Method for providing variable application rate data for application sub-areas of a field for agricultural products based on measurements of the green area index (GAI)

Abstract

Computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field (120), comprising: providing green area index data for at least one measurement sub-area, wherein the green area index data are provided by at least one detection system (14), wherein the at least one detection system (14) is configured to detect a green leaf area of a crop canopy in at least one measurement sub-area of an agricultural field (120) while an application device is moving through the agricultural field (120); providing a variable application rate model (15) configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data of the at least one measurement sub-area; providing variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data by utilizing the variable application rate model (15).

Description

[0001] METHOD FOR PROVIDING VARIABLE APPLICATION RATE DATA FOR APPLICATION SUB-AREAS OF A FIELD FOR AGRICULTURAL PRODUCTS BASED ON MEASUREMENTS OF THE GREEN AREA INDEX (GAI)

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to a computer implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, an apparatus for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, and an application device for applying an agricultural product on an application sub-areas of an agricultural field.

[0004] TECHNICAL BACKGROUND

[0005] The general background of the present disclosure is the treatment of a plantation in an agricultural field.

[0006] Agricultural machines get equipped with more and more sensors. Crop protection decisions may be executed by detecting in a plantation, particularly crop plants, weeds, insects and / or pathogens in real time.

[0007] In practice farmers apply in the majority of cases still one selected rate of a fertilizer, fungicide or plant growth regulator on a crop. More advanced farmers use variable rate application systems (VRA) to react to the heterogeneity of the crop density and crop development in a field based on biomass mass maps derived from various sensor systems to measure vegetation indices. Common vegetation indices may be the normalized density vegetation index, NDVI, or the Leaf Area Index, LAI, etc. These sensors can be satellite, drone or tractor sprayer mounted. However, in this case the dose is adapted for the whole sprayer or spreader width based on the values read by the GNSS / GPS system from the application maps, which is mostly mounted on the tractor in the middle of the sprayer and / or spreader boom. In other words, in conventional agricultural practice, variable application rates of the products (VRA) are adjusted on the basis of application maps derived from heterogeneity maps (e.g., biomass, soil type, historic yield maps etc.). The rates are read from these maps on the position of the GNSS / GPS receiver of the application device or tractor in the maps, which means that the application rate of the agricultural and / or treatment products is the same for each application sub-devices (e.g., a nozzle) across the boom.

[0008] However, since the application device fitting extends (e.g., nozzles) also far away respectively spaced apart from the GNSS / GPS-receiver, e.g., when a sprayer boom extends in the range of 6 to 54 meters, the optimum treatment product / application rate of a treatment product differs over the whole width of the application system due to the inhomogeneity of crop canopy densities. This means that in conventional agricultural VRA practice a non-optimal treatment / application rate of treatment products on an agricultural field is provided on sub-areas of the agricultural field.

[0009] Therefore, especially in case of large equipment that for example may have a width of 24, 36, 42 or 54 meter for spraying or spreading, this is not a very detailed adaptation of the application rate to the crop heterogeneity covered across the applicator width.

[0010] Therefore, there is a need to provide a method by which more localized variable application rates for smaller sub-areas can be provided.

[0011] SUMMARY OF THE INVENTION

[0012] The present invention relates to a computer implemented method, an apparatus, and an application device for applying an agricultural product on an application sub-area of an agricultural field according to the independent claims, which become apparent upon reading the following description. The dependent claims refer to further embodiments of the invention.

[0013] In one aspect of the present disclosure, a computer-implemented method for providing variable application rate data for applying an agricultural product on an application subarea of an agricultural field is disclosed, comprising: providing green area index data for at least one measurement sub-area, wherein the green area index data are provided by at least one detection system, wherein the at least one detection system is configured to detect a green leaf and / or stem area of a crop canopy in at least one measurement sub-area of an agricultural field while an application device is moving through the agricultural field; providing a variable application rate model configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data of the at least one measurement sub-area; providing variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data by utilizing the variable application rate model.

[0014] In one aspect of the present disclosure, a computer-implemented method for providing variable application rate data for applying an agricultural product on an application subarea of an agricultural field is disclosed, comprising: providing green area index data for at least one measurement sub-area, wherein the green area index data are provided by at least one detection system, wherein the at least one detection system is configured to detect a green leaf and / or stem area of a crop canopy in at least two subsequent, in particular 3-4 measurement sub-areas in a kind of sequential measurement cycles while an application device is moving through the agricultural field; providing a variable application rate model configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on averaged green area index data of the at least two subsequent, in particular 3-4 measurement sub-areas; providing variable application rate data for applying the agricultural product on the application sub-area at least based on averaged green area index data of two subsequent, in particular 3-4 sub-areas by utilizing in particular a product specific variable application rate model.

[0015] Alternatively, in one aspect of the present disclosure, a computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field is disclosed, comprising: detecting green leaf area data in a measurement sub-area by at least one detection system while an application device is moving through the agricultural field; generating green area index data for the measurement sub-area from the green leaf and / or stem area data by the detection system; providing a variable application rate model configured to generate variable application rate data for applying the agricultural product on the application sub-area based on the generated and averaged green area index data from at least two subsequent, in particular 3-4 measurement sub-areas; utilizing the variable application rate model for generating variable application rate data; and providing the variable application rate data for applying the agricultural product on the application sub-areas.

[0016] A further aspect of the present disclosure relates to an apparatus for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the apparatus comprising: a computing nodes; and a computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the computing nodes, cause the apparatus to perform the following steps: providing averaged green area index data for at least at least two subsequent, in particular 3-4 measurement sub-areas, wherein the green area index data are provided by at least one detection system, wherein the at least one detection system is configured to detect and to average the green leaf and / or stem area of a crop canopy in at least two subsequent, in particular 3-4 measurement sub-areas of an agricultural field while an application device is moving through the agricultural field; providing a variable application rate model configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on averaged green area index data of the at least two subsequent, in particular 3-4 measurement sub-area; providing variable application rate data for applying the agricultural product on the application sub-area at least based on the averaged green area index data by utilizing the variable application rate model. A further aspect of the present disclosure relates to an application device for applying an agricultural product on a sub-area of an agricultural field, wherein the variable application rate data are provided according to a computer-implemented method for providing variable application rate data for applying an agricultural product on an application subarea of an agricultural field as disclosed herein.

