WO2024033483A1

WO2024033483A1 - Providing of benefit index data

Info

Publication number: WO2024033483A1
Application number: PCT/EP2023/072195
Authority: WO
Inventors: Daniel Ebersold; Clemens Christian DELATREE; Dominic Sturm; Guilherme FAGANELLO DRESSANO; Greg Robert KRUGER; Holger Hoffmann; Andrew David HUNT; Steffen TELGMANN; Marcel Enzo GAUER; Peter HOEPER; Erik Hass; Carvin Guenther SCHEEL; Bjoern Kiepe
Original assignee: Basf Se
Priority date: 2022-08-12
Filing date: 2023-08-10
Publication date: 2024-02-15

Abstract

A computer-implemented method for providing benefit index data, the method comprising the steps of: providing image data of at least one section of an agricultural field; providing field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field; providing non-crop species data comprising data of different non-crop species; providing benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field based on the non-crop species data and the crop species data; analyzing the provided image data of the at least one section of the agricultural field for classifying the non-crop species on the agricultural field, and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field; providing benefit index data for each non-crop species based on the specie of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field.

Description

PROVIDING OF BENEFIT INDEX DATA

TECHNICAL FIELD

The present disclosure relates to a computer-implemented method for providing benefit index data, an apparatus for providing benefit index data, a use of image data for providing benefit index data, and a computer program element.

TECHNICAL BACKGROUND

The general background of this disclosure is the providing of benefit index data.

In conventional agricultural farming, plants onto an agricultural field are strict classified into crop plants and non-crop plants, wherein the non-crop plants are normally controlled/ treated and therefore removed from the agricultural field, such that only crop plants remain on the agricultural field. Current research shows that not all non-crop plants may have a negative effect on the crop plants, in particular some non-crop plants may have a positive effect, such that a removing of these positive non-crop plants leads to a decrease of the harvest of the crop plants. Exemplary, positive effects are the storage of surplus nutritive substances in the non-crop plants and the release from the stored surplus nutritive substances later, the storage of detrimental nutritive substances in the non-crop plants, the attraction of helpful animals and/or insects.

It has been found that a further need exists to provide benefit index data indicating the benefit of the non-crop such that the positive effects of the positive non-crop plants can be included in the farming for increasing the harvest.

SUMMARY OF THE INVENTION

In one aspect of the present disclosure, a computer-implemented method for providing benefit index data is presented, the method comprises the steps of: providing image data of at least one section of an agricultural field; providing field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field; providing non-crop species data comprising data of different non-crop species; providing benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field based on the non-crop species data and the crop species data; analyzing the provided image data of the at least one section of the agricultural field for classifying the non-crop species on the agricultural field, and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field; providing benefit index data for each non-crop species based on the benefit data of each species of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field.

In a further aspect of the present disclosure, an apparatus for providing benefit index data is presented, the apparatus comprises: 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 image data of at least one section of an agricultural field; providing field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field; providing non-crop species data comprising data of different non-crop species; providing benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field based on the non-crop species data and the crop species data; analyzing the provided image data of the at least one section of the agricultural field classifying the non-crop species on the agricultural field, and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field; providing benefit index data for each non-crop species based on the specie of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field.

In a further aspect a computer element, in particular a computer program product or a computer readable medium, with instructions, which when executed on one or more computing node(s) is configured to carry out the steps of the method disclosed herein in any of the systems disclosed herein is presented.

In a further aspect the use of image data in a method for providing benefit index data as disclosed herein is presented.

In a further aspect of the present disclosure, a system for providing benefit index data is presented, wherein the system comprises: a first providing unit for providing image data of at least one section of an agricultural field; a second providing unit for providing field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field; a third providing unit for providing non-crop species data comprising data of different non-crop species; a fourth providing unit for providing benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field based on the non-crop species data and the crop species data; an analyzing unit for analyzing the provided image data of the at least one section of the agricultural field classifying the non-crop species on the agricultural field, and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field; a fifth providing unit for providing benefit index data for each non-crop species based on the benefit data of each species of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field.

