed during one pass of the irrigation device, either during a first pass forth or during a second pass back. In some embodiments/implementations, the
  • the trolley is usually moving twice across the field (back and forth), i.e. the data can be collected either only during one pass of the trolley, or during both passages.
  • 16 th aspect of "Sensor Retrofit” The Agricultural System according to any one of the 1 st to 15 th aspect of "Sensor Retrofit", comprising a light source mounted at the irrigation device and being moveable with respect to the growth area, thus.
  • a light source is mounted at the irrigation device. This can for instance be a high-power UV LED for disinfection or a red / far- red source for night interruption.
  • the light source is mounted to the rail as well.
  • a plurality of sensor devices can be provided. For instance, each camera can be equipped with a respective light source.
  • the light sources can emit light at the wavelength measured by the respective camera to improve the brilliance. They can also emit light with a complementary color of an expected disease reflectance as described in
  • the light sources can also emit infrared light for nighttime inspection with infrared-sensitive cameras.
  • the irrigation device and the sensor device share a common power supply.
  • the power supply of the sensor device can be realized via a small solar panel with a battery on the top of the sleigh if no power supply is available.
  • the common power supply is implemented based on the existing infrastructure with an electric control box above each irrigation trolley. The power supply lines can go next to the irrigation pipes.
  • morphological or other parameters measured can be analyzed by the computing device.
  • the parameters and the result of the analysis can be provided to the farmer or a customer.
  • the system can automatically change a growth parameter (e.g. illumination or temperature) or it can inform the farmer or a customer (e.g. on a display about for instance the actual growth, simulated growth, growth prognosis, AR (augmented reality) or VR (virtual reality) representation, 2D and 3D plant configuration, and so on).
  • a growth parameter e.g. illumination or temperature
  • AR augmented reality
  • VR virtual reality representation
  • the growth of plants is monitored by measuring the deceasing distance between a distance-measuring device and the growing plants.
  • a controlled agricultural system comprises a growth area for growing plants and a distance measuring device for measuring a distance to an object in a detection field, the distance measuring device being arranged in a relative position to the growth area such that the detection field and the growth area have at least an overlap.
  • the distance measuring device is oriented towards the growth area of the agricultural system, for measuring the distance to the plants grown there. For instance, it can be arranged above the growth area, "looking" downward onto the latter. Then, the distance measured will decrease the larger the plants become.
  • the distance measurement can enable a profile measurement giving information on morphological parameters of the plants.
  • the distance measurement is a time-of-flight distance measurement.
  • an ultrasonic measurement is possible, even though a light-based measurement is preferred, in particular with a LiDAR system.
  • the term "light” is not restricted to the visible part of the electromagnetic spectrum, it also relates to UV and IR light, the latter can even be preferred.
  • 3 rd aspect of "LiDAR Plant Surveillance” The controlled agricultural system according to the 1 st or 2 nd aspect of "LiDAR Plant Surveillance", the distance measuring device comprising a light source for emitting light pulses, in some embodiments/implementations laser pulses, and a sensor device for detecting echo pulses returning from the detection field after a reflection at the object.
  • a light source is provided for emitting the light pulses, in particular a laser source, which in some embodiments/implementations comprise one or more laser diodes.
  • the light/laser pulses are emitted into the detection field and are reflected at the surface of the object(s) located there, for instance at the leaves in case of the plants.
  • the light source in particular a laser, emits short light pulses (typically with a pulse half-width between 0.1 ns and 100 ns, here preferred between 0.1 and 10 ns).
  • the sensor which can also be sensor array, can for instance be a Photo- Diode, an Avalanche Photo Diode (APD), a Single Photon Avalanche Diode (SPAD), a PIN-Diode, or a Photo-Multiplier, it detects the echo pulse.
  • APD Avalanche Photo Diode
  • SPAD Single Photon Avalanche Diode
  • PIN-Diode PIN-Diode
  • Photo-Multiplier it detects the echo pulse.
  • infrared light is used (wave length between 850 nm and 1600 nm, or larger), but visible or UV-light can be used as well.
  • the light source can emit the light pulses with repeat frequencies between 1 kHz and 1 MHz, in some embodiments/implementations between 1 kHz and 100 kHz (this gives a pulse enough time to return back to the sensor; 2 ps delay time corresponds to a distance of 300 m, 1 ps to 150 m, and 100 ns to 15 m).