[0017] Another 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 computer-implemented method for a computer- implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, in particular in a respective apparatus and / or system.

[0018] A further aspect of the present disclosure relates to a system for providing variable application rate data for applying an agricultural product on application sub-areas of an agricultural field, comprising: a green area index (GAI) data providing unit configured to provide averaged green area index data for at least two subsequent, in particular 3-4 measurement sub-areas wherein the green area index data are provided by at least one detection system, wherein the at least one detection system is configured to detect a green leaf area of a crop canopy in at least two subsequent, in particular 3-4 measurement sub-areas of an agricultural field while an application device is moving through the agricultural field; a variable application rate model providing unit configured to provide a variable application rate model configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on averaged green area index data of the at least two subsequent, in particular 3-4 measurement sub-areas; a variable application rate data providing unit configured to provide variable application rate data for applying the agricultural product on the application sub-areas at least based on green area index data by utilizing the variable application rate model.

[0019] A further aspect of the present disclosure relates to a use of green area index (GAI) data, one or more product specific variable application rate models, one or more lookup tables, one or more detection systems, RGB-images and / or Near Infrared Images and / or Red Edge Images and the related binary images in a computer-implemented method, a system and / or an apparatus for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field. RGB-images or red green blue-images may be images which substantially are based on the red-green-blue- color space or a RGBNIR combination.

[0020] A further aspect of the present disclosure relates to a use of control data for controlling at least one application sub-devices of an application device at least based on the provided variable application rate data according to a method disclosed herein.

[0021] Any disclosure and embodiments described herein relates to the methods, the system, the apparatus, the device or the computer program product lined out above. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa.

[0022] 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”.

[0023] The method, device, apparatus, system and / or computer program element, disclosed herein provide a method by which good variable application rates may be determined for sub-areas of an agricultural field, which may also be adapted to varying crop development and crop densities along the width of the sprayer boom and across a field while the sprayer is moving forward. In this context, the term good respectively optimal represents that the variable application rates of an agricultural product are selected or chosen in such a manner that a desired agricultural target, e.g., an improved growth of the crop plant or an increased harvest etc., is reached.

[0024] In particular, the present disclosure may allow an automation of determining and / or providing an optimized respectively good application rate on-the-go during spraying on a high resolution level or hyperlocal level. In an example high resolution level may be reached if for each single nozzle of a sprayer and / or for a predetermined and / or chosen section of a spray boom of a sprayer the application rate is set and / or adapted to e.g. the canopy density individually. In an example this means that the application rate, e.g., variable mode, may be adapted across a sprayer boom on a nozzle or section level while driving forward when spraying. In other words, application rates may be determined in an objective manner on a detailed sub-area level, virtually for every square meter of an agricultural field. Thus, the present disclosure may provide an economically optimal intrafield application rate adjustment as reaction to inhomogeneous crop canopies. It may allow the optimal respectively efficient crop performance with the lowest possible input of an agricultural product, e.g., a plant growth regulator, fungicide product and / or liquid nitrogen. This may allow the use of a more optimal doses for an agricultural product avoiding unnecessary treating and / or over treatment of the agricultural field and saving amounts of agricultural products while at the same time maintaining the required efficacy, but also reducing stress to the crop at the same time.

[0025] Further, the presented invention will allow a rate adaptation on every nozzle position based on the measured green area index (GAI), in particular GAI-crop only, while moving across an agricultural field. Due to algorithms the green leaf surface area of weeds can be excluded from the green area index (GAI).

[0026] Furthermore, the present invention provides a method by which optimized variable application rates can be determined for sub-areas of an agricultural field, which may also be adapted to varying crop canopy development across a field while the sprayer is moving forward. This may allow the use of a more optimal dose of the agricultural product on the level of single nozzles or boom sections avoiding unnecessary treating and / or over treatment of the agricultural field, and saving amounts of agricultural products while maintaining the required efficacy.

[0027] The term “agricultural product” as used herein is to be understood broadly in the present case and comprises any product / object / material which may be applied on an agricultural field. In the context of the present disclosure, the term “agricultural product” may comprise: chemical products such as fungicides (e.g., of the chemical classes of the triazoles, the carboxamides, methoxyacrylates etc.), plant growth regulators (e.g., of the chemical classes of the triazoles, gibberilin inhibitors, quarternay ammonium salts etc.), insecticides (e.g., of the chemical class of the pyrethroides, juvenile hormone analoges etc.), herbicides, acaricides, molluscicides, nematicides, avicides, rodenticides, repellants, bactericides, biocides, safeners, urease inhibitors, nitrification inhibitors, denitrification inhibitors, foliar applied fertilizers (particular ammonia and / or nitrate based), other nutrients (e.g., micronutrients), seeds, seedlings, water or any combination thereof. Further biological products such as microorganisms useful as fungicide (e.g., Bacillus subtilis, Bacillus amyloliquefacients), herbicide (bioherbicide), insecticide (bioinsecticide e.g., Bacillus thuringiensis), or plant growth promoting bacteria (e.g., Bacillus amyloliquefacients, Bacillus pumilus etc.) acaricide (bioacaricide), molluscicide (biomolluscicide), nematicide (bionematicide- e.g., Bacillus firmus), avicide, piscicide, rodenticide, repellant, bactericide, biocide, safener, total herbicide / burndown herbicide (e.g., pelargonic acid), defoliant, desiccant, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof.

[0028] 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 by e.g., seeding, planting and / or fertilizing. The agricultural field may be any plant or crop cultivation area, such as a farming field, a greenhouse, a plantation or the like. 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.