Any disclosure and embodiments described herein relate to the methods, the apparatus, the system, 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 ..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 or receive”. “Initiating or causing to perform an action” includes any processing signal that triggers a computing device to perform the respective action.

The methods, apparatus, computer program elements/products disclosed herein provide an efficient, sustainable and robust way for providing benefit index data. By a providing of benefit index data the positive effects of non-crop plants on crop plants can be provided/identified such that these positive effects can be considered in the farming in order to increase the harvest of crop.

It is an object of the present invention to provide an efficient, sustainable and robust way of providing benefit index data. These and other objects, which become apparent upon reading the following description, are solved by the subject matter 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 represents any agricultural field 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 plant or a non-crop plant, wherein the non-crop plants may be 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. A section of the agricultural field is to be understood broadly in the present case and relates to at least one position or location on the agricultural field. The section may relate to a zone of the agricultural field including multiple positions or locations on the agricultural field forming a contiguous area of the agricultural field. The section may relate to distributed patches of the agricultural field multiple positions or locations on the agricultural field indicating a common field condition. The section may be flagged indicating the field condition of the section. The section may include one or more position(s) or location(s) on the agricultural field flagged with one or more flags indicating the field condition. The agricultural field may comprise one or more sections. The sections may be related to field data, in particular field conditions. The section may be flagged. The section 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 section.

The term data as used herein is to be understood broadly in the present case and refers to any kind of data and/or data arrangement. 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.

The term image data as used herein is to be understood broadly in the present case and represents any data or electromagnetic radiant imagery that may be obtained or generated by one camera, one image sensor, a plurality of cameras or a plurality of image sensors. Image data are not limited to the visible spectral range and to two dimensionalities. Thereby, also cameras obtaining image data in e.g. the infrared spectral range are included in the term image data. The frame rate of the camera may be in the range of 0.3 Hz to 48 Hz, but is not limited thereto.

The term field information data as used herein is to be understood broadly in the present case and represents any data configured to indicate information about the agricultural field. The field information data may be obtained from the argicultural unit and/or the agricultural device. Field information data may be crop species data, the location of the field, climate data, weather data, soil data and/or nutrient content, but is not limited thereto. Field information data may further comprise measuring data obtained by the argicultural device and/or agricultural unit. Measuring data may comprise data related to a field condition on the agricultural field and/or to an operation of the agricultural device amd/or agricultural unit. Field information data may comprise image data, spectral data, section data indicating flagged sections derived plant data, derived crop data, derived weed data, derived soil data, geographical data, trajectory data of the agricultural device and/or agricultural unit, measured environmental data (e.g. humidity, airflow, temperature, and sun radiation), and historic treatment data relating to the historic treatment operation. The field information data may be associated with a section such as location or position data of the section.

The term benefit index data as used herein is to be understood broadly in the present case and refers to an index configured to indicate the benefit of a non-crop plant with respect to a crop plant. The benefit index data are based on the benefit data of each species of non-crop and the number of individuals of the non-crop species on the at least one section of an agricultural field, but are not limited thereto.

The term total benefit index data as used herein is to be understood broadly in the present case and represents the sum of each benefit index data of each non-crop species multiplied with the number of individuals of each non-crop species. In other words, the total benefit data represents the sum of a plurality of benefit index data, wherein one of the plurality of benefit index data is respectively provided for one non-crop specie. The total benefit index data may be a single value, but is not limited thereto.

The term providing as used herein is to be understood broadly in the present case and represents any method for receiving, measuring, determining, generating, selecting, sending, or receiving of parameter or data. For instance, the at least one image data or field information data are provided by a sensor being arranged at the application device or at the application unit, by receiving data via the internet, cloud or radio of remote sensing methods, of global models, of mesoscale models, of micro-scale models, of short-time weather forecasts, of long-time weather forecasts, but is not limited thereto. Alternatively or additionally, parameter/ data can be provided or changed/adapted by a manual input by the user via a user interface. The term crop species data as used herein is to be understood broadly in the present case and relates to any data comprising information with respect to the crop species to be planted/grownon the at least one section of an agricultural field. The crop species data may include the name of the crop species, the scientific name of the crop species, and growth data for the crop species indicating e.g. the nutrient content in the soil and/or the moisture of the soil leading to an ideal, i.e. fast and fruitful, growth of the crop plant, but is not limited thereto. The crop species data may include the data of solely one crop species or the data of a plurality of crop species.