  • the light can be pulsed stochastically to filter out the background illumination, which could be sunlight but also heat radiation. This will improve the signal-to-noise ratio of the signal.
  • the distance measuring device is adapted for a spatially resolved distance measurement (referred to as "enhanced” system below).
  • the detection field is segmented into a plurality of segments, for each segment a distance value is measured. This gives a distance image with a spatial resolution, namely a three-dimensional picture of the environment, in particular of the growth field.
  • the spatial resolution can be achieved with the sensor device.
  • the sensor assigns the echo pulses returning from the detection field to different solid angles.
  • the sensor or sensor array comprises several pixels, each detecting the reflected light in a certain solid angle. This can for instance be achieved with CCD or CMOS sensor combined with an optical system, for instance a lens.
  • the optical system guides the echo pulses from the different solid angles onto different pixels, for instance different areas of the CCD or CMOS array.
  • each pixel is linked to a respective solid angle, and the echo pulse can be assigned accordingly.
  • the light/laser source can for instance illuminate the whole detection field (area of interest) in a flash mode.
  • the spatial resolution can also be achieved by scanning the light/laser pulses across the detection field, for instance by moving mirrors like MEMS-mirrors. Accordingly, at a certain point in time, the light/laser pulse is emitted in a certain solid angle (depending on the current tilt of the mirror).
  • the sensor can even consist of just one sensor element with an optic which covers the whole detection field (the sensor has no spatial resolution).
  • the measuring device or control/computing device connected thereto) knows from which solid angle the detected echo pulse returned.
  • the distance measuring device is adapted for a spectrally resolved distance measurement, namely for emitting and detecting light pulses having a different wavelength (referred to as "enhanced” system below).
  • An enhanced system can employ various wavelengths at the same time, then a (segmented or multi-) sensor needs to have respective filters, or apply the filters sequentially. Each wavelength could also use another pulse-pattern, so that it can be differentiated from other wavelengths. Using different wavelengths can provide additional information, like leaf reflectivity, fluorescence radiation, e.g.
  • any subsequent pulse needs to wait until a typical fluorescence or phosphorescence decay time is over.
  • Using light pulses having different infrared wavelengths will help increase the Signal-to-Noise Ratio (SNR) or the measured Lidar Pulses, since more measurement data can be used for data measurement and processing, object recognition and classification.
  • SNR Signal-to-Noise Ratio
  • LIDAR includes laser radiation in the entire wavelength range from Ultraviolet to Infrared.
  • Using visible laser radiation in the visible wavelength range can be used to detect and measure not just plant morphology but also biological or chemical plant features and health conditions.
  • Causes of discolorations can for example be caused by lack of nutrients or lack of chemical elements like Nitrogen (N), Phosphor (P), Potassium (K), Sulfur (S), Manganese (Mn), over-supply of nutrients, too much light, too rapid temperature changes, lack of air circulation, too dry air, too much irrigation, bacterial and virus infestation causing for example bacterial blight and bacterial wilt, soil contamination, soil temperature and many others.
  • plant leaves can develop holes.
  • some color changes signal a next stage of ripening, e.g. the change of color in fruits. For instance, tomatoes discolor from green to red while ripening, eventually triggering harvesting.
  • a LIDAR scanning system (as described above) in a Controlled Agricultural System for plant breeding and cultivating, particularly for detection of plant diseases and various stages of ripening, comprising by using a data storage device comprising data, which are related to spectra of light, particularly of light with colors complementary to colors of parts of plants (Complementary Color Spectrum CCS), for example, complementary to discolored areas or parts of plants, an LIDAR illumination device able to emit light with a color spectrum according to the data stored in the data storage device and illuminate plants, a sensor device able to detect the light reflected by the illuminated plants, a computing device configured to control the illumination device based on the data of the database, and further configured to analyze the data from the sensor device and detect dark areas on the plants.
  • a data storage device comprising data, which are related to spectra of light, particularly of light with colors complementary to colors of parts of plants (Complementary Color Spectrum CCS), for example, complementary to discolored areas or parts of plants
  • an LIDAR illumination device able to emit
  • the distance measuring device is arranged in a distance of 30 m maximum from the growth area, in some embodiments / implementations 25 m, 20 m, 15 m or 10 m at maximum (possible lower limits are for instance at least 2 m, 4 m or 5 m).