[0029] The term “substantially corresponds” as used herein is to be understood broadly in the present case and represents that the at least one measurement sub-area and the application sub-area, in particular the spatially dimension of the at least one measurement sub-are and the application sub-area, correlates with a value of 0.7 to 1.0, in particular 0.9 to 1.0.

[0030] The term “green area index” (GAI) as used herein is to be generally understood as the ratio of green leaf and stem area to the area of ground on which the crop is growing. In other words, the green area index is the ratio of green leaf and / or stem area (GLA) to the area of ground on which the crop is growing. The green area index (GAI) is provided as green area index (GAI) data. The green area index (GAI) data may be provided, for example, by analyzing images of a camera of the detection system with respect to their green leaf and / or stem area (GLA). In this case, i.e. , the providing of the green area index (GAI) data by analyzing images, the providing, in particular determination, of the green area index (GAI), in particular green area index (GAI) data, may comprise the following steps:

[0031] In a first step, red green blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images are used by image analysis algorithms using the binary conversion in order to output the binary images respectively the value of the green leaf and / or stem area (GLA) for a selectable area of the images. A detailed description of the conversion of the red green blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images to the binary images is described below.

[0032] In a second step, green area index (GAI) data measured by experiments are provided. These experiments have been carried out to determine the real three-dimensional leaf and stem surface of a crop with respect to a soil surface unit, e.g., one square meter. The real leaf surface of a crop is measured by a destructive method. In the destructive method the leaves and stems of a crop are harvested and scanned per soil surface unit, e.g., a square meter, by a flat scanner. By scanning all leaves and stems of the crop, the real leaf and stem surface of a crop can be measured, determined or estimated. The measured real leaf surface of a crop represents a measured green leaf area (GLA), such that the ratio of the measured green leaf area (GLA) to the area of ground on which the crop is growing represents respectively equals the green area index (GAI) data measured by experiments.

[0033] In a third step, the green leaf and stem area (GLA) derived from the binary images are converted into green area index (GAI) data by means of a respective conversion model and / or conversion algorithm. The conversion model and / or conversion algorithm may be provided by plotting the green leaf area (GLA) derived from the binary images versus the green area index (GAI) data measured by experiments in a diagram. Figure 7 shows such an example diagram in which the green leaf and stem area (GLA) from red-green-blue (RGB) images is plotted versus the green area index (GAI) data measured by experiments of a crop of winter oilseed rape. Corresponding experiments may have been performed also for other crop species such as winter wheat. Such an approach may have the advantage that the green leaf area (GLA) values of the binary images may be instantly provided from an image and converted by the above described calibrated algorithm to green area index data, so that this approach may also be used while driving over the agricultural field, since this approach is sufficiently fast. As can be taken from Figure 7 the plotted green leaf area (GLA) from red-green-blue (RGB) images versus the green area index (GAI) data measured by experiments may form a regression line.

[0034] By comparing the real leaf surface of a crop measured for all leaf layers, i.e. , the green leaf area (GLA) measured by experiments and the green leaf area (GLA) value derived from the binary images, the accuracy of the image analysis algorithms can be estimated respectively determined.

[0035] Additionally, beside the leaf surfaces of the crop, also the green stems of the crop may be included in the method for providing of the green area index (GAI) data by analyzing images as mentioned above to get a higher accuracy concerning the size of the photosynthetic parts of the crop.

[0036] The term “detection system” as used herein is to be understood broadly in the present case and refers to any system configured to capture and / or determine a unit corresponding to the green leaf area (GLA) and / or determining a green area index (GAI), i.e., the detection system is configured to detect and in particular average green area index data and / or the green leaf area at least in the measurement sub-area of the agricultural field. The detection system may, for example, comprise at least one camera unit that takes a, i.e., one, snapshot respectively picture of the ground in front of the application device or the application sub-device at regular time intervals while the applications device is moving, in particular moving through the agricultural field. Alternatively, the detection system may, for example, comprise at least one camera unit that takes at least two sequential snapshots respectively pictures (e.g., 5 or 15 images) of the ground in front of the application device or the application sub-device at the regular time interval while the applications device is moving, in particular moving through the agricultural field. In this context, from each single image of the at least two snapshots, the green area index is determined respectively analyzed. The at least two determined green area indices of the at least two single images, respectively, will be averaged. Based on the green area index of the at least two snapshots of measurement sub-areas, a decision on the application rate is taken. The images can show overlapping areas of the ground or showing non-overlapping areas of the ground. In addition, within the context of the present disclosure, it is also possible that a video stream may be continuously analyzed. The present disclosure is not limited to a particular detection system. For example, camera units may be used that may be arranged near application sub-devices, e.g., the camera units may be mounted on a sprayer boom.

[0037] Moreover, camera units may be used arranged on separate drone units that fly in front of an application sub-devices / device and capture the ground or using a two stage system with drones analyzing in a first flight and spraying in a second flight. The detection system may also comprise one or more LED lightning / illumination units, wherein the LED lightning / illum ination units are particularly provided as a flash units being directed towards the ground. Such a setup is particularly advantageous since an image may be captured synchronized with a flash triggering. In this way, the ground or the plants may be illuminated sufficiently so that shadowing by plants or parts of plants may be avoided as far as possible. Shadowing, also by sunlight, is often problematic, since such shadows are often wrongly interpreted as green leaf area by image recognition software, e.g., in mobile phone or drone camera applications. Another advantage may be that the sprayer can operate with the camera sensors day and night due to the LED lightning / illumination. Further, the detection system may comprise a system for converting red green blueimages and / or the Near Infrared Images and / or the Red Edge Images into binary images as described below in further detail. Furthermore, the detection system may comprise a conversion model.

[0038] The term “conversion model” used herein is to be understood broadly in the present case and represents any model capable of converting green leaf area (GLA) into green area index (GAI) data for single images. The conversion model is included in the detection system. The conversion can be provided by using regression formula or another algorithm based on calibration data from specific field trials.