The term non-crop species data as used herein is to be understood broadly in the present case and represents any data comprising information with respect to the non-crop species identified on the agricultural field or with respect to all known non-crop species. The non- crop species data may include the name of the non-crop species, the scientific name of the non-crop species, and growth data for the non-crop species indicating e.g. the nutrient content in the soil and/or the moisture of the soil leading to an ideal, i.e. fast and fruitful, growth of the non-crop plant, but is not limited thereto. The non-crop species data may include the data of solely one non-crop species or the data of a plurality of non-crop species.

The term benefit data as used herein is to be understood broadly in the present case and refers to any data comprising information of how the non-crop species have an positive/beneficial effect with respect to the crop species to be grown on the at least one section of the agricultural field. The benefit data may be a list of values or descriptions, but is not limited thereto. The benefit data are based on the non-crop species data and the crop species data, but are not limited thereto.

The term analyzing as used herein is to be understood broadly in the present case and refers to any method for analyzing data. For instance, analyzing comprises the sub steps classifying and/or identifying, but is not limited thereto. Classification may be provided by any classification method or procedure and the identificaction may be provided by any identification method, procedure or machine learning algorithms, but are not limited thereto. The term number of non-crop species as used herein is to be understood broadly in the present case and refers to the number of different non-crop species. The number of noncrop species may be presented/provided as a value, but is not limited thereto.

The term number of individuals as used herein is to be understood broadly in the present case and refers to the number of the plurality of individuals of the each different non-crop species or crop species. The number of individuals may be presented/provided as a value, but is not limited thereto.

The term biodiversity index as used herein is to be understood broadly in the present case and refers to an index presenting the diversity of different species in a biotic community and/or agricultural field. Generally, the biodiversity index is high when there are many different species in a community and/or agricultural field and is low when there are only few. The biodiversity index may be presented/provided as a value, a 2- dimensional map, and a 3-dimensional map, but is not limited thereto. The biodiversity index is based on the number of non-crop species on the at least one section of the agricultural field, but is not limited thereto.

The term disturbance crop data as used herein is to be understood broadly in the present case and refers to any data comprising information about which non-crop species leads to disturbance effects with respect to the crop species. These non-crop species/data are identified when the non-crop species has a benefit index below a predefined benefit threshold index. The term predefined benefit threshold index as used herein is to be understood broadly in the present case and refers to a threshold, which may be preset by a user or may be automatically provided by another simulation, but is not limited thereto. The predefined benefit threshold index may be a value, but is not limited thereto.

The term proportion data as used herein is to be understood broadly in the present case and refers to any data comprising information of the proportion of the number of individuals of non-crop species with respect to the number of individuals of the crop species. The proportion data may be represented as percentage, but are not limited thereto. The term treatment data as used herein is to be understood broadly in the present case and refers to any data comprising information with respect to a treatment type, e.g. spot spraying, a treatment time, e.g. only in the morning, a treatment location, e.g. before or after the crop plant or non-crop plant on the soil, a treatment value, e.g. 20 liters, and or at least one product for treating the non-crop species. The treatment data are provided based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data, but are not limited thereto. The treatment data may be represented as instructions or as a value, but is not limited thereto.

The term recommendation crop data as used herein is to be understood broadly in the present case and refers to any data comprising information/proposal/suggestion which crop species will optimal/ideal grow on the agricultural field taking into account the exisiting non-crop species on the at least one section of the agricultural field. Additionally, the recommendation crop data may comprise a seeding time of the suggested crop species and/or a seeding location, but is not limited thereto. The recommendation crop data may be based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data, but are not limited thereto.

The term control data as used herein is to be understood broadly in the present case and relates to any data configured to operate and control the treatment of an agricultural field, an argicultural device and/or an argicultural unit. The control data are provided by a control unit and may be configured to control one or more technical means of the argicultural device, e.g. the drive control of the argicultural device, and to control the application of crop protecting products but is not limited thereto. The control data are based on the benefit index data, the total benefit index data, the biodiversity index, the treatment data, the proportion data and/or the recommendation data, but are not limited thereto.

The term treatment as used herein is to be understood broadly in the present case and represents any treatment for the cultivation of plants. The term treating or treatment is to be understood broadly in the present case and relates to any treatments of the agricultural field such as seeding, applying products, harvesting etc. The term agricultural device as used herein is to be understood broadly in the present case and comprises any device being configured to apply an agricultural application product onto the soil of an agricultural field and/or onto the plants on the agricultural field. The argicultural device may be configured to traverse the agricultural field. The argicultural device may be a ground or an air vehicle, e.g. a tractor-mounted vehicle, a self-propelled sprayer, a rail vehicle, a robot, an aircraft, an unmanned aerial vehicle (UAV), a drone, or the like. The argicultural device may be equipped with one or more argicultural unit(s).

The term agricultural unit as used herein is to be understood broadly in the present case and comprises any device configured to apply an agricultural application product onto the soil of an agricultural field, a crop plant and/or a non-crop plant. The argicultural unit may be an elastic arm, a robotic arm, in particular a single- or multi-articulated robot arm, or a stiff arm at which at least one outlet, respectively nozzle, of the crop protection product is arranged, but is not limited thereto. The outlet of the agricultural application product may be a spot spray equipment or broad band spray equipment. The argicultural unit may be arranged on the application device. In case of an application of a plurality of different agricultural application products, the argicultural unit may comprise a plurality of different tanks and different outlets for each different agricultural application product, wherein each of the different tanks and the different outlets can be arranged e.g. on a separate arm. The argicultural unit may be a spot sprayer or a broad band sprayer, but is not limited thereto.

In an embodiment, the method further comprises the step of: providing total benefit index data for the at least one section of the agricultural field by determining the sum of each benefit index data of each non-crop species multiplied with the number of individuals of each non-crop species on the at least one section of an agricultural field.

In a further embodiment, the method further comprises the step of: providing a biodiversity index based on the number of non-crop species on the at least one section of the agricultural field. By providing a biodiversity index, the biodiversity of the at least one section of the agricultural field can be estimated, compared to other agricultural fields and/or used for achieve costumer requirements.

In a further embodiment, the method further comprises the step of: providing disturbance crop data by identifying each non-crop species having benefit index below a predefined benefit threshold index. By providing the disturbance crop data, non-crop plants leading to a decreased growth of the crop/negative effect on the crop can be reliable identified.

In a further embodiment, the method further comprises the steps of: analysing the provided image data of the at least one section of the agricultural field identifying the crop specie on the agricultural field and a number of individuals of the crop specie on the at least one section of an agricultural field; and providing proportion data comprising the proportion of crop plants to non-crop plants based on the provided number of individuals of the crop specie and the provided number of individuals of the crop species. By providing proportion data a fast overview if a treatment is necessary can be provided to a user/farmer.

In a further embodiment, the method further comprises the step of: providing treatment data indicating a treatment type, a treatment time, a treatment location, a treatment value and/or at least one product for treating the non-crop species included in the disturbance crop data based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data. By providing treatment data specific information what, when, how, and/or where a treatment is necessary can be provided automatically and computer based, such that the expertise of the user/farmer is no longer necessary for treating an agricultural field. Hence, also unexperienced user/farmer can successful treat an agricultural field.

In a further embodiment, the method according to any one of the preceding claims, further comprising the step of: providing recommendation crop data recommendation at least one crop species which will optimal/ideal grow on the agricultural field based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data. By providing recommendation crop data specific information what, when and/or where a planting of crop species on the agricultural field is necessary in order to reach an optimal grow can be provided automatically and computer based, such that the expertise of the user/farmer is no longer necessary for planting crop plants onto an agricultural field. Hence, also unexperienced user/farmer can successful plant crop species successfully onto an agricultural field.