  • a LiDAR system can reach a resolution of a few millimeters at a distance of about 10 meters. This resolution is sufficient to detect morphological parameters of plants like biomass, size, leave size, flowers (number and size), etc., future systems will even provide a better resolution.
  • 1 1 th aspect of "LiDAR Plant Surveillance” The controlled agricultural system according to any one of the 1 st to 10 th aspects of "LiDAR Plant Surveillance", comprising a light fixture for illuminating at least a part of the growth area, wherein the distance measuring device is a part of the light fixture.
  • the Controlled Agricultural System is an indoor farm, for instance a greenhouse or vertical farm.
  • the LiDAR-system or enhanced-LiDAR system with
  • spectral/spatial resolution can be attached at an elevated place in the greenhouse or vertical farm, it can be mounted below a ceiling above the growth area, either at the ceiling itself or at a scaffold.
  • the LiDAR-system can also be integrated into a light fixture which is provides artificial lighting to the growth area; the LiDAR-system can for instance be arranged in the housing of the light fixture.
  • the distance measuring device can be mounted movably for capturing the growth area from different sides. It can for instance move along a track in a vertical farm. In a vertical farm, the plants grow on shelfs in racks, the LiDAR-system could then move along the rack to measure the plants on each shelf.
  • the distance measuring device (LiDAR-system) is mounted immovably (in an immobile manner) with respect to the growth area. In comparison to the prior art mentioned above, this mounting is far less complex. With a LiDAR-system, the light can be flashed or scanned over the entire detection field / growth area, whereas in the prior art the whole sensor system has to be moved across the growth area. A LiDAR-system does not need a movable, mechanical support and can continuously measure a much wider area.
  • the Controlled Agricultural System comprises an additional distance measuring device for measuring a distance to an object in a detection field, in some embodiments/implementations an additional LiDAR-system.
  • the LiDAR-systems are arranged to capture different regions of the growth area and/or to capture the growth area from different points of view.
  • Using several LiDAR-systems in a vertical farm or greenhouse can enable the creation of a full view of the plants.
  • an (enhanced) LiDAR-system is attached close to one of the four corners of a greenhouse. From this perspective, the whole area of interest can be covered.
  • an (enhanced) LiDAR from another angle can cover this area.
  • the plants can be measured from all angles, creating a 360°-view of the plant morphology.
  • each can scan the whole growth area (e.g. the full view the system is able to scan).
  • the detection field can even be larger than the growth area.
  • a computing device either a local or a central device, can distinguish the growth area from other parts of the
  • the computing device can then reduce or adapt the scanned area for each LiDAR-system so that it only covers the area of interest ("commissioning").
  • the Controlled Agricultural System is configured for a time-synchronized measurement with the different distance measuring devices / LiDAR-systems.
  • the control unit or computing device of the Agricultural system activates the LiDAR-systems at a specific point in time or at specific points in time - for example during illumination with light emitted by the regular horticulture lighting fixtures with a specific color, or with a specific spectral intensity or other photometric values, like photosynthetically active radiation (PAR) or Photon Flux, or only during a dark time period (no lighting), or after the plants have been treated with UV-radiation - in the greenhouse or horticultural indoor farm in an interleaved mode (i.e. one after the other) to avoid that one LiDAR-system interferes with a second LiDAR-system, leading to a "false" signal.
  • PAR photosynthetically active radiation
  • Photon Flux or only during a dark time period (no lighting)
  • the distance measuring devices / LiDAR- systems are equipped for operating in different spectral regions.
  • Each sensor IR
  • UV, visible is connected to a computing device (via its control unit). Since different wavelengths are used, the signals from another (wrong) LiDAR light source can be ignored by the sensor (e.g. by using a wavelength-filter).
  • the Controlled Agricultural System is configured for a clocked measurement with the different distance measuring devices / LiDAR-systems.
  • the control unit or computing device of the Agricultural system activates the LiDAR-systems in the greenhouse in an interleaved mode (i.e. one after the other) to avoid that one LiDAR-system interferes with a second LiDAR- system, leading to a "false" signal.
  • 16 th aspect of "LiDAR Plant Surveillance” The controlled agricultural system according to the 13 th to 15 th aspect of “LiDAR Plant Surveillance”, comprising a computing device configured for merging distance images taken by the distance measuring devices.
  • 17 th aspect of "LiDAR Plant Surveillance” The controlled agricultural system according to the 16 th aspect of “LiDAR Plant Surveillance”, comprising a reference point in a defined relative position with respect to the growth area, the computing device being configured for merging the distance images by means of the reference point.
  • Each LiDAR-system will provide a three-dimensional set of (time- sequential) pictures, from its point of view. Different wavelength ranges are possible, but not mandatory.
  • the pictures of the LiDAR-systems need to be mapped over each other.
  • the system in some embodiments/implementations uses a reference point.
  • This reference point can be an object in the greenhouse and the respective distances are calculated from this object.
  • the reference point can be the walls of the greenhouse.
  • the computing device receives the information from the LiDAR-system how far a plant is away from the opposite wall, and it knows the distance between the walls of the greenhouse.
  • the height of a plant is measured with respect to the upper surface of the soil. Usually, the plants do not cover the whole soil, so that this information should be available any time. However, the system can make a reference
  • the detected morphological parameters will be analyzed by the computing device.
  • the parameters and the result of the analysis can be provided to the farmer or a customer.
  • the system can automatically change a growth parameter (e.g. illumination or temperature) or it can inform the farmer or a customer (e.g. on a display about for instance about the actual growth, simulated growth, growth prognosis, yield forecast, AR or AV representation, and so on).
  • a growth parameter e.g. illumination or temperature
  • Such a system is able to measure even at night as to detect night behavior (e.g. also after a nightly UV-exposure).
  • a LiDAR system It is also possible for a LiDAR system to recognize other objects inside a greenhouse or vertical farm, like humans, agribots, animals, and so on and provide a movement pattern of the other object. It is also possible for a LiDAR system to measure the movement and/or location of a plant or product along a moving belt or tray, allowing correct identification of the product.
  • the method for agricultural management comprises at least one controlled agricultural system, wherein plants are grown at the growth area, and wherein the plants are captured by a distance measurement performed with the distance measuring device.
  • 19 th aspect of "LiDAR Plant Surveillance” The method according to the 18 th aspect of “LiDAR Plant Surveillance", wherein a reference measurement of the growth area (203) is performed before the plants are grown at the growth area.
  • the growth and health of plants can be monitored according to any of the aspects of the disclosure described above or a combination of various aspects.
  • growth and health of plants may be monitored by combining any or all of the aspects of "Stress Detection", ..Discolored Spots Detection", “Sensor Retrofit” and practiceLiDAR Plant Surveillance”.
  • potentially critical situations may be detected according to the aspects of "Prophylaxis”.
  • the results of the monitoring i.e. the probability that something detrimental may have happened to the plants, based on the aforementioned aspects may be further analyzed according to the aspects of "Disease & Pest Control”.
  • countermeasures may be taken according to the aspects of "Fungi Growth Inhibition”.
  • an Agricultural System comprising at least two light fixtures at different locations and being configured for applying a different illumination with these light fixtures at the different locations based on a temperature value measured.
  • a Controlled Agricultural System comprising:
  • the Agricultural System is configured for applying a different illumination with the light fixtures at the different locations based on a temperature value measured.
  • the "temperature value” can be the actual temperature, measured for instance in K, °C or °F.
  • the temperature can be measured by any kind of a thermal sensor (Electric, Resistance, Pyrometer, Piezo, etc.). In this respect, the
  • temperature value can be any type of output signal of a thermal sensor, which relates to the temperature, for instance an electrical current or voltage.
  • different locations for instance trays or bowls can be provided, in which the plants are grown.
  • the different locations can in some embodiments/implementations lie on a different height respectively, for instance on different shelfs.
  • the different locations are arranged in the same building, particularly preferred in the same room.
  • the light fixtures and locations can be spaced vertically, for instance in a vertical farm.
  • Vertical farm buildings can have heights of 10, 20, 30 or more meters and contain dozens of shelfs from the ground to the top in which the plants (includes Food Crops, Floriculture, Cannabis) are growing.
  • Each shelf usually contains its own lighting fixture.