[0039] The term “application sub-area” is to be understood broadly in the present case and refers to the sub-area of the agricultural field on which the agricultural product is to be applied based on the variable application rate data. In this context, the application sub-area is the area, in particular sub-area, of the agricultural field on which currently, i.e., in this moment, the agricultural product is to be applied. Therefore, the application sub-area is fully or at least partially covered by or below the boom of the application sub-device. The application sub-area may substantially correspond to the at least one measurement sub-area. In this context, the application sub-area and the at least one measurement sub-area may fully or at least partially overlap. A length of the application sub-area is defined by the orientation and / or inclination of one or a plurality of nozzles and the spray diameter of one or a plurality of nozzles being arranged at the boom of the application sub-device, but is not limited thereto. The length of the application sub-area is measured parallel to the moving direction of the application device. For instance, the length of the application subarea may be between 1 .0 and 3.0 m, particularly between 1 .0 and 2.0 m, but is not limited thereto. A width of the application sub-area is defined by the orientation and / or inclination of one or a plurality of nozzles and the spray diameter of one or a plurality of nozzles being arranged at the boom of the application sub-device, but is not limited thereto. Therefore, the width of the application sub-area may be between the spray diameters of only one nozzle up to the sum of the spray diameter of a plurality of nozzles. For instance, the spray diameter may be between 0.25 m and 6.0 m, but is not limited thereto. The width of the application sub-area is measured perpendicular to the moving direction of the application device.

[0040] The term “measurement sub-area” is to be understood broadly in the present case and represents an area, in particular sub-area, on which the green leaf area is provided by or derived from at least one detection system, i.e. , a camera sensor. The measurement subarea may be defined respectively provided by the shooting range of the camera of the detection system. As already mentioned above, the detection system may, for example, comprise at least one camera unit that takes a, i.e., one, snapshot respectively picture of the ground in front of the application device or the application sub-device at regular time intervals while the applications device is moving, in particular moving through the agricultural field. In this case, the measurement sub-area is defined by the shooting range of the at least one camera taking solely the one snapshot. As also already mentioned above, alternatively, the detection system may, for example, comprise at least one camera unit that takes at least two snapshots respectively pictures (e.g., 5 or 15 images) of the ground in front of the application device or the application sub-device at the regular time interval while the applications device is moving, in particular moving through the agricultural field. In this case, from each single image of the at least two snapshots, the green area index is determined respectively analyzed. The at least two determined green area indices of the at least two single images, respectively, will be averaged. Based on the green area index of the one snapshot or the averaged green area index of the at least two snapshots, a decision on the used application rate is taken. In other words, when the measurement sub-areas are provided by a pre-defined sequence, i.e., a particular number of single images which is used as measurement cycle, e.g., 5 or 15 images, the determined respectively detected green leaf area (GLA) or the green area index (GAI) provided by the detection system is averaged. The averaged green leaf area (GLA) or the averaged green area index (GAI) are used to take a decision, if the application rate for applying the agricultural product on the application sub-area need to be changed or not. In other words, each of the images of the pre-selected / pre-defined measurement cycle, i.e., 5 or 15 images, is individually analyzed with respect to green leaf area (GLA) and / or green area index (GAI). These green leaf area (GLA) and / or green area index (GAI) data are averaged. The application rate is only adapted, if the result in the measurement cycle of a sequence of measurement sub-areas is below or above a preset green area index (GAI) thresholds being provided by the application rate model, which is product specific and was developed in field trials. Otherwise, the application or use rate in the following application sub-area remains the same. The actual / current measurement sub-area refers to the measurement sub-area which is currently measured, i.e., the at least one camera takes at least one image. The previous measurement sub-area refers to a measurement sub-area which have been already measured, i.e., the at least one camera has already taken at least one image. In this case, the measurement sub-area is defined by the shooting range of the at least one camera taking the at least two snapshots. The shooting range of the at least one camera of the detection system may be affected by the orientation and / or inclination of the at least one camera. Therefore, the length and / or width of the measurement sub-area are defined by the width of the application sub-device, the shooting range, the measuring rate of the one or more cameras, the sampling rate of the one or more cameras, the cycle duration of the measurements of the one or more cameras, but is not limited thereto. For instance, the measurement sub-area may have a width between 0.25 m and 6.0 m and a length between 1.0 and 3 m, particularly between 1.0 and 2.0 m, but is not limited thereto. The width is perpendicular to the moving direction of the application device. The length is parallel to the moving direction of the application device. Alternatively or additionally, the measurement sub-area may be a region of interest (ROI), i.e. , a section or part of the shooting range of one camera or a plurality of cameras. The term “Region of interest (ROI)” refers to an area in the camera image for which green leaf area (GLA) or green area index (GAI) is detected, measured and processed. The “region of interest” may be a rectangular area in the single image of the camera of the detection system, but is not limited thereto.

[0041] In each measurement cycle the camera is continuously switched on and off in order to sample the field. Dependent on the sample frequency or the angle of the camera, the measurement sub areas may overlap and / or substantially are seamlessly joint together. Application sub-areas may be former measurement sub-areas.

[0042] The term “variable application rate data” used herein is to be understood broadly in the present case and represents any data indicating, initiating or triggering an application rate of an agricultural product being variable with respect to whole width of an application subdevice of an application device. The term variable may also mean that an application rate is provided separately for each nozzle of the application sub-device being arranged at the application device. The variable application rate data comprises a high resolution, if the variable application rate data refers to data of a single nozzle of the application sub-device or a plurality of nozzles of the application sub-devices being arranged at a section of the boom of the application sub-device. In other words, the variable application rate data have a high resolution when only single nozzles of the application sub-devices or a plurality of nozzles of the application sub-device being arranged at a section of the boom of the application sub-device, wherein the section of the boom is smaller than the whole extend of the boom. The variable application rate data may include values for the amount and / or treating time of treatment products, in particular agricultural products, to be applied on the agricultural field having the unit second, liter, milliliter, kilogram, or gram.