In a further embodiment, the field information data of the at least one section of an agricultural field further comprises the location of the at least one section of the field and/or the environmental conditions.

In a further embodiment, the method further comprises: providing control data for controlling a treatment, an agricultural unit and/or an agricultural device based on the benefit index data, the total benefit index data, the biodiversity index, the treatment data, the proportion data and/or the recommendation data. By providing control data the automatisation of the treatment of crop, non-crop and/or agricultural field can be increased.

In a further embodiment, the benefit data of the non-crop species includes a competitiveness of the non-crop species with respect to the at least one crop specie at the at least one section of the agricultural field and at given environmental conditions, the location of the non-crop species with respect to the at least one crop specie, a list of rare/endangered non-crop species, the number of seeds/offspring per plant of the non- crop species, the size of the plant of the non-crop species with respect to the size of the plant of the at least one crop specie, and/or the impact on insects. By using one, a plurality or all of the above mentioned parameter, the benefit of each non-crop species with respect to a crop plant can be accurate presented, identified and described.

In a further embodiment, the providing of benefit index data of non-crop species comprises a matching of the non-crop species with a preprovided/preset list of benefits. By matching the non-crop species with a preprovided list of benefits the providing time/calculation time of the benefit index data can be significantly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

Fig. 3 illustrate an example embodiment of a distributed computing environment;

Fig. 4 illustrates a flow diagram of an example method for providing benefit index data;

Fig. 5 illustrates an example embodiment for providing image data and or field information data;

Fig. 6 illustrates another example embodiment for providing image data and or field information 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 100 comprising a central computing node 101 (filled circle in the middle) and several peripheral computing nodes 101.1 to 101.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 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 101.1 to 101. n may be connected to one central computing system (or server). In another example, the peripheral computing nodes 101.1 to 101.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 101. n has been expanded to provide an overview of the components present in the peripheral computing node. The central computing node 101 may comprise the same components as described in relation to the peripheral computing node 101.n.

Each computing node 101 , 101.1 to 101.n may include at least one hardware processor 102 and memory 104. 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 104 may refer to a physical system memory, which may be volatile, nonvolatile, 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 computer-executable 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 101 , 101.1...101.n may include multiple structures 106 often referred to as an “executable component, executable instructions, computer-executable instructions or instructions”. For instance, memory 104 of the computing nodes 101 , 101.1... 101. n may be illustrated as including executable component 106. 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 101 , 101.1 ...101. n, whether such an executable component exists in the heap of a computing node 101 , 101.1... 101. 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 101 , 101.1... 101. n (e.g., by a processor thread), the computing node 101 , 101.1 ... 101 n 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 102 of each computing node 101 , 101.1...101.n may direct the operation of each computing node 101 , 101.1... 101. n in response to having executed computerexecutable 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 104 of each computing node 101 , 101 .1 ... 101 n. Computerexecutable instructions comprise, for example, instructions and data which, when executed at a processor 101 , cause a general purpose computing node 101 ,

101.1... 101. n, special purpose computing node 101 , 101.1... 101. 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 101 ,

101.1... 101. 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 101 , 101 .1 ... 101 .n may contain communication channels 108 that allow each computing node 101.1...101.n to communicate with the central computing node 101 , for example, a network (depicted as solid line between peripheral computing nodes and the central computing node in Figure 1 a). A “network” may be defined as one or more data links that enable the transport of electronic data between computing nodes 101 , 101 .1 ... 101 .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 101 , 101.1... 101. n, the computing node 101 , 101.1... 101. 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 computerexecutable instructions or data structures and which can be accessed by a general- purpose or special-purpose computing nodes 101 , 101 .1 ... 101 n. Combinations of the above may also be included within the scope of computer-readable media.

The computing node(s) 101 , 101.1 to 101. n may further comprise a user interface system 110 for use in interfacing with a user. The user interface system 110 may include output mechanisms 110A as well as input mechanisms 110B. The principles described herein are not limited to the precise output mechanisms 110A or input mechanisms 110B as such will depend on the nature of the device. However, output mechanisms 110A might include, for instance, displays, speakers, displays, tactile output, holograms and so forth. Examples of input mechanisms 110B 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 100’ with several computing nodes 101.T to 101.n’ denoted as filled circles. In contrast to the centralized computing environment 100 illustrated in Figure 1 , the computing nodes 101.1’ to 101.n’ of the decentralized computing environment are not connected to a central computing node 101 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 101.1’...101.n’ (local or remote computing system) and data may be distributed among various computing nodes 101 .T... 101. 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 101’ has been expanded to provide an overview of the components present in the computing node 101 ’. In this example, the computing node 101’ comprises the same components as described in relation to Figure 1 .

Figure 3 illustrates an example embodiment of a distributed computing environment 103. 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 103 may contain the following computing resources: mobile device(s) 114, applications 116, databases 118, data storage 120 and server(s) 122. The cloud computing environment 103 may be deployed as public cloud 124, private cloud 126 or hybrid cloud 128. A private cloud 124 may be owned by an organization and only the members of the organization with proper access can use the private cloud 126, rendering the data in the private cloud at least confidential. In contrast, data stored in a public cloud 126 may be open to anyone over the internet. The hybrid cloud 128 may be a combination of both private and public clouds 124, 126 and may allow to keep some of the data confidential while other data may be publicly available.

Fig. 4 illustrates a flow diagram of an example method for providing benefit index data. The computer-implemented method for providing benefit index data, comprises the following steps. In a first step image data of at least one section of an agricultural field are provided by a camera being arranged on the agricultural device. In a second step field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field are provided. In a third step non-crop species data comprising data of different non-crop species are provided. In a fourth step, benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field are provided based on the non-crop species data and the crop species data. In a fifth step, the provided image data of the at least one section of the agricultural field are analyzed for classifying the non-crop species on the agricultural field and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field. In a sixth step, benefit index data for each non-crop species are provided based on the benefit data of each species of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field.

Optionally, total benefit index data for the at least one section of the agricultural field are provided. The total benefit index data are determiney by the sum of each benefit index data of each non-crop species multiplied with the number of individuals of each non-crop species on the at least one section of an agricultural field.

Optionally, a biodiversity index is provided based on the number of non-crop species on the at least one section of the agricultural field.

Optionally, disturbance crop data are provided by identifying each non-crop species having benefit index below a predefined benefit threshold index.

Optionally, the provided image data of the at least one section of the agricultural field are analyzed for identifying the crop specie on the agricultural field and a number of individuals of the crop specie on the at least one section of an agricultural field. Further, proportion data comprising the proportion of crop plants to non-crop plants are provided based on the provided number of individuals of the crop specie and the provided number of individuals of the crop species.

Optionally, treatment data indicating a treatment type, a treatment time, a treatment location, a treatment value and/or at least one product for treating the non-crop species included in the disturbance crop data are provided based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data. Optionally, recommendation crop data are provided recommendation at least one crop species which will optimal grow on the agricultural field based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data.

Optionally, control data are provided for controlling a treatment, an agricultural unit and/or an agricultural device based on the benefit index data, the total benefit index data, the biodiversity index, the treatment data, the proportion data and/or the recommendation data.

Fig. 5 illustrates an example embodiment for providing image data and/or field information data.

The providing of image data and/or field information data is provided by a plurality of drones 102, 104, 106 each comprising a camera for providing image data of plants 113 onto an agricultural field 112 and/or field information data. The drones 102, 104, 106 are configure to transmit the provided image data and/or field information data to a computer system 110, to the cloud 100 and/or to a communication device 108. The drones 102, 104, 106 are able to transmit provided image data and/or field information data to each other. The transmission is a wireless data transmission.

Fig. 6 illustrates another example embodiment for providing image data and/or field information data.

The providing of image data and/or field information data is provided by a plurality of cameras 107i being arranged at a boom of an agricultural application device 107. The agricultural application device 107 comprises a plurality of tanks 107c, 107d, 107e each comprising a different crop protection product. The crop protection products are provided via nozzles 107b onto the plants, i.e. weed or crop plants, 107j onto an agricultural field 112. 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 “a” 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

Claims A computer-implemented method for providing benefit index data, the method comprising the steps of: providing image data of at least one section of an agricultural field; providing field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field; providing non-crop species data comprising data of different non-crop species; providing benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field based on the non-crop species data and the crop species data; analyzing the provided image data of the at least one section of the agricultural field for classifying the non-crop species on the agricultural field, and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field; providing benefit index data for each non-crop species based on the benefit data of each species of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field. The method according to claim 1 , further comprising the step of: providing total benefit index data for the at least one section of the agricultural field by determining the sum of each benefit index data of each non-crop species multiplied with the number of individuals of each non-crop species on the at least one section of an agricultural field. The method according to any one of the preceding claims, further comprising the step of: providing a biodiversity index based on the number of non-crop species on the at least one section of the agricultural field. The method according to any one of the preceding claims, further comprising the step of: providing disturbance crop data by identifying each non-crop species having benefit index below a predefined benefit threshold index. The method according to any one of the preceding claims, further comprising the steps of: analysing the provided image data of the at least one section of the agricultural field identifying the crop specie on the agricultural field and a number of individuals of the crop specie on the at least one section of an agricultural field; providing proportion data comprising the proportion of crop plants to non-crop plants based on the provided number of individuals of the crop specie and the provided number of individuals of the crop species. The method according to any one of the preceding claims, further comprising the step of: providing treatment data indicating a treatment type, a treatment time, a treatment location, a treatment value and/or at least one product for treating the non-crop species included in the disturbance crop data based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data. The method according to any one of the preceding claims, further comprising the step of: providing recommendation crop data recommendation at least one crop species which will optimal grow on the agricultural field based on the benefit index data, the total benefit index data, the biodiversity index, the proportion data and/or the disturbance crop data. The method according to any one of the preceding claims, wherein the field information data of the at least one section of an agricultural field further comprises the location of the at least one section of the field and/or the environmental conditions. The method according to any one of the previous claims, further comprising: providing control data for controlling a treatment, an agricultural unit and/or an agricultural device based on the benefit index data, the total benefit index data, the biodiversity index, the treatment data, the proportion data and/or the recommendation data. The method according to any one of the previous claims, wherein the benefit data of the non-crop species includes a competitiveness of the non-crop species with respect to the at least one crop specie at the at least one section of the agricultural field and at given environmental conditions, the location of the non-crop species with respect to the at least one crop specie, a list of rare/endangered non-crop species, the number of seeds/offspring per plant of the non-crop species, the size of the plant of the non-crop species with respect to the size of the plant of the at least one crop specie, and/or the impact on insects. The method according to any one of the previous claims, wherein the providing of benefit index data of non-crop species comprises a matching of the non-crop species with a preprovided list of benefits. An apparatus for providing benefit index data, 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 image data of at least one section of an agricultural field; providing field information data of the at least one section of an agricultural field comprising crop species data indicating at least one crop specie to be grown on the at least one section of an agricultural field; providing non-crop species data comprising data of different non-crop species; providing benefit data of non-crop species with respect to the crop species to be grown on the at least one section of the agricultural field based on the non-crop species data and the crop species data; analyzing the provided image data of the at least one section of the agricultural field classifying the non-crop species on the agricultural field, and for providing a number of non-crop species on the at least one section of an agricultural field and a number of individuals of each non-crop species on the at least one section of an agricultural field; providing benefit index data for each non-crop species based on the specie of non-crop and the number of individuals of the non-crop specie on the at least one section of an agricultural field. A computer program element with instructions, which when executed on one or more computing node(s) is configured to carry out the steps of the method of any one of the claims 1 to 11 or by the apparatus of claim 12. Use of image data in a method for providing benefit index data according to claims 1 to 11.