  • Efforts to construct especially designed vertical farms are sometimes summarized under the term Agritecture.
  • An approach here is to influence the growth by applying a different illumination depending on the temperature.
  • the growth at different locations can be synchronized by applying for instance a higher DLI value where the temperature is lower, and vice versa.
  • the methodology also allows application of certain lighting conditions at given local temperatures in order to reach other growth targets, like super-fast growth, or super-slow growth for instance (see also "Customer Request").
  • photosynthetically active photons (individual particles of light in the 400-700 nm range) that are delivered to a specific area over a 24-hour period.
  • red light can be of interest at a specific temperature (e. g. 12-19 °C) and blue light at another specific temperature (e. g. 20-25 °C).
  • the red light can affect the activation of phytochrome
  • the blue light can affect the activation of phototropin and cryptochrome.
  • Additional effects may be generated when applying red or blue light at different humidity levels, e.g. red light irradiation at a cultivation atmosphere humidity level of 40 % to 90 % and blue light irradiation at a humidity level of 40 % to 90 %.
  • Plant growth during various growth cycles can be strongly influenced by the applied light spectra, for example, cucumber and lettuce plants reach greater length and/or mass when illuminated with the inventive horticultural light that includes far red light (700-800 nm).
  • one solution is to adjust the spectral ratios of a horticulture lamp (LED) as a function of ambient temperature.
  • the Far-Red radiation content better: the Far-Red related Photosynthetically Active Photon Flux Density, PPFD or PFD measured in mmol/(m 2 s), or the Far-Red related Applied Daily Light Integral (DLI) or the Far-Red related Daily Light Applied Spectrum Integral (DLASI)
  • DLI Far-Red related Applied Daily Light Integral
  • DLASI Far-Red related Daily Light Applied Spectrum Integral
  • the DLI values can be additionally adjusted.
  • Temperature distribution is of course also influenced by the applied cooling conditions and the day/night illumination cycles (ON/OFF).
  • a sensor device is provided for measuring the (local) temperature settings, in particular inside a vertical farm building or the like, in order to apply favorable growth influencing conditions (as explained above). Basically, even a single sensor device could be sufficient, for instance an infrared camera allowing a temperature measurement at different locations at the same time. In some
  • a plurality of local sensor devices are provided.
  • all or at least some of the locations can be equipped with a respective sensor device.
  • the sensor device can be integrated into the lighting fixture.
  • Lighting controllers can be placed locally at the lighting fixture or remotely.
  • a plurality of light fixtures are provided on a different height respectively, for instance at least 3, 5, 8, 10, 12,
  • each shelf comprising a plurality of horizontal locations on the same height level, equipped with an own lighting fixture respectively.
  • Such a desired temperature profile may be dependent on external weather conditions (adjustment time), but this is then already reflected in the actual local temperature measurement. Of course, it is necessary to apply a certain
  • the temperature profile may only be measured after quite long time intervals like hours, or only once per day.
  • thermo sensors Electronic Resistance, Pyrometer, Piezo, etc.
  • TPik vertical temperature profile
  • measured local data can be stored into a Data Bank (DB).
  • CLik actually applied lighting conditions
  • the measurement of the applied lighting conditions can contain a variety of parameters (DLI, spectral ratios, and so on, as explained above). Measurements can be done e.g. at preselected time intervals (like seconds, minutes, hours), or irregularly. Once measured, such data can be fed into a computer system and proper lighting conditions can be calculated and applied (intermediate approach).
  • the Growth Parameters are compared to target growth values and the proper lighting conditions are chosen to reach the harvesting goal with respect to the actual growth parameters and the actual temperature profile (high- level approach).
  • Agricultural System comprising a computing device, configured to compare the data measured by the sensor device with a reference data set and to apply an illumination based on the result of the comparison.
  • Temporal dependent illumination also relates to a method for generating a data set for controlling an Agricultural System.
  • a plurality of plants are grown, wherein a defined temperature and illumination is applied.
  • the temperature and illumination differs in groups.
  • several different temperatures are defined and several different illumination setups are defined.
  • conspecific plants are grown and divided into groups with different temperature/illumination.
  • Such an evaluation can measure and assess for instance the necessary DLI levels (illumination setup) in order to reach the same Time to Flower Rate (growth parameter) at various temperature settings within a given (defined) temperature range, see Table 1 for illustration (for Petunia Coral Pink).
  • DLI levels increase, keep, decrease
  • the "plant growth” measured can for instance also be the plant height, plant morphology, plant chemistry, plant leaf density index, plant color and other growth and ripening indicative parameters with various measurement techniques (destructive and non-destructive). These data will then be stored as well into a data bank (Growth Parameters GP).
  • DLASI Daily Light Integral
  • DLASI Daily Light Applied Spectrum Integral
  • the change of light spectra vi. the ratios of spectra e.g. the ratio of blue to Far-Red; or the ratio between UV-A and Far-Red
  • a Data Bank (DB) can for instance hold:
  • the compute unit can also calculate (or extrapolate) time to harvest based on any applied temperature, e.g. a temperature on the ground level of a vertical farm, or on the top level.
  • the compute unit can also communicate this information to third parties: user, provider, etc. ‘ADAPTIVE SPECTRUM’
  • a sensor device for a measurement of an ambient spectrum of an ambient light (second light) incident on the growth area
  • a superposition (superimposed light) of the first light and the second light is spectrally closer to the target spectrum (third light) than the ambient spectrum.
  • a respective “spectrum” can for instance cover the entire spectrum or only a spectral range of the respective light.
  • a spectral range can for instance extend over at least 20 nm, 50 nm, or 80 nm (possible upper limits being for instance 1000 nm, 800 nm, 600 nm, 400 nm, or 200 nm).
  • a “spectrum” can be continuous or quasi-continuous, or it can consist of discrete values at discrete wavelengths (e.g. at least one value per nm). For comparing spectra, for instance a radiant flux related value, e.g.
  • the radiant flux itself in Watt
  • the spectrum resulting from the superposition of the additional and the ambient spectrum namely the superimposed spectrum, shall be closer to the target spectrum (third light) than the ambient spectrum.
  • a difference between the target spectrum and the superimposed spectrum shall be smaller than a difference between the target spectrum and the ambient light.
  • the absolute value (modulus) of the respective difference value is taken.
  • the ambient light is natural light, in particular sunlight.
  • the natural light can be the light available at day or also during the night.
  • sunlight is available, e.g. in terms of daytime and weather, it is incident on the growth area.
  • the growth area of the agricultural system or facility can for instance be a arranged in a glasshouse.
  • the sunlight could be also guided to the plants via light tubes or the like.
  • the sensor device allows for a spectral measurement, namely for measuring a radiant flux related value at different wavelengths.
  • a spectral measurement is important, because the growth or thrive of the plants can depend on the flux or intensity in specific spectral ranges, see the "Examples of Light Recipes" below. Measuring for instance only a color of the ambient light would not be sufficient, because different spectral compositions can result in the same color.
  • the resulting light (ambient + first light) has the same spectral composition as the target light.
  • the first spectrum of the first light is basically identical to the difference spectrum.
  • 3 rd aspect of "Adaptive Spectrum” The controlled agricultural system of the 1 st or 2 nd aspect of "Adaptive Spectrum", configured for an operation in which the first light has, at least temporarily, a share of at least 10 % at the superimposed light.
  • Further lower limits can for instance be at least 20 %, 30 %, or 40 %. Therein, for instance the irradiance of the first light and the superimposed light are compared. Even though a complete substitution (100 %) is possible, preferred upper limits can for instance be 90 % or 80 % at maximum (at least temporarily, in the supplementation mode).
  • the ambient light (second light) having a certain share at the superimposed light can be advantageous in terms of the energy consumption.
  • Adaptive Spectrum is to obtain the spectrum of the target light, at least approximately and in some embodiments/implementations to the best possible extent. Only setting the correctly perceived color of the light (given by the color coordinate in a CIE diagram, for example) with the aid of the additional light is insufficient in the agricultural sector; this is because a color can be represented in different ways, i.e. , by different spectra (for example, yellow light can be represented by a spectrum of yellow light or by a spectrum containing red and blue components). However, the accurate spectrum is important for the growth of the plants in the agricultural sector.
  • Adaptive Spectrum The controlled agricultural system of any of the 1 st to 3 rd aspect of "Adaptive Spectrum", wherein the light fixture comprises at least two different light sources adapted for emitting light with different spectral properties.
  • These light sources differ in their spectral properties. Their peak intensities can for instance lie at different wavelengths and/or the spectral distribution can differ (narrowband or broadband). In some embodiments/implementations, the different light sources can be light-emitting diodes, see in detail below.
  • the target spectrum can be reached under various ambient light conditions.
  • the ambient light can for instance be sunlight. However, it can also be artificial light or a superposition of sunlight an artificial light.
  • Possible fields of application of "Adaptive Spectrum” may for instance be: greenhouses (in particular glasshouses), indoor farming or portable growing units, in which the plants (agricultural plants) are irradiated by a second light which, for example, may be the sunlight and/or artificial illumination (e.g., from adjacent or the surrounding regions, too).
  • the second light is not constant.
  • the sunlight has a certain daily cycle and a yearly cycle, depending on geographic position.
  • further influencing variables can influence or change the characteristics of the available sunlight, such as, e.g., the formation of clouds, fine dust, rain, snow, etc.
  • the sunlight has a daily color temperature response. In the morning and in the evening, it has a color temperature of
  • the spectral range from 400 to 800 nm is most important for the growth of plants. Said range comprises blue (b) radiation (400-500 nm), green (g) radiation (500-600 nm), red (r) radiation (600-700 nm) and dark red (dr) radiation (700- 800 nm).
  • LEDs light-emitting diodes
  • phosphor wavelength conversion element
  • LED light sources can emit in the ultraviolet, visible or infrared spectrum.
  • the wavelengths of the emission radiation can be accurately set by means of quantum dot LEDs.
  • Organic LEDs (OLEDs), electroluminescence light sources, electrodeless induction lamps and mercury-free dielectric barrier discharge lamps can also be used as a light module.
  • the light sources can have a compact or areal embodiment and can be equipped with primary and secondary optics, such as lenses, light guides, stationary and movable reflectors or radiation-reflective optical devices, holographic elements, partly transparent or completely light-opaque films, heat-reflecting films, luminescent films.
  • primary and secondary optics such as lenses, light guides, stationary and movable reflectors or radiation-reflective optical devices, holographic elements, partly transparent or completely light-opaque films, heat-reflecting films, luminescent films.
  • LARP laser-activated remote phosphor
  • the agricultural system may comprise a computing device connected to the sensor device.
  • the computing device may be configured to establish the difference spectrum between the spectrum of the ambient light and the target spectrum on the basis of the measurement values of the sensor device.
  • the agricultural system may comprise a control unit, the light fixture being connected to the control unit and the control unit being connected to the computing device.
  • the control unit may be configured to convert the previously established difference spectrum into control signals for the light fixture.
  • the light fixture can be triggered to emit the additional light (first light) to supplement the ambient light.
  • Adaptive Spectrum The controlled agricultural system of any of the 1 st to 5 th aspect of "Adaptive Spectrum”, configured to restrict an evaluation of the ambient light to wavelengths at which an intensity is designated in the target spectrum.
  • Adaptive Spectrum The controlled agricultural system of any of the 1 st to 6 th aspect of "Adaptive Spectrum", wherein the light fixture comprises LEDs and the agricultural system is configured to restrict an evaluation of the spectrum of the ambient light to intensity maxima of the LEDs of the light fixture.
  • the sensor device is configured to restrict the measurement of the spectrum of the ambient light to these intensity maxima.
  • the measurement is restricted to the intensity maximums of the LEDs installed in the light fixture.
  • the width of the wavelength range in the measurement may be fixed around the maximum in this case (e.g., +/-25 nm); however, it may also be determined by the curve of the peak, and so the boundaries lie where the intensity has fallen to a certain value (1/10 or 1/e) of the maximum.
  • the actual intensity of the second light is measured in these regions, said intensity is compared to the desired intensity and the intensity of the LEDs can be determined by simply forming the difference.
  • the "ambient light” can be natural light (direct or indirect sunlight) but also artificial light or a mixture of artificial and natural light.
  • missing/supplemental parts of the spectrum in a targeted and energy saving manner, said missing/supplemental parts of the spectrum filling the second light spectrally with the desired intensity or further characteristics in order to obtain the target spectrum.
  • Adaptive Spectrum The controlled agricultural system as described in any of the preceding aspects of "Adaptive Spectrum", wherein the sensor device comprises one sensor or a plurality of sensors.
  • the controlled agricultural system therefore comprises at least one light fixture (agricultural light fixture) with at least one light source and a sensor or an arrangement of sensors (sensor device), by means of which the locally available second light spectrum (in the target area) can be analyzed in respect of composition and intensity, etc.
  • the spectrum means a region from UV to infrared or far infrared, i.e. , approximately 100 nm to 100 000 nm (i.e. , also including thermal radiation).
  • the spectrum of the available illumination can be analyzed, for example in region increments of 1 nm, of 10 nm or of 50 nm (i.e., it is not the continuous intensity that is recorded; instead, the intensity of the spectrum is digitized in certain ranges).
  • Adaptive Spectrum The controlled agricultural system as described in any one of the preceding aspects of "Adaptive Spectrum", wherein the target spectrum corresponds to a light recipe for irradiating produce, in particular a plant.
  • the measurement data are compared to the stored reference variables and supplied to a program.
  • the program runs on a computing device, which may be part of the controlled agricultural system or which may else be cloud- based.
  • the controlled agricultural system comprises a control unit (light control unit), which actuates the light sources of the at least one light fixture on the basis of the data of the computing device and optionally modifies these appropriately.
  • different light fixtures may also receive different actuation data.
  • Adaptive Spectrum The controlled agricultural system as described in any one of the preceding aspects of "Adaptive Spectrum", comprising an interface for weather forecast data for a predictive adaptation of the additional light to the weather-dependent change in the sunlight (ambient light).
  • a "prediction” or “predictive adaption” can for instance be based on or implemented by Artificial Intelligence.
  • a superposition (superimposed light) of the first light and the second light is spectrally closer to the target spectrum (third light) than the ambient spectrum.
  • the spectrum of the ambient light is compared with the target spectrum (spectrum of the third light).
  • the first light emitted then has, at least approximately, the spectral composition of the difference spectrum to fill this gap. In particular, this can be achieved by way of a suitable actuation of a light fixture.
  • the growth area (target area) is irradiated with the produced additional light, in addition to the ambient light.
  • the spectrum of the second light is compared to the spectrum of the target light that should be used to illuminate the plants.
  • the spectrum is available using the same type of description as the measured spectrum, in this example as intensities in a wavelength range (if the spectrum is available as a continuous spectrum, the corresponding value can easily be calculated by way of the area of the intensity present in this wavelength range).
  • the differences in the intensity can be determined for the individual ranges (e.g., using the method of least squares) and the control unit can actuate the light fixture accordingly so that the plants are irradiated by the required intensity in the determined wavelength ranges.

Abstract

La présente invention concerne différentes techniques de commande d'un système agricole, comme par exemple un système agricole commandé, un appareil d'éclairage agricole et un procédé de gestion agricole. En outre, l'invention concerne un système agricole, qui comprend une pluralité de lignes de traitement de croissance de plantes d'un type de plante donné, une première ligne de traitement de la pluralité de lignes de traitement étant conçue pour déplacer une première pluralité de plantes à travers le système agricole le long d'un itinéraire ; et pour appliquer une première condition de croissance à la première pluralité de plantes de façon à satisfaire un premier paramètre d'agent actif pour la première pluralité de plantes.
PCT/US2020/017910 2019-02-14 2020-02-12 Systèmes agricoles commandés et procédés de gestion de systèmes agricoles WO2020167934A1 (fr)

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CA3130218A CA3130218A1 (fr) 2019-02-14 2020-02-12 Systemes agricoles commandes et procedes de gestion de systemes agricoles
EP20755186.2A EP3924808A4 (fr) 2019-02-14 2020-02-12 Systèmes agricoles commandés et procédés de gestion de systèmes agricoles
CN202080028508.6A CN113966518B (zh) 2019-02-14 2020-02-12 受控农业系统和管理农业系统的方法

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US16/275,476 US20190259108A1 (en) 2018-02-20 2019-02-14 Controlled Agricultural Systems and Methods of Managing Agricultural Systems
US16/275,476 2019-02-14
US16/786,001 US11663414B2 (en) 2018-02-20 2020-02-10 Controlled agricultural systems and methods of managing agricultural systems
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EP3924808A1 (fr) 2021-12-22

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