[0043] The described green area index (GAI) driven high resolution VRA system can be used without loading a satellite or drone based application map. However, these could also be used to deliver a base rate which is than modified on a nozzle or section basis via the GAI-based algorithm. The variable application rate data may be provided by a variable application rate model.

[0044] The term “variable application rate model” used herein is to be understood broadly in the present case and represents any model being capable to provide and / or determine the variable application rate data. The variable application rate model may comprise at least one look-up table in which a relationship between Green Area Index (GAI) ranges or classes and the application rate for the agricultural product is provided respectively stored. Such lookup tables may be based on the results of large scale field trials, which may allow for specific crops, for certain growth stages and specific products to read out the best product application rate for certain green area indexes. For instance, the lookup table include application rate variation recommended for different products, wherein each product may be different depending on the product profiles. Exemplary, often selected VRA application or use rates for the plant growth regulating fungicide Caryx ® are 0, 50 and 100% rate of the standard label rate vs. for Folicur ® 50, 75 and 100% rate. GAI-classes are used to switch between application rates. For example: for the PGR- fungicide product Caryx ® in winter oil seed rape; GAI < 0,8 rate 0 (no application); GAI 0, 8-2,0 = 0,7 I Caryx / ha; GAI >2,0 = 1 ,4 I Caryx I ha. The rates can be pre-defined, preset and / or predetermined. The number of rates can be 2 to 10, but in practice 3 to 7 rates are the norm. In other words, the variable application rate depends directly on the value of the GAI, in particular pre-set, pre-determined GAI classes. The same process is also used for tank mixtures of products as fungicides and growth regulators etc. Alternatively, the variable application rate model may be a machine learning model for providing and / or determining the variable application rate data based on the Green Area Index (GAI) ranges or classes.

[0045] The term “base application rate data” used herein is to be understood broadly in the present case and represents any data indicating a 100 percent application rate of the agricultural product defined by the user. For instance, the 100 percent application rate may be a maximum application rate provided by the manufacturer of the agricultural product. The selected 100 percent application rate of the agricultural product can be reduced based on the comparison of the measured GAI values with the rate defining GAI classes. The base application rate may be determined based on historical trial data, satellite data other data sources providing a data for an agricultural field or the parts of an agricultural field which underlies an application by an application boom.

[0046] The term “conversion into binary images” used herein is to be understood broadly in the present case and represents a method or a process by which red-green-blue (RGB)images and / or the Near Infrared Images and / or the Red Edge Images and / or multi- spectral images are converted into binary images. This conversion may be provided by using the BW= im2bw() function of MATLAB®, or the sequences of cv2.cvtColor() and cv2.threshold(9) in Python®, but is not limited thereto. In this context, the conversion of the red-green-blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images may be converted into binary images by determining the Normalized Difference Vegetation Index, NDVI, and by setting threshold levels for the intensity level of the pixels. In other words, all pixels are replaced according to the specified luminance with either white (logical 1 ), if the pixel is equal or greater than the chosen luminance level, or black (logical 0), if lower. As a result, the binary image shows any green plant material in white and the background of the soil or dead plant material, having a same or almost same luminance of the soil, in black. Therefore, the binary image can be separated into white areas and black areas. Both the white areas and the black areas may be provided in the unit percent of all pixels of the binary image or in the unit percent of the soil surface depicted in the red-green-blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images. Alternatively, the white areas and the black areas may be provided in the unit square meters or square millimeters of the soil surface depicted in the red-green-blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images. In other words, the binary image presents the area ratio between green plant material and the background of the soil with respect to the soil surface depicted in the red-green-blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi- spectral images. The soil surface depicted in the red-green-blue (RGB)-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images may have the unit square meters or square millimeters and may be pre-defined and / or defined surface. Additionally, the conversion into binary images may include a distinguishing of crop plants and weed plants by algorithms based on the shape of the crop plants or weed plants and / or the position of the plants in rows. In this context, the identified respectively distinguished weed plants may be considered as to belong to the black area, such that only the green plant material of crop plants are white and the background of the soil, dead plant material and / or the green plant material of the weed plants are black. Alternatively, the area ratio of the distinguished weed plants and crop plants can be provided separately. The percent value, the square meter value or the square millimeter value indicates the green leaf area (GLA).

[0047] The term “application device” used herein is to be understood broadly in the present case and represents any device being configured to apply an agricultural product onto the soil of an agricultural field using respective application sub-devices. 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.

[0048] The term “application sub-devices” used herein is to be understood broadly in the present case and comprises any sub-devices configured to be controlled by respective control data when applying the agricultural product on the agricultural field. The application subdevice may be sprayer, in particular a smart sprayer, comprising a boom and a plurality of pulse width modulation (PWM) nozzles and / or electronically controlled multi nozzle heads or other VRA devices, for example Vortex nozzles, but is not limited thereto. In an example, the application sub-device is a smart sprayer comprising a boom and a plurality of spray nozzles mounted in a distance of 25 or 50 cm on a sprayer boom, e.g., most common in the US are 15, 20 or 30 inch spacing. Additionally or alternatively, the application sub-device may be a spreader with single nozzle or section-outlet control, but is not limited thereto.

[0049] In case the sprayer has a two or more tank system and is equipped with two or more spray lines on the boom, the camera systems and / or the detection system could be used at the same time to run (a) the spot spraying based on weed coverage thresholds with the first spray line and (b) the VRA spraying on the second spray line based GAI-driven rates, but is not limited thereto. 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 or application sub-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.

[0050] The term “machine learning algorithm” may comprise decision trees, naive bayes classifications, nearest neighbors, neural networks, convolutional or recurrent neural networks, generative adversarial networks, support vector machines, linear regression, logistic regression, random forest and / or gradient boosting algorithms. Particularly, the result of a machine learning algorithm is used to adjust the application rate decision logic. Particularly, the machine learning algorithm is organized to process an input having a high dimensionality into an output of a much lower dimensionality. Such a machine learning algorithm is termed “intelligent” because it is capable of being “trained.” The algorithm may be trained using records of training data. A record of training data comprises training input data and corresponding training output data. The training output data of a record of training data is the result that is expected to be produced by the machine learning algorithm when being given the training input data of the same record of training data as input. The deviation between this expected result and the actual result produced by the algorithm is observed and rated by means of a “loss function”. This loss function is used as a feedback for adjusting the parameters of the internal processing chain of the machine learning algorithm. For example, the parameters may be adjusted with the optimization goal of minimizing the values of the loss function that result when all training input data is fed into the machine learning algorithm and the outcome is compared with the corresponding training output data. The result of this training is that given a relatively small number of records of training data as “ground truth”, the machine learning algorithm is enabled to perform its job well for a number of records of input data that higher by many orders of magnitude.

[0051] 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 and / or shown to a user by a display, and / or received from other devices, queried from other devices, measured from other devices, calculated by other devices, determined by other devices and / or transmitted by other devices.

[0052] 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 and / or numerical values, a plurality of a numbers and / or numerical values, a plurality of a numbers and / or numerical values being arranged within a list, 2 dimensional maps or 3 dimensional maps, but are not limited thereto.

[0053] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the method comprises providing base application rate data representing a base application rate, wherein providing variable application rate data for applying the agricultural product comprises providing variable application rate data based on base application rate data and variable application rate data in the measurement sub-areas by utilizing the variable application rate model, in particular providing application rate data by adapting base application rate data based on the variable application rate data provided by the variable application rate model to obtain adapted application rate data.

[0054] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the variable application rate model is adapted to provide adapted variable application rate data for a respective application sub-area if the green area index data for the measurement sub-areas has a predetermined relation.

[0055] A predetermined relation may be seen as a particular pattern or tendency of the green area index of within the preceding number of measurement sub-areas. The predetermined relation may be a deviation from a predetermined value of the green area index, a predetermined strong change of the of the green area index or a predetermined deviation from a floating average of the green area index, but is not limited thereto. The predetermined relation may also be threshold of an average of the green area index in the preceding number of measurement sub-areas and / or a predetermined gradient of the green area index in the preceding number of measurement sub-areas. In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the predetermined relation is a predetermined gradient of the green area index in the measurement sub-areas.

[0056] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, providing coverage data comprises coverage data selectively for a plurality of sections of an application boom, providing green area index data comprises providing green area index data for measurement sub-areas for each of said sections of the application boom, wherein the detection system is adapted to the detect green area index in the measurement sub-areas for each of said sections of the application boom while an application device is moving through the agricultural field; wherein the variable application rate model is configured to provide variable application rate data for a respective application sub-area in each of said sections of the application boom based on the provided green area index data in the measurement sub-areas of the respective one of said sections of the application boom; wherein setting the variable application rate data for applying the agricultural product on a respective application sub-area in each of said sections of the application boom is based on the green area index data in the measurement sub-areas in the respective one of said sections of the application boom by utilizing the variable application rate model.

[0057] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the variable application rate model is configured to provide variable application rate data for a respective application sub-area in each of said sections of the application boom based on the provided green area index data in the measurement sub-areas of the respective one of said sections of the application boom and the provided green area index data in the measurement sub-areas of adjacent sections of the application boom; wherein setting the variable application rate data for applying the agricultural product on a respective application sub-area in each of said sections of the application boom is based on the green area index data in the measurement sub-areas in the respective one of said sections of the application boom and green area index data in the measurement subareas in the adjacent sections of the application boom by utilizing the variable application rate model.

[0058] Adjacent sections of an application boom may be considered as those sections which lay in the beside the section in question. When considering the green area index between a section and an adjacent section thereto, a relation is determined between the green area index in one section and a green area index in the adjacent section, i.e., the section beside the one section. This may apply also to both sides. Upon the relative green area index or extension of the relative green area index over a predetermined threshold, the application variable application rate may be adapted.

[0059] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one measurement sub-area substantially corresponds to the application sub-area. In other words, application sub-area substantially corresponds to the at least one measurement sub-area. This means that the GAI value currently in the detection systems detection area, e.g. in a camera's field of view is used to determine the application rate for the current sub-area.

[0060] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one measurement sub-area comprises at least one previous measurement sub-area, e.g. two sequential subareas, wherein the at least one previous measurement sub-area and the application sub-area are located at different positions in the agricultural field. In other words, the application sub-area may be spatially subsequent in a moving direction of the application device moving through the agricultural field, in particular direct subsequent to the at least one measurement sub-area, wherein in particular the at least one measurement sub-area is spatially continuous. Further, the at least one measurement sub-area comprises at least one previous measurement subarea, wherein the green area index data take into account green leaf areas for the at least one previous measurement sub-area. This means that not only the GAI value currently in the detection system’s detection area, e.g. a camera's field of view, is used to determine the application rate, but also the GAI value of the previous sub-area, which may be held available in a memory, as it has already been sprayed at the time of the decision for the current sub-area. Further, also the GAI value of the sub-area or a plurality of sub-areas before the previous sub-area used, which also may be held available in a memory, to determine the application rate. The GAI values or data of the different sub-areas may be averaged to compare to the preset GAI-thresholds for a decision.

[0061] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one, in particular at least two sequential, in particular 3-4 measurement sub-areas comprises an actual measurement sub-area and at least one previous measurement sub-area, wherein the at least one measurement sub-area and the application sub-area at least partially overlap. In other words, one of the at least one measurement sub-areas and the application sub-area substantially spatially corresponds to each other and the rest of the at least one measurement sub-areas are spatially different to each other and different to the application sub-area. The application sub-area is spatially subsequent in a moving direction of the application device moving through the agricultural field, in particular direct subsequent to the rest of the at least one measurement sub-area. The at least one measurement sub-area is spatially continuous.

[0062] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the method further comprise: averaging of the provided green area index data for at least two subsequent measurement sub-areas; providing variable application rate data for applying the agricultural product on the application sub-area at least based on the averaged green area index data by utilizing the variable application rate model.

[0063] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the method may further comprise: providing a conversion model configured to convert green leaf area (GLA) measured with single 2-dimensional red-green-blue (RGB) images and / or Near Infrared and / or red edge images and the resulting binary image to green area index (GAI) data; providing green area index (GAI) data for the sub-area based on a measurement cycle (e.g., of 10-15 images) of the provided green leaf areas (GLA) utilizing the conversion model; wherein the variable application rate model is configured to provide variable application rate data for applying the agricultural product on the sub-area at least based on the green area index (GAI) data for the sub-area; providing variable application rate data for applying the agricultural product on subareas at least based average green area index (GAI) data of the at least two sequential, in particular 3-4 sub-areas by utilizing the variable application rate model.

[0064] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one detection system may comprise at least one camera unit configured for capturing image data of the at least one measurement sub-area of the agricultural field. Such a camera unit may be assigned to one or more application sub-devices. In an example, an application device may comprise multiple cameras, wherein a camera is assigned to a specific application sub-devices, e.g., one nozzle or a specific section of a sprayer boom.

[0065] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one camera unit is configured to provide red green blue-images and / or Near Infrared Images and / or Red Edge Images and / or multi-spectral images, and the at least one detection system is adapted to detect or determine the green leaf area based on the red green blue-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images.

[0066] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the red green blue-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images are converted into binary images, and the at least one detection system is configured to detect or determine green area index data based on the binary images.

[0067] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one detection system comprises a conversion model configured to convert green leaf area measured in the measurement sub-area into green area index data for the at least on, in particular two consequential, in particular 3-4 measurement sub-areas.

[0068] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on a sub-area of an agricultural field, the red green blue (RGB)-images and / or Near Infrared Images and / or Red Edge Images and / or multi-spectral images and the related binary images are provided after emergence of the crop, i.e. , when the crop plants penetrate through the soil surface. In this context, it will be usually after the BBCH 14. Further, the red green blue-images and / or Near Infrared Images and / or Red Edge Images and / or multi-spectral images and the related binary images are provided in a time range from after emergence until end of flowering (e.g., BBCH 71 ) of the crop.

[0069] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the variable application rate model comprises at least one lookup table or algorithm in which a relationship between a green area index and an application rate for the agricultural product is provided. Such lookup tables or algorithms may be based on the results of large scale field trials, which may allow for specific crops, for certain growth stages to read out the best product application rate for certain green area indexes or GAI- classes.

[0070] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, providing the variable application rate data for applying the agricultural product on the application sub-area comprises: providing base application rate data for the application sub-area; providing the variable application rate data for applying the agricultural product on the application sub-area based on the base application rate data and the averaged green area index data or the green area index data by utilizing the variable application rate model.

[0071] Within the scope of the present disclosure, the variable application rate data may be determined in a single-stage or two-stage setting. A single-stage setting within the context of the present disclosure is to be understood to mean that the variable application rate is only determined in real time in the field, i.e. , substantially during the application of the agricultural product. A two-stage setting within the context of the present disclosure is to be understood to mean that in a first step a predefined application rate and / or a base application rate is specified for an agricultural field or for an area of an agricultural field and in a second step, this base application rate is adjusted / changed for sub-areas of a field during the application of the agricultural product. Notably, such a base application rate may be provided field-specific or also sub-area-specific (“teilflachenspezifisch”).

[0072] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the at least one detection system is assigned to one or more application sub-devices configured to apply the agricultural product on application sub-area, wherein the application sub-devices are particularly single nozzle units and / or boom sections of a sprayer boom or single outlets or sections of outlets of a spreader.

[0073] In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field, the agricultural product is a crop protection product, particularly a fungicide product and / or a plant growth regulator product, an insecticide, a biological fungicide or insecticide, a plant growth stimulant, a defoliant / desiccant, a bumdown herbicide or a liquid fertilizer product and in case of a spreader a solid fertilizer. In an embodiment of the computer-implemented method for providing variable application rate data for applying an agricultural product on a sub-area of an agricultural field, the method may further comprise: providing control data for controlling at least one application sub-devices of an application device at least based on the variable application rate data for applying the agricultural product on the application sub-area of the agricultural field. In an example, control data for all application sub-devices of an application device are provided such that the application rates may be adapted across a sprayer boom on a nozzle or section level while driving through the agricultural field when spraying.

[0074] An exemplary process can be described as follows: For a hyperlocal application or use rate variation for crop protection products, e.g. fungicides, PGRs and nitrogen fertilizers the same camera sensor equipment as for weed thresholds is used, but the row detection is switched off. This is the so-called a ‘burn down mode’ which is, in the case of herbicides, used for the application of total herbicides such as Roundup / glyphosate to control green vegetation before the planting of com or soy. However, the same ‘mode of the detection’ can be used in non-row crops, which are drilled in narrow lines (usually 5-25cm row distance) such as cereals, oil seed rape, Canola etc. which have in Canada, Australia, parts of the US, Europe including Eastern Europe and CIS states much larger hectarages than row crops. In this detection mode each the camera sensor system is capturing any green surface of the crop canopy (leaf+stem) without soil and senescent part of leaves.

[0075] Up to this point it is technically like a weed coverage detection. But one use in ‘non row crops’ as oil seed rape and cereals the green crop coverage detection, as this is based on 2- dimensional images, a further step had to be built in to integrate the relationship to the real 3-dimensional leaf and stem surface of crops to calculate GAI values. For this purpose on many places in a field where photos were taken of a defined surface (1 m2), the plants on these surfaces were cut and the leaf and stem surfaces measured on a laboratory scanner.

[0076] In Fig. 7 shows the regression or the relationship of the measured green area with the 3- dimensional scanner data, as a calibration for the Green Area Index (GAI) for the example of the crop winter oil seed rape. These regressions specific for each crop. Implementation of using GAI-thresholds is carried out to vary the uses rates of for example fungicides, PGRs or nitrogen fertilizers on a hyperlocal level for each camera sensor and nozzle position on the sprayer boom while the sprayer passes a field. The GAI values of at least two preceding, preferably of 3-4 measurement sub-areas, would be compared with presently fixed GAI-thresholds for certain products which depend on the characteristics of the product, e.g. dose response curves. This product information may come from field trials.

[0077] However, this process is done so far in practice mainly by comparing photos with a certain GAI with a crop in the field or by using an app on a smart phone on a limited number of positions in a larger field. The invention will allow to ‘transfer’ this agronomic knowledge of using the Green Area Index for crop management for a use on SmartSprayers. This automizes the decision making for application or use rate variations and implements it on the hyperlocal level on each camera sensor position to adopt rates to patches of crop canopies in the driving directions but also along the sprayer boom.

[0078] The rate variation is triggered by GAI-thresholds which are pre-defined based on the agronomic knowledge from product related field trials. For example for the plant growth regulator and fungicide Metconazole & Mepiquat-CI 240 EW in spring in oil seed rape:

[0079] GAI-threholds Application or use rates

[0080] GAI below 0.8 = application or use rate 0

[0081] GAI 0.8-2.0 = application or use rate 0.8 l / ha

[0082] GAI above 2.0 = application or use rate 1 .2 l / ha.

[0083] In another example the use of Tebuconazole 250 EC:

[0084] GAI-thresholds Application or use rates

[0085] GAI below 1 .0 = application or use rate 0.5 l / ha

[0086] GAI 1.0-2.0 = application or use rate 1 .0 l / ha

[0087] GAI above 2.0 = application or use rate 1 .5 l / ha

[0088] The GAI-thresholds are used to adapt the product application or use rates (for example

[0089] 3 or 4 different rates per ha) to a patchy uneven crop of oil seed rape in a hyperlocal level in an automized way on each camera sensor position.

[0090] BRIEF DESCRIPTION OF THE DRAWINGS In the following, the present disclosure is further described with reference to the enclosed figures:

[0091] Figure 1 illustrates example embodiments of a centralized and a decentralized computing environment with computing nodes;

[0092] Figure 2 illustrates example embodiments of a centralized and a decentralized computing environment with computing nodes;

[0093] Figure 3 illustrates an example embodiment of a distributed computing environment;

[0094] Figure 4 illustrates a flow diagram of a computer-implemented method for providing variable application rate data for applying an agricultural product on a sub-area of an agricultural field;

[0095] Figure 5 illustrates a system for providing variable application rate data for applying an agricultural product on a sub-area of an agricultural field;

[0096] Figure 6 illustrates an application device for applying an agricultural product on a sub-areas of an agricultural field;

[0097] Figure 7 illustrates an example diagram in which the green leaf area (GLA) from red-green-blue (RGB) images is plotted versus the green area index (GAI) data measured by experiments of a crop of winter oilseed rape;

[0098] Figure 8 illustrates exemplarily the different possibilities to receive and process field data, in particular green leaf area (GLA) data and / or green area index (GAI) data.

[0099] DETAILED DESCRIPTION OF EMBODIMENT 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.

[0100] Figures 1 to 3 illustrate different computing environments, central, decentral and distributed. The methods, apparatuses, computer elements etc. 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.

[0101] 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.

[0102] 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 floatingpoint 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.

[0103] The memory 24 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.

[0104] The computing nodes 21 , 21.1 to 21. n may include multiple structures 26 often referred to as “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 thereof. 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 21 n is caused to perform a function. Such a structure may be computer-readable directly by the processors (as it 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 hard-coded 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”.

[0105] 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 executed computer-executable instructions that constitute an executable component. For example, such computerexecutable 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 computerexecutable 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.

[0106] 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 communication 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.

[0107] 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 and 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.

[0108] Figure 2 illustrates an example embodiment of a decentralized computing environment 30’ with several computing nodes 21. T to 21 .n’ denoted as filled circles. In contrast to the centralized computing environment 20’ illustrated in Figure 1 , the computing nodes 21. T to 21. n’ of the decentralized computing environment are not connected to a central computing node 2T 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. T to 21. n’ (local or remote computing system) and data may be distributed among various computing nodes 21. T 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 2T has been expanded to provide an overview of the components present in the computing node 2T. In this example, the computing node 2T comprises the same components as described in relation to Figure 1.

[0109] 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.

[0110] Figure 4 illustrates a flow diagram of a computer-implemented method for providing variable application rate data for applying an agricultural product on an application subarea of an agricultural field. In a first step S100, green area index data for at least one measurement sub-area are provided, wherein the green area index data are provided by at least one detection system, wherein the at least one detection system is configured to detect a green leaf area of a crop canopy in at least one measurement sub-area of an agricultural field while an application device is moving through the agricultural field. In a second step S200, a variable application rate model configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data of the at least one measurement sub-area is provided. In a third step S300, variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data by utilizing the variable application rate model are provided.

[0111] Figure 5 illustrates a system 10 for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field. The system comprises a green area index data providing unit 11 configured to provide green area index data for at least one measurement sub-area. The green area index data are provided by at least one detection system 14, which is configured to detect a green leaf area of a crop canopy in at least one measurement sub-area of an agricultural field while an application device is moving through the agricultural field. A variable application rate model 15 providing unit 12 is configured to provide a variable application rate model 15 configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data of the at least one measurement sub-area. A variable application rate data providing unit 13 is configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data by utilizing the variable application rate model 15. The detection system 14 includes a conversion model 16.

[0112] Figure 6 illustrates an application device 100 for applying an agricultural product on an application sub-area 111 of an agricultural field 120 in a variable application mode, wherein the variable application rate data are provided according to a method disclosed herein. The application sub-area 111 is located underneath the sprayer boom 130. As shown in Figure 6, the agricultural field 120 is divided into measurement sub-areas 110. The GAI index of at least two subsequent, in particular 3-4 measurement subareas, may be averaged and compared to the GAI index allocated to pre-set or the previous used application rates to decