WO2024017731A1

WO2024017731A1 - Computer-implemented method for providing combined application data

Info

Publication number: WO2024017731A1
Application number: PCT/EP2023/069300
Authority: WO
Inventors: Hubert Schmeer; Nicolas WERNER; Christian COMBERG; Mauricio Lopes Agnese; Clemens Christian DELATREE; Carvin Guenther SCHEEL; Dominic Sturm; Erik Hass; Steffen TELGMANN; Marcel Enzo GAUER
Original assignee: Basf Agro Trademarks Gmbh
Priority date: 2022-07-22
Filing date: 2023-07-12
Publication date: 2024-01-25

Abstract

Computer-implemented method for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the method comprising: - providing field parameter data comprising spatial distribution data of at least one field parameter in the agricultural field; - providing variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; - providing sensor data with respect to the agricultural field; - providing spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data; - providing combined application data comprising the variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.

Description

COMPUTER-IMPLEMENTED METHOD FOR PROVIDING COMBINED APPLICATION DATA

TECHNICAL FIELD

The present disclosure relates to a computer-implemented method for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, an application device for applying the first and second agricultural product according to the provided combined application data, a system for providing such combined application data, an apparatus for providing such combined application data, use of variable application data and/or spot application data for providing combined application data in such a method or such systems and a respective computer program element.

TECHNICAL BACKGROUND

The general background of this disclosure is the treatment of an agricultural field. The treatment of an agricultural field comprises the treatment of the soil and plants of an agricultural field, a greenhouse, or the like, by agricultural products, e.g. herbicide products, fungicide products, fertilizer products, etc.

In common agricultural practice, agricultural products are often applied on agricultural fields on basis of experience, expertise, and knowledge of the farmers, in particular by interpreting single soil, plant and/or weather parameters in order to make a decision for or against the application of a respective agricultural product on a field level.

It has been found that a need exists to provide a method for providing robust, spatially adapted, and precise information about the application of agricultural products onto an agricultural field.

SUMMARY OF THE INVENTION

In one aspect of the present disclosure, a computer-implemented method for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field is disclosed, the method comprising: providing field parameter data comprising spatial distribution data of at least one field parameter in the agricultural field; providing variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; providing sensor data with respect to the agricultural field; providing spot application data for applying the second agricultural product on the agricultural field at least based on sensor data; providing combined application data comprising the variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.

A further aspect of the present disclosure relates to an application device for applying the first and second agricultural products on an agricultural field, wherein the application device is controlled at least based on combined application data provided by a method as disclosed herein, wherein the application device preferably comprises at least two product tanks.

A further aspect of the present disclosure relates to a system for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the system comprising: a first providing unit configured to provide field parameter data comprising data of the spatial distribution of at least one field parameter in the agricultural field; a second providing unit configured to provide variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; a third providing unit configured to provide sensor data with respect to the agricultural field; a fourth providing unit configured to provide spot application data for applying the second agricultural product on the agricultural field at least based on sensor data; a fifth providing unit configured to provide combined application data comprising variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.

A further aspect of the present disclosure relates to an apparatus for providing for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: providing field parameter data comprising data of the spatial distribution of at least one field parameter in the agricultural field; providing variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; providing sensor data with respect to the agricultural field; providing spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data; providing combined application data comprising variable application data for applying the first agricultural product on the agricultural field and spot application data for applying the second agricultural product on the agricultural field.

A further aspect of the present disclosure relates to a use of variable application data and/or spot application data for providing combined application data in such a method and/or such systems.

A further aspect of the present disclosure relates to a computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the method disclosed herein in a system disclosed herein.

This and embodiments described herein relate to the method, the system, the treatment device, the computer program element lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa.

As used herein ..determining" also includes ..estimating, calculating, initiating or causing to determine", “generating" also includes ..initiating or causing to generate", and “providing” also includes “initiating or causing to determine, generate, select, send, query or receive”.

The method, device, systems, apparatus and computer program element, disclosed herein provide an efficient, sustainable and robust way for providing robust, spatially adapted, and precise information about the application of agricultural products onto an agricultural field. In particular, the beneficial effect is to consider not only the effect of an application of one agricultural product, but to consider the effects of the application of two agricultural products as a whole. For example, different decisions, such as different application quantities, can be made for applying a first agricultural product, when the application of a second product is already planned when the first product is applied. This makes it possible, for example, to benefit from amplification effects and/or complementary effects of the two agricultural products. In an example of complementary effects, it was found that “riskier” decisions can be made in the application of the first agricultural product if the second agricultural product can, if necessary, compensate for a wrong decision with the first agricultural product. Risk in this context can mean, for example, that a too low application rate is selected for the first agricultural product, for example a soil herbicide. However, a too low application rate of the first agricultural product may be compensated by a later application of the second agricultural product if necessary. A particular example here is when the first product is a soil herbicide and the second product is a foliar herbicide. If, for example, it turns out that the soil herbicide has been applied with an insufficient application rate, a certain compensation can be provided, for example, by a later application of a foliar herbicide. For example, if the soil herbicide was applied at a time that turns out to be less than optimal, for example, if there was too little soil moisture after the soil herbicide was applied to "activate" the soil herbicide, compensation can be provided by the second product. In particular, it is an object of the present disclosure to provide the agronomist with a means of making somewhat riskier decisions while still having the security of being able to compensate at a later time for a wrong decision and/or a misconception with the second product. Alternatively, it is an object of the present disclosure to provide the agronomist with application data for two products that optimally complement or optimally reinforce each other so that the total amount of necessary products can be reduced.

It is an object of the present disclosure to provide an efficient and sustainable way for providing combined application data comprising both variable application data and spot application data. In a preferred embodiment this allows the use of optimum doses by considering the effects of both applications together avoiding unnecessary treating and/or over treatment of the agricultural field, and saving money and amounts of treating products and having less environmental impact.

These and other objects, which become apparent upon reading the following description, are solved by the subject matters of the independent claims. The dependent claims refer to preferred embodiments of the invention.

The term agricultural field as used herein is to be understood broadly in the present case and presents any area, i.e. surface and subsurface, of a soil to be treated. The agricultural field may be any plant or crop cultivation area, such as a farming field, a greenhouse, or the like. A plant may be a crop, a weed, a volunteer plant, a crop from a previous growing season, a beneficial plant or any other plant present on the agricultural field. The agricultural field may be identified through its geographical location or geo-referenced location data. A reference coordinate, a size and/or a shape may be used to further specify the agricultural field.

The term variable application data as used herein is to be understood broadly in the present case and presents any data providing information about a spatially resolved application of the first agricultural product on the agricultural field. Furthermore, the variable application data may include dose rate data. The dose rate for applying the first agricultural product, e.g. a soil herbicide product, on the agricultural field may be provided for the surface or sub-areas of the agricultural field. Furthermore, the variable application data may be provided by an application map. The application map may be a 2-dimensional application map. The variable application data may comprise instructions, tasks for application devices, and/or applicators to guide a parameter dependent variable rate application of the first agricultural product.

The term field parameter as used herein is to be understood broadly in the present case and present any parameter suitable to provide directly or indirectly spatially resolved variable application rates for the first agricultural product. These can be, for example, plant-related parameters and/or soil-related parameters. In this respect, the term soil-related parameters of an agricultural field as used herein is to be understood broadly in the present case and presents any data defining, describing or indicating the properties of the soil. The soil property data may be provided, presented or depicted as a 2-dimensional map of the agricultural field. The soil property data may include soil property parameters like soil organic matter data, soil texture data, cation exchange capacity, total carbon content data, organic carbon content data, inorganic carbon content data, pH-value data of the soil, soil type data, soil texture data, soil temperature data, soil surface temperature data, soil density data, water holding capacity data, soil conductivity data and/or topography data of the agricultural field but is not limited thereto. The soil property parameters may be provided, determined or estimated by physical measurements, e.g. temperature measurements, and/or chemical measurements or chemical experiments/tests, e.g. determination of the pH-value. Further, the soil property parameter as texture and soil organic matter may be combined with plant parameters like weed maps or NDVI, LAI, biomass index, but is not limited thereto. The soil property data may be historical data and/or current data. The soil property data may be provided by geophysical measurements/analysis and/or modelling but is not limited thereto.

The term spot application data as used herein is to be understood broadly in the present case and presents any data providing information required or about a spot application of the second agricultural product on the agricultural field. Such a spot application may be performed as so called on/off application or as a variable application of the further agricultural product. The latter means that not every spot and/or not an entire spot is provided with the same application rate, but with a variable application rate.

The term combined application data as used herein is to be understood broadly in the present case and comprises the variable application data and the spot application data. However, the term combined application data does not mean that the variable application data and the spot application data have to be provided as one data file/container. In the simplest case, both applications are scheduled at the same time and the respective application data of the two application types are output together as combined data. In an example, the combined application data may also comprise a relationship between the variable application data and the spot application data, like dependencies, complementary effects, amplification effects of the two products, which may be taken into account during planning and output as combined application data are provided.

The term soil herbicide product as used herein is to be understood broadly in the present case and presents any herbicide material to be applied onto an agricultural field. Herbicides can specifically be referred to as selective or non-selective herbicides. A selective herbicide controls specific weed species, while leaving the desired crop relatively unharmed. In contrast, a non- selective herbicide, e.g. called total weed killers, kill all plant material with which they come into contact. The term soil herbicide is used in the context for any herbicide showing weed control activity via the soil, but preferably for so-called “residual herbicides” showing a longer activity of some weeks after application. The term soil herbicide product data as used herein is to be understood broadly in the present case and presents any data/information about the soil herbicide product. Exemplary, the soil herbicide product data may include the name of the herbicide, the properties of the herbicide formulation in terms of the weed spectrum covered and in particular of the active ingredients, the application information with respect to the at least one soil property parameter and preferably a soil moisture. Further, the soil herbicide product data may comprise the application time/ season, i.e. summer, winter, autumn, and spring, information about environmental compatibility, sustainability and impact (e.g. buffer zones). Also warnings with respect to the safe use may be included in the soil herbicide product data.

The term weed distribution data as used herein is to be understood broadly in the present case and presents any data/information defining or indicating the existence, distribution and/or appearance of weed plants on the agricultural field. Weed plants are unwanted plants which populations can be managed by using herbicides. The weed distribution data may be depicted as 2-dimensional for one season or a plurality of seasons. The weed distributing data may be historical data indicating/depicting areas of high appearance/high density, i.e. hot-spots, of weeds. The weed distribution data may be provided by scouting, camera or sensor based mapping analysis methods.

The term crop data as used herein is to be understood broadly in the present case and presents any data defining, indicating or giving information about crops being planned to be planted on the agricultural field. The crop data may include data/information about the species of the crop plant and if relevant the herbicide tolerance, trait conditions, in particular soil conditions, enabling a fastest, fruitfullest and productive growth of the crop plant. The crop data may include information about actually planned crop but also about following crop to check on waiting periods. The crop data may be provided by a user via a user interface.

The term historical treatment data as used herein is to be understood broadly in the present case and presents any data/information providing, defining, describing or indicating historical treatments of the agricultural field. Specifically, the historical treatment data may comprise information about treatments performed in previous seasons on the agricultural field. The historical treatment data may be provided as 2-dimensional maps of the agricultural field depicting either treatment information for one specific previous season/ sum of a plurality of specific previous seasons, e.g. depending on weather influences, or a sum for all previous seasons. The historical treatment data are provided by a database and/or a data system.

The term control data as used herein is to be understood broadly in the present case and presents any data being configured to operate and control an application device. The control data are provided by a control unit and may be configured to control one or more technical means of the application device, e.g. the drive control but is not limited thereto.

The term agricultural product application model, e.g. a soil herbicide application model, as used herein is to be understood broadly in the present case and presents any model, i.e. numerical, statistical, forward, backward, prediction model, being usable for modeling the soil herbicide application. The agricultural product application model may also be a trainable/trained model.

The term weighting as used herein is to be understood broadly in the present case and presents any preference or favoritism of available input data for providing/determining the variable soil herbicide application data.

The term soil moisture data as used herein is to be understood broadly in the present case and presents any data describing, defining and/or indicating the water content/moisture content in the soil. The unit of the water content/moisture content is g/m³. The soil moisture data may be provided, presented or depicted as a 2-dimensional map of the agricultural field or as values for the agricultural field. The soil moisture data may be provided by a soil moisture model modeling the water content/moisture content at least based on historical weather data, actual weather data and/or predicted weather data. Further, the soil moisture data may be provided by remote soil moisture sensing data, i.e. by remote sensing data provided by airborne vehicle like drones or airplanes or by satellites. The remote sensing data are provided by using active or passive remote sensing systems. Passive remote sensing systems record or measures the solar radiation reflected from the surface of the earth, e.g. by a multispectral camera, and/or the natural radiation emitted by the surface of the earth, e.g. by a thermal imaging camera. Active remote sensing systems emit microwave or laser beams and receive, measures and interprets the reflected components of the emitted beams, e.g. by radar systems and laser altimeters, wherein it is preferred that SAR satellite for a soil humidity mapping is used. Furthermore, the soil moisture data may be provided by proximal soil moisture sensing data being provided by arranging a plurality of humidity/soil moisture sensors to an agricultural device, i.e. being provided by proximal soil sensing methods. Furthermore, the soil moisture data may be provided by a soil moisture model modeling the water content/moisture content in the soil at least based on moisture measurements, i.e. soil probe measurements, taken at various locations in the agricultural field and/or by soil built-in on-line sensors.

The term application device used herein is to be understood broadly in the present case and represents any device being configured to provide/spread seeds, plants and/or fertilizers onto the soil of an agricultural field. The application device may be configured to traverse the agricultural field. The application device may be a ground or an air vehicle, e.g. a rail vehicle, a robot, an aircraft, an unmanned aerial vehicle (UAV), a drone, or the like. The application device can be an autonomous or a non-autonomous application device. In case, the first agricultural product and the second agricultural product are applied at the same time, the application device may comprise at least two product tanks. However, in an example, two application devices may be used at the same time, each comprising at least one product tank for the first or second agricultural product. In case, the agricultural products are applied at different times, the application device(s) may comprise only one product tank.

The term providing as used herein is to be understood broadly in the present case and represents any providing, receiving, querying, measuring, calculating, determining, transmitting of data, but is not limited thereto. Data may be provided by a user via a user interface, depicted/shown to a user by a display, and/or received from other devices, queried from other devices, measured other devices, calculated by other device, determined by other devices and/or transmitted by other devices. The term data as used herein is to be understood broadly in the present case and represents any kind of data. Data may be single numbers/numerical values, a plurality of a numbers/numerical values, a plurality of a numbers/numerical values being arranged within a list, 2 dimensional maps or 3 dimensional maps, but are not limited thereto.

In an embodiment of the computer-implemented method according, the field parameter data comprise at least one of the following field parameter data: weed distribution data comprising actual weed distribution data and/or historical weed distribution data; soil organic matter distribution data and/or soil texture distribution data; soil fertility distribution data (e.g. based on available nutrients, ability for mineralisation in the vegetation period; provided by means of grid based soil sampling or sensor based soil measuring, e.g. by using VOC (Volatile Organic Compounds) sensors mounted on a vehicle); total carbon content distribution data, organic carbon content distribution data, inorganic carbon content distribution data and/or pH-value distribution data of the soil; nutrient distribution data (e.g. potassium, phosphor, nitrate and/or ammonium content or distribution data); soil type data, soil texture data, soil temperature data, soil surface temperature data, soil density data, and/or water holding capacity data of the soil in the agricultural field; soil conductivity distribution data; biomass distribution data; topography data of the agricultural field; and/or crop data comprising information about the crop planted on the agricultural field and/or about crops planned to be planted on the agricultural field. In an example, based on such field parameter data so called heterogeneity maps of the agricultural field may be provided. Such heterogeneity maps or field parameter data providing a spatial resolution of the respect field parameter over the agricultural field which may be used to adopt the application dose or rate of the agricultural product on the field. Thereby, an agricultural product may only have applied at these locations of the agricultural field where it is needed and/or useful. Moreover, the agricultural product may be applied with such a dose needed/required at the respective location of the agricultural field.

In an embodiment of the computer-implemented method, the sensor data are provided by one of the following: at least one camera sensor and/or other sensor types mounted on an agricultural device; remote sensing means, preferably provided from satellite data; measurements taken at various locations in the field and/or by built-in on-line sensors. In an example, the spot application is triggered by analysing image data of at least a camera unit which is mounted on the agricultural device.

In an embodiment of the computer-implemented method, the variable application and the spot application are performed at the same time or the variable application is performed at a first time ti and the spot application is performed at a second time t2. In an example, a first agricultural product, e.g. a residual soil herbicide product, may be applied in an early growth stage, e.g. at BBCH 11/12, and a second agricultural product, e.g. a foliar herbicide product, may be applied in a later growth stage, e.g. at BBCH 15/16.

In an embodiment of the computer-implemented method, the spot application data comprise threshold value data for applying the second agricultural product indicating at which threshold value an application with the second product is performed. In an embodiment of the computer- implemented method, the variable application data are depending on the spot application data or vice versa, the spot application data are dependent on the variable application data.

In an example, based on the variable application data, a reduction factor or reduction value is derived, wherein the reduction factor or the reduction value preferably depend on the spot application data. In this respect, the relationship between a possible reduction of the application quantities for the variable application in view of a possible compensation effect of the spot application can, for example, be provided by a classic optimization solution. Also a reinforcing and/or complementing effect of the two agricultural products can be provided by such an optimization solution. The goal in this respect is to find an optimal reduced application rate for the first agricultural product, i.e. a reduction of the first agricultural product, which, if the circumstances go well, does not have to be later compensated by the second agricultural product, but which, if necessary, can still be compensated by the spot application of the second agricultural product. In other embodiments, the goal is to determine the maximum amplification effect, e.g. in particular for these embodiments where the first and second agricultural products are applied at the same time. There may also be an interaction between the variable rate application and the spot application intensity or % area needs to be spotted during the spot application, which is also dependent on the precipitation and properties of the active ingredients as residual herbicide. This interaction may also allow an optimization of agricultural products, e.g. herbicide products, savings and efficacy.

In an embodiment of the computer-implemented method, the variable application data comprise at least one of the following: application time data comprising at least one-time window for applying the first agricultural product on the agricultural field; dose rate data comprising at least one dose rate for applying the first agricultural product on the agricultural field, wherein the dose rates for applying the first agricultural product are preferably provided for sub-areas of the agricultural field; spatial variation data related to sub-field areas of the agricultural field; and/or at least one application map for applying the first agricultural product and/or the second agricultural product on the agricultural field. In an embodiment of the computer-implemented method, the agricultural products for the variable application data and the spot application data are a combination of one of the following: a soil herbicide product as first agricultural product and a foliar herbicide product as second agricultural product; such a combination and optimization of these agricultural products may allow savings and lower plant stress; a liquid fertilizer product, preferably a nitrogen based fertilizer, as first agricultural product and a foliar herbicide product as second agricultural product; as liquid fertilizer, e.g. an Ammonium-Urea-Solution, applied together with an herbicide product may cause a comparable high phytotoxicity effect, an adaption of the rates of the liquid fertilizer and a spot application of the foliar herbicide product reducing the area covered by the foliar herbicide product may provide a reduced phytotoxicity effect; a fungicide and plant growth regulator mixture product as first agricultural product and a foliar herbicide product as second agricultural product; plant stress and plant damage from an application of a fungicide and plant growth regulator mixture product and a foliar herbicide product may be reduced by an adaption of the application rates of the fungicide and plant growth regulator mixture and the spot application of the foliar herbicide product reducing the area covered by the foliar herbicide product; a nematizide and/or insecticide product as first agricultural product and a foliar herbicide product as second agricultural product; plant stress and plant damage from an application of a nematizide and/or insecticide product and a foliar herbicide product may be reduced by an adaption of the application rates of the nematizide and/or insecticide product and the spot application of the foliar herbicide product reducing the area covered by the foliar herbicide product; a plant growth regulator product as first agricultural product and a foliar herbicide product as second agricultural product; plant stress and plant damage from an application of a plant growth regulator product and a foliar herbicide product may be reduced by an adaption of the application rates of the plant growth regulator product and the spot application of the foliar herbicide product reducing the area covered by the foliar herbicide product; a plant growth regulator product as first agricultural product and a fungicide product as second agricultural product; a microorganism product promoting soil health and/or soil fertility as first agricultural product and a foliar herbicide product, nitrogen containing product or a plant growth regulator product; and/or an insecticide product as first agricultural product and a plant growth regulator product as second agricultural product.

Notably, also combinations of the above mentioned agricultural products may be applied.

In an embodiment of the computer-implemented method, the method is further comprising: providing soil moisture data of the agricultural field, wherein providing variable application data for applying the agricultural products, e.g. a residual soil herbicide product as first product, on the agricultural field is further based on the soil moisture data, wherein the soil moisture data is provided by/as: a soil moisture model at least based on historical weather data, actual weather data and/or predicted weather data; remote soil moisture sensing data, preferably provided from satellite data; proximal soil moisture sensing data, preferably provided by humidity/soil moisture sensors attached to an agricultural device; and/or a soil moisture model at least based on moisture measurements taken at various locations in the field and/or by soil built-in on-line sensors.

In an embodiment of the computer-implemented method, the method is further comprising: providing precipitation data of the agricultural field, wherein providing application data for applying the agricultural products, e.g. a residual soil herbicide product as first agricultural product, on the agricultural field is further based on the precipitation data. In an example, providing variable application data for applying the first agricultural product, e.g. a soil herbicide product, on the agricultural field further comprises determining whether a soil moisture value and/or a precipitation value of the agricultural field is equal or below a predefined threshold and in case the determined soil moisture value and/or precipitation value is equal or below the predefined threshold, providing pesticide application data indicating non-application data.

The precipitation data may relate to historical, actual and/or forecast precipitation data.

In an embodiment of the computer-implemented, in case the first agricultural product is a residual soil herbicide product, in a first step soil herbicide product data for different/pre- selected soil herbicide products may be provided comprising information and/or recommendations about soil moisture values and/or precipitation values required/beneficial for a sufficient efficacy of the soil herbicide product. In a further step, a soil moisture value and/or a precipitation value may be provided/determined, e.g. based on weather and/or sensor data.

Based on the soil moisture value and/or a precipitation value it may be determined whether or not the agricultural field may be treated at all, e.g. in case the soil moisture value and/or a precipitation value is very low. In case, the soil moisture value and/or the precipitation value is sufficient high, it may be determined which soil herbicide products might be applied in view of the soil moisture value and/or a precipitation value. In this respect, different soil herbicide products may be provided for different soil moisture values ranges and/or precipitation value ranges.

In an embodiment of the method for providing variable application data, in case the first agricultural product is a soil herbicide product, providing variable soil herbicide application data for applying a soil herbicide product on the agricultural field is further based on soil moisture data, wherein the soil moisture data is provided by/as: the soil moisture data is provided by/as: a soil moisture model at least based on historical weather data, actual weather data and/or predicted weather data; remote soil moisture sensing data, preferably provided from satellite data; proximal soil moisture sensing data, preferably provided by humidity/soil moisture sensors attached to an agricultural device; or a soil moisture model at least based on moisture measurements taken at various locations in the field and/or by built-in on-line soil sensors.

In an embodiment, the first agricultural product is a soil herbicide products, wherein the soil herbicide product is/comprises, inter alias, Isoxaflutole, Flufenacet, Dimethenamid-P, S- Metolachlor, Pendimethalin, Aclonifen, Acetochlor, Atrazin, Terbutylazin, S-Metolachlor, Metolachlor, Metribuzin, Pyroxasulfone, Cloransulam-methyl, Imazamethayr, Dimethenamid-P, Metamitrion, Ethofumesate, Quimerac, Prosulfocarb, Chlortoluron, Cinmethylin, Pendimethalin, Mesotrione, Tembotrione, Clopyralid, Sulfentrazone, Saflufenacil, Imazethapyr, Imazamox, Trifluralin, Triallate and/or a combination thereof. Generally, the term soil herbicide product is not limited to herbicide products which are only absorbed via the roots of the plants (i.e. are only active via the soil), this term also includes herbicide products that are at least partially active via the soil. Preferably, the herbicide products are significantly active via the soil, e.g. are absorbed via the roots of the plant. In such an embodiment, the method may further comprise the step of providing weed distribution data comprising actual weed distribution data and/or historical weed distribution data, wherein providing variable soil herbicide application data for applying a soil herbicide product on the agricultural field is further based on the weed distribution data. By including the weed distribution data into the providing/determination of the variable soil herbicide application data, an efficient, sustainable and robust way for providing variable soil herbicide application data can be provided. Therefore, the effectivity of the application of the soil herbicides can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved. In such an embodiment, the method may further comprise the step of providing crop data comprising information about crops planned to be planted on the agricultural field, wherein providing variable soil herbicide application data for applying a soil herbicide product on the agricultural field is further based on the crop data. By including the crop data into the providing/determination of the variable soil herbicide application data, an efficient, sustainable and robust way for providing variable soil herbicide application data can be provided. Therefore, the effectivity of the application of the soil herbicides can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved. In such an embodiment, the method may further comprise the step of providing historical treatment data comprising information about treatments performed in previous seasons on the agricultural field, preferably comprising information about the mode of the historical treatment action as factor in resistance management, wherein providing variable soil herbicide application data for applying a soil herbicide product on the agricultural field is further based on the historical treatment data. By including the historical treatment data into the providing/determination of the variable soil herbicide application data, an efficient, sustainable and robust way for providing variable soil herbicide application data can be provided. Therefore, the effectivity of the application of the soil herbicides can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved.

In an embodiment of the method for providing combined application data, when providing the variable application data, the available input data are weighted and/or a trained application model is used to provide the variable application data. A weighting of input parameters and/or the use of a trained application model enables a highlighting of several factors/data being more important for the variable application data.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following, the present disclosure is further described with reference to the enclosed figures:

Figure 1 illustrate example embodiments of a centralized and a decentralized computing environment with computing nodes;

Figure 2 illustrate example embodiments of a centralized and a decentralized computing environment with computing nodes;

Figure 3 illustrate an example embodiment of a distributed computing environment; Figure 4 illustrates a flow diagram of an example method for providing combined application data;

Figure 5 illustrates a schematic illustration of a system for providing combined application data;

Figure 6 illustrates a schematic illustration of an application of two agricultural products at different application times;

Figure 7 illustrates a schematic illustration of an application of two agricultural products at the same application time;

Figure 8 illustrates an optional workflow for a treatment decision and a treatment product selection based on the predicted cumulative precipitation amount;

Figure 9 illustrates an optional exemplary workflow for determining variable rate application data for the treatment of an agricultural field with a soil herbicide product;

Figure 10 illustrates a further optional exemplary workflow to determine variable rate application data for the treatment of an agricultural field with a soil herbicide product; and

Figure 11 illustrates exemplarily the different possibilities to receive and process field data.

DETAILED DESCRIPTION OF EMBODIMENT

The following embodiments are mere examples for implementing the methods, the systems, the apparatus or the computer elements disclosed herein and shall not be considered limiting.

The following embodiments are mere examples for implementing the method, the system, the apparatus, or application device disclosed herein and shall not be considered limiting.

Figures 1 to 3 illustrate different computing environments, central, decentral and distributed.

The methods, apparatuses, computer elements of this disclosure may be implemented in decentral or at least partially decentral computing environments. In particular, for data sharing or exchange in ecosystems of multiple players different challenges exist. Data sovereignty may be viewed as a core challenge. It can be defined as a natural person’s or corporate entity’s capability of being entirely self-determined with regard to its data. To enable this particular capability related aspects, including requirements for secure and trusted data exchange in business ecosystems, may be implemented across the chemical value chain. In particular, chemical industry requires tailored solutions to deliver chemical products in a more sustainable way by using digital ecosystems. Providing, determining or processing of data may be realized by different computing nodes, which may be implemented in a centralized, a decentralized or a distributed computing environment.

Figure 1 illustrates an example embodiment of a centralized computing system 20 comprising a central computing node 21 (filled circle in the middle) and several peripheral computing nodes 21.1 to 21. n (denoted as filled circles in the periphery). The term “computing system” is defined herein broadly as including one or more computing nodes, a system of nodes or combinations thereof. The term “computing node” is defined herein broadly and may refer to any device or system that includes at least one physical and tangible processor, and/or a physical and tangible memory capable of having thereon computer-executable instructions that are executed by a processor. Computing nodes are now increasingly taking a wide variety of forms. Computing nodes may, for example, be handheld devices, production facilities, sensors, monitoring systems, control systems, appliances, laptop computers, desktop computers, mainframes, data centers, or even devices that have not conventionally been considered a computing node, such as wearables (e.g., glasses, watches or the like). The memory may take any form and depends on the nature and form of the computing node.

In this example, the peripheral computing nodes 21.1 to 21. n may be connected to one central computing system (or server). In another example, the peripheral computing nodes 21.1 to 21. n may be attached to the central computing node via e.g. a terminal server (not shown). The majority of functions may be carried out by, or obtained from the central computing node (also called remote centralized location). One peripheral computing node 21. n has been expanded to provide an overview of the components present in the peripheral computing node. The central computing node 21 may comprise the same components as described in relation to the peripheral computing node 21. n.

Each computing node 21, 21.1 to 21. n may include at least one hardware processor 22 and memory 24. The term “processor” may refer to an arbitrary logic circuitry configured to perform basic operations of a computer or system, and/or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processor, or computer processor may be configured for processing basic instructions that drive the computer or system. It may be a semi-conductor based processor, a quantum processor, or any other type of processor configures for processing instructions. As an example, the processor may comprise at least one arithmetic logic unit ("ALU"), at least one floating-point unit ("FPU)", such as a math coprocessor or a numeric coprocessor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an L1 and L2 cache memory. In particular, the processor may be a multicore processor. Specifically, the processor may be or may comprise a Central Processing Unit ("CPU"). The processor may be a (“GPU”) graphics processing unit, (“TPU”) tensor processing unit, ("CISC") Complex Instruction Set Computing microprocessor, Reduced Instruction Set Computing ("RISC") microprocessor, Very Long Instruction Word ("VLIW') microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing means may also be one or more special-purpose processing devices such as an Application- Specific Integrated Circuit ("ASIC"), a Field Programmable Gate Array ("FPGA"), a Complex Programmable Logic Device ("CPLD"), a Digital Signal Processor ("DSP"), a network processor, or the like. The methods, systems and devices described herein may be implemented as software in a DSP, in a micro-controller, or in any other side-processor or as hardware circuit within an ASIC, CPLD, or FPGA. It is to be understood that the term processor may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.

The memory 24 may refer to a physical system memory, which may be volatile, non-volatile, or a combination thereof. The memory may include non-volatile mass storage such as physical storage media. The memory may be a computer-readable storage media such as RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, non-magnetic disk storage such as solid-state disk or any other physical and tangible storage medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by the computing system. Moreover, the memory may be a computer-readable media that carries computer- executable instructions (also called transmission media). Further, upon reaching various computing system components, program code means in the form of computerexecutable instructions or data structures can be transferred automatically from transmission media to storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computing system RAM and/or to less volatile storage media at a computing system. Thus, it should be understood that storage media can be included in computing components that also (or even primarily) utilize transmission media.

The computing nodes 21 , 21.1 to 21. n may include multiple structures 26 often referred to as an “executable component, executable instructions, computer-executable instructions or instructions”. For instance, memory 24 of the computing nodes 21, 21.1 to 21. n may be illustrated as including executable component 26. The term “executable component” or any equivalent thereof may be the name for a structure that is well understood to one of ordinary skill in the art in the field of computing as being a structure that can be software, hardware, or a combination thereof or which can be implemented in software, hardware, or a combination. For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component includes software objects, routines, methods, and so forth, that is executed on the computing nodes 21 , 21.1 to 21. n, whether such an executable component exists in the heap of a computing node 21, 21.1 to 21. n, or whether the executable component exists on computer-readable storage media. In such a case, one of ordinary skill in the art will recognize that the structure of the executable component exists on a computer- readable medium such that, when interpreted by one or more processors of a computing node 21, 21.1 to 21. n (e.g., by a processor thread), the computing node 21 , 21.1 to 21n is caused to perform a function. Such a structure may be computer-readable directly by the processors (as is the case if the executable component were binary). Alternatively, the structure may be structured to be interpretable and/or compiled (whether in a single stage or in multiple stages) so as to generate such binary that is directly interpretable by the processors. Such an understanding of example structures of an executable component is well within the understanding of one of ordinary skill in the art of computing when using the term “executable component”. Examples of executable components implemented in hardware include hardcoded or hard-wired logic gates, that are implemented exclusively or near-exclusively in hardware, such as within a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other specialized circuit. In this description, the terms “component”, “agent”, “manager”, “service”, “engine”, “module”, “virtual machine” or the like are used synonymous with the term “executable component.

The processor 22 of each computing node 21 , 21.1 to 21. n may direct the operation of each computing node 21, 21.1 to 21. n in response to having executed computer-executable instructions that constitute an executable component. For example, such computer-executable instructions may be embodied on one or more computer-readable media that form a computer program product. The computer-executable instructions may be stored in the memory 24 of each computing node 21 , 21.1 to 21. n. Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor 21, cause a general purpose computing node 21 , 21.1 to 21. n, special purpose computing node 21 , 21.1 to 21. n, or special purpose processing device to perform a certain function or group of functions.

Alternatively or in addition, the computer-executable instructions may configure the computing node 21 , 21.1 to 21. n to perform a certain function or group of functions. The computer executable instructions may be, for example, binaries or even instructions that undergo some translation (such as compilation) before direct execution by the processors, such as intermediate format instructions such as assembly language, or even source code.

Each computing node 21, 21.1 to 21. n may contain communication channels 28 that allow each computing node 21.1 to 21. n to communicate with the central computing node 21, for example, a network (depicted as solid line between peripheral computing nodes and the central computing node in Figure 1). A “network” may be defined as one or more data links that enable the transport of electronic data between computing nodes 21, 21.1 to 21.n and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computing node 21 , 21.1 to 21. n, the computing node 21, 21.1 to 21. n properly views the connection as a transmission medium. Transmission media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general- purpose or special-purpose computing nodes 21, 21.1 to 21.n. Combinations of the above may also be included within the scope of computer-readable media.

The computing node(s) 21, 21.1 to 21. n may further comprise a user interface system 25 for use in interfacing with a user. The user interface system 25 may include output mechanisms 25A as well as input mechanisms 25B. The principles described herein are not limited to the precise output mechanisms 25A or input mechanisms 25B as such will depend on the nature of the device. However, output mechanisms 25A might include, for instance, displays, speakers, displays, tactile output, holograms and so forth. Examples of input mechanisms 25B might include, for instance, microphones, touchscreens, holograms, cameras, keyboards, mouse or other pointer input, sensors of any type, and so forth.

Figure 2 illustrates an example embodiment of a decentralized computing environment 30 with several computing nodes 21.1 to 21.n denoted as filled circles. In contrast to the centralized computing environment 20 illustrated in Figure 1, the computing nodes 21.1 to 21. n of the decentralized computing environment are not connected to a central computing node 21 and are thus not under control of a central computing node. Instead, resources, both hardware and software, may be allocated to each individual computing node 21.1 to 21. n (local or remote computing system) and data may be distributed among various computing nodes 21.1 to 21. n to perform the tasks. Thus, in a decentral system environment, program modules may be located in both local and remote memory storage devices. One computing node 21 has been expanded to provide an overview of the components present in the computing node 21. In this example, the computing node 21 comprises the same components as described in relation to Figure 1.

Figure 3 illustrates an example embodiment of a distributed computing environment 40. In this description, “distributed computing” may refer to any computing that utilizes multiple computing resources. Such use may be realized through virtualization of physical computing resources.

One example of distributed computing is cloud computing. “Cloud computing” may refer a model for enabling on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). When distributed, cloud computing environments may be distributed internationally within an organization and/or across multiple organizations. In this example, the distributed cloud computing environment 40 may contain the following computing resources: mobile device(s) 42, applications 43, databases 44, data storage and server(s) 46. The cloud computing environment 40 may be deployed as public cloud 47, private cloud 48 or hybrid cloud 49. A private cloud 47 may be owned by an organization and only the members of the organization with proper access can use the private cloud 48, rendering the data in the private cloud at least confidential. In contrast, data stored in a public cloud 48 may be open to anyone over the internet. The hybrid cloud 49 may be a combination of both private and public clouds 47, 48 and may allow to keep some of the data confidential while other data may be publicly available.

Figure 4 illustrates a flow diagram of an example method for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field.

In a first step, field parameter data comprising spatial distribution data of at least one field parameter in the agricultural field are provided. In an example, the field parameter data is weed distribution data comprising actual weed distribution data and/or historical weed distribution data, soil organic matter distribution data and/or soil texture distribution data. In a second step, variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data are provided. In an example, based on such field parameter data so called heterogeneity maps of the agricultural field may be provided. Such heterogeneity maps or field parameter data providing a spatial resolution of the respect field parameter over the agricultural field which may be used to adopt the application dose or rate of the agricultural product on the field. In an example, the first agricultural product is a residual soil herbicide product.

In a third step sensor data, e.g. by means of a camera sensor/unit, with respect to the agricultural field are provided. In a fourth step, spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data are provided. For example, the spot application data may comprise threshold value data for applying the second agricultural product indicating at which threshold value of a sensor value an application with the second product is performed. In an example, the second agricultural product is a foliar herbicide product.

In a further step, combined application data comprising the variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field are provided. In an example, the combined application data may not only comprise the application data for both agricultural products, but also further information. In an example, the variable application data may comprise a reduction factor or reduction value, wherein the reduction factor or the reduction value preferably depend on the spot application data. In this respect, the relationship between a possible reduction of the application quantities for the variable application in view of a possible compensation effect of the spot application can, for example, be provided by a classic optimization solution.

In an example, the following agricultural product are combined: a soil herbicide product as first agricultural product and a foliar herbicide product as second agricultural product; a liquid fertilizer product, preferably a nitrogen based fertilizer, as first agricultural product and a foliar herbicide product as second agricultural product; a fungicide and plant growth regulator mixture product as first agricultural product and a foliar herbicide product as second agricultural product; a nematizide product as first agricultural product and a foliar herbicide product as second agricultural product; a plant growth regulator product as first agricultural product and a foliar herbicide product as second agricultural product; a plant growth regulator product as first agricultural product and a fungicide product as second agricultural product; a microorganism product promoting soil health and/or soil fertility as first agricultural product and a foliar herbicide product, nitrogen containing product or a plant growth regulator product; and/or an insecticide product as first agricultural product and a plant growth regulator product as second agricultural product. Notably, also combination of these agricultural products may be applied.

Figure 5 illustrates a schematic illustration of a system 10 for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field. The system 10 for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field comprises a first providing unit 11 configured to provide field parameter data comprising data of the spatial distribution of at least one field parameter in the agricultural field, a second providing unit 12 configured to provide variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data, a third providing unit 13 configured to provide spot application data for applying the second agricultural product on the agricultural field at least based on sensor data, and a fourth providing unit 14 configured to provide combined application data comprising variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.

Figure 6 illustrates a schematic illustration of a variable application of a first agricultural product, at a first time ti , e.g. in an early growth stage. Subsequently, at a second time t2, a second agricultural product is applied as spot application. Such a spot application may be performed as so called on/off application or as a variable spot application of the second agricultural product.

In an example, the first agricultural product is a residual soil herbicide product, which is applied at BBCH 11/12 and the second agricultural product is a foliar herbicide product, which is applied at BBCH 15/16. In the shown example, it is possible that “riskier” decisions can be made with respect to the application rates of the first agricultural product, if the second agricultural product can, if necessary, compensate for a wrong decision with respect to the first agricultural product.

Risk in this context can mean, for example, that a too low application rate is selected for the first product, for example a soil herbicide. If, for example, it turns out that the soil herbicide has been applied with an insufficient application rate, a certain compensation can be provided by a later spot application of a foliar herbicide. Moreover, if the soil herbicide was applied at a time that turns out to be less than optimal, for example, if there was too little soil moisture after the soil herbicide was applied to “activate” the soil herbicide, compensation can be provided by the second product. In addition, or alternatively, it is also possible to adjust the threshold values for the spot application dependent on the precipitation after the variable application. For example, in case the precipitation after the variable application was sufficient for fully activating the soil herbicide product, the weed covering after the variable application is low and a high threshold value for the spot application may be selected resulting in high savings with respect to the second agricultural product. On the other hand, in case the precipitation after the variable application was insufficient for fully activating the soil herbicide product, the weed covering after the variable application is high and a low threshold value for the spot application may be selected, thereby compensating the not optimal decision with respect to the application of the first agricultural product. In an example, the relationship between a possible reduction of the application quantities for the variable application in view of a potential compensation effect of the spot application can, for example, be provided by a classic optimization solution and/or by a trained model/algorithm. Moreover, the relationship between an activation of the first agricultural product and a potential compensation effect of the spot application may also be provided by a classic optimization solution and/or by a trained model/algorithm. In an example, such models are applied for providing the combined application data.

Figure 7 illustrates a schematic illustration of a variable application of a first agricultural product and a spot application of a second agricultural product at the same time. In an example, the first agricultural product is a residual soil herbicide product and the second agricultural product is a foliar herbicide product. Here, both agricultural products may have reinforcing and/or complementing effects allowing a reduction of the needed agricultural products for treating an agricultural field. Also here, finding the optimal quantities with respect to such reinforcing and/or complementing effects may be subject of optimizations and/or training models/algorithms. In an example, the variable application of the first agricultural product and the spot application of the second agricultural product may be repeated several times at different times, e.g. in sequences at different plant growth stages.

Figure 8 illustrates an optional exemplary workflow for a treatment decision with respect to the variable application and a treatment product selection based on predicted cumulative precipitation amount within the next 5 days. Time window, thresholds for precipitation amount, data layers and treatment products are only illustrative and may vary depending on field characteristics, region, soil properties, treatment product category, weather conditions and/or climatic setting. The treatment decision and the treatment product selection can also be based on soil property data/field parameter data and/or the current and predicted soil moisture. As shown in Figure 8, in a first step weed spectrum data are gathered and a pre-selection of suitable first agricultural products, here soil herbicide products, for such the determined weed spectrum is provided. In a further step, weather data for the agricultural field is provided and the predicted cumulative precipitation amount for the next five days is determined. Based on this cumulative precipitation amount, it is decided whether or not the agricultural field is to be treated at all. For example, in case the cumulative precipitation amount is below a predefined amount, e.g. here 6 mm, it is decided not to treat the agricultural field, since the efficacy of some agricultural products, like soil herbicides dependents on a sufficient precipitation to transport the agricultural product to the roots of the plants. In case, the cumulative precipitation amount is above the predefined amount, it can further be decided to select a specific first agricultural product, e.g. a specific soil herbicide dependent on the cumulative precipitation amount. In the shown example, it is decided to select an amid-based soil herbicide, if the cumulative precipitation amount is not between 6 and 20 mm and to select a dimethenamid based soil herbicide, if the cumulative precipitation amount is between 6 and 20 mm.

Figure 9 illustrates an optional exemplary workflow for determining variable rate application data for the treatment of an agricultural field with a first agricultural product, here soil herbicide product. This workflow can be performed following the workflow shown in Figure 8. In this example, the rate at which a treatment product is applied is based on soil texture and soil organic matter (SOM). Treatment rates, SOM thresholds and soil texture categories are only illustrative and may vary depending on field characteristics, region, soil properties, treatment product category, weather conditions and/or climatic setting. The variable rate application data can also be based on other field parameters/ soil properties and/or on weed distribution data to indicate parts of an agricultural with problematic weeds or a high density of weeds (“hot spots”). As shown, based on the provided soil texture data and soil organic matter data, respective amounts of the soil herbicide product are determined.

In the shown example, in case, the soil texture has been determined as “coarse”, the following exemplary application/treatment rates are provided for the variable application of the soil herbicide product:

• SOM < 1.5% - 1.4 l/ha;

• 1.5% < SOM < 3% - 1.7 l/ha; and

• SOM > 3% - 2 l/ha.

In case, the soil texture has been determined as “fine”, the following exemplary application/treatment rates are provided for the variable application of the soil herbicide product:

SOM < 1.5% - 1.7 l/ha; and

SOM > 1.5% - 2.0 l/ha. Basically, as soil organic matter increases, more soil herbicide product is applied. In one example, minimum and maximum application rates for the application of the soil herbicide product are predefined. Subsequently, an approximately linear distribution of the application rates between the predefined minimum and maximum application rates are provided depending on the soil organic matter values. In the shown example, the application rates for applying the soil herbicide product have been increased if the soil texture has been determined as “fine”.

Figure 10 illustrates an exemplary workflow to determine variable rate application data for the treatment of an agricultural field with a soil herbicide product as a first agricultural product. In this example, the variable rate application data is determined by soil texture data, soil organic matter data and optional weed distribution data to indicate parts of the agricultural field with a high density of weeds (“hot spots”). Two sample points indicate locations in the agricultural field where soil texture and soil organic matter content are the same, but where the weed distribution is different which is why the final treatment rate is different as indicated by the variable rate application data (cf. Figure 9). The variable application rate data can also be represented as a grid that corresponds to the nozzles or sections of a sprayer. The soil herbicide can be applied separately according to the variable rate data or in combination with the spot application of a foliar herbicide. The shown values, categories and thresholds are only illustrative and may vary depending on field characteristics, region, soil properties, treatment product category, weather conditions and/or climatic setting. Dashed lines indicate optional elements.

Figure 11 illustrates exemplarily the different possibilities to receive and process field data. For example, field data can be obtained by all kinds of agricultural equipment 300 (e.g. a tractor 300) as so-called as-applied maps by recording the application rate at the time of application. It is also possible that such agricultural equipment comprises sensors (e.g. optical sensors, cameras, infrared sensors, soil sensors, etc.) to provide, for example, a weed distribution map. It is also possible that during harvesting the yield (e.g. in the form of biomass) is recorded by a harvesting vehicle 310. Furthermore, corresponding maps/data can be provided by land-based and/or airborne drones 320 by taking images of the field or a part of it. Finally, it is also possible that a geo-referenced visual assessment 330 is performed and that this field data is also processed. Field data collected in this way can then be merged in a computing device 340, where the data can be transmitted and computed, for example, via any wireless link, cloud applications 350 and/or working platforms 360, wherein the field data may also be processed in whole or in part in the cloud application 350 and/or in the working platform 360 (e.g., by cloud computing). Aspects of the present disclosure relates to computer program elements configured to carry out steps of the methods described above. The computer program element might therefore be stored on a computing unit of a computing device, which might also be part of an embodiment.

This computing unit may be configured to perform or induce performing of the steps of the method described above. Moreover, it may be configured to operate the components of the above described system. The computing unit can be configured to operate automatically and/or to execute the orders of a user. The computing unit may include a data processor. A computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to carry out the method according to one of the preceding embodiments. This exemplary embodiment of the present disclosure covers both, a computer program that right from the beginning uses the present disclosure and computer program that by means of an update turns an existing program into a program that uses the present disclosure. Moreover, the computer program element might be able to provide all necessary steps to fulfill the procedure of an exemplary embodiment of the method as described above. According to a further exemplary embodiment of the present disclosure, a computer readable medium, such as a CD- ROM, USB stick, a downloadable executable or the like, is presented wherein the computer readable medium has a computer program element stored on it which computer program element is described by the preceding section. A computer program may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. However, the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network. According to a further exemplary embodiment of the present disclosure, a medium for making a computer program element available for downloading is provided, which computer program element is arranged to perform a method according to one of the previously described embodiments of the present disclosure.

The present disclosure has been described in conjunction with a preferred embodiment as examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims. Notably, in particular, the any steps presented can be performed in any order, i.e. the present invention is not limited to a specific order of these steps. Moreover, it is also not required that the different steps are performed at a certain place or at one node of a distributed system, i.e. each of the steps may be performed at a different nodes using different equipment/data processing units. In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims.

The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.

Claims

1 . Computer-implemented method for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the method comprising: providing field parameter data comprising spatial distribution data of at least one field parameter in the agricultural field; providing variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; providing sensor data with respect to the agricultural field; providing spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data; providing combined application data comprising the variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.

2. Computer-implemented method according to claim 1 , wherein the field parameter data comprise at least one of the following field parameter data: weed distribution data comprising actual weed distribution data and/or historical weed distribution data; soil organic matter distribution data and/or soil texture distribution data; soil fertility distribution data; total carbon content distribution data, organic carbon content distribution data, inorganic carbon content distribution data and/or pH-value distribution data of the soil; nutrient distribution data; soil type data, soil texture data, soil temperature data, soil surface temperature data, soil density data, and/or water holding capacity data of the soil in the agricultural field; soil conductivity distribution data; biomass distribution data; topography data of the agricultural field; and/or crop data comprising information about the crop planted on the agricultural field and/or about crops planned to be planted on the agricultural field. Computer-implemented method according to claims 1 or 2, wherein the sensor data are provided by one of the following: at least one camera sensor mounted on an agricultural device; remote sensing means, preferably provided from satellite data; measurements taken at various locations in the field and/or by built-in on-line sensors. Computer-implemented method according to any one of the preceding claims, wherein the variable application and the spot application are performed at the same time or the variable application is performed at a first time ti and the spot application is performed at a second time t2. Computer-implemented method according to any one of the preceding claims, wherein the spot application data comprise threshold value data for applying the second agricultural product indicating at which threshold value an application with the second agricultural product is performed. Computer-implemented method according to any one of the preceding claims, wherein the variable application data are dependent on the spot application data or the spot application data are dependent on the variable application data. Computer-implemented method according to any one of the preceding claims, wherein the variable application data comprise at least one of the following: application time data comprising at least one-time window for applying the first agricultural product on the agricultural field; dose rate data comprising at least one dose rate for applying the first agricultural product on the agricultural field, wherein the dose rates for applying the first agricultural product are preferably provided for sub-areas of the agricultural field; spatial variation data related to sub-field areas of the agricultural field; and/or at least one application map for applying the first agricultural product and/or the second agricultural product on the agricultural field. Computer-implemented method according to any one of the preceding claims, wherein the agricultural products for the variable application data and the spot application data are one of the following combinations: a soil herbicide product as first agricultural product and a foliar herbicide product as second agricultural product; a liquid fertilizer product, preferably a nitrogen based fertilizer, as first agricultural product and a foliar herbicide product as second agricultural product; a fungicide and plant growth regulator mixture product as first agricultural product and a foliar herbicide product as second agricultural product; a nematizide product as first agricultural product and a foliar herbicide product as second agricultural product; a plant growth regulator product as first agricultural product and a foliar herbicide product as second agricultural product; a plant growth regulator product as first agricultural product and a fungicide product as second agricultural product; a microorganism product promoting soil health and/or soil fertility as first agricultural product and a foliar herbicide product, nitrogen containing product or a plant growth regulator product; and/or an insecticide product as first agricultural product and a plant growth regulator product as second agricultural product. Computer-implemented method according to any one of the preceding claims, further comprising: providing soil moisture data of the agricultural field, wherein providing variable application data for applying the agricultural products on the agricultural field is further based on the soil moisture data, wherein the soil moisture data is provided by/as: a soil moisture model at least based on historical weather data, actual weather data and/or predicted weather data; remote soil moisture sensing data, preferably provided from satellite data; proximal soil moisture sensing data, preferably provided by humidity/soil moisture sensors attached to an agricultural device; and/or a soil moisture model at least based on moisture measurements taken at various locations in the field and/or by soil built-in on-line sensors. Computer-implemented method according to any one of the preceding claims, further comprising: providing precipitation data of the agricultural field, wherein providing application data for applying the agricultural products on the agricultural field is further based on the precipitation data.

11. Application device for applying the agricultural products on an agricultural field, wherein the application device is controlled at least based on application data are provided by a method according to any one of claims 1 to 10, wherein the application device preferably comprises at least two product tanks.

12. System for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the system comprising: a first providing unit configured to provide field parameter data comprising data of the spatial distribution of at least one field parameter in the agricultural field; a second providing unit configured to provide variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; a third providing unit configured to provide sensor data with respect to the agricultural field; a fourth providing unit configured to provide spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data; a fifth providing unit configured to provide combined application data comprising variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.

13. An apparatus for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: providing field parameter data comprising data of the spatial distribution of at least one field parameter in the agricultural field; providing variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; providing sensor data with respect to the agricultural field; providing spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data; providing combined application data comprising variable application data for applying the first agricultural product on the agricultural field and spot application data for applying the second agricultural product on the agricultural field. 14. Use of variable application data and/or spot application data for providing combined application data in a method according to any one of claims 1 to 10.

15. Computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the method according to any one of the claims 1 to 10 in a system according to claim 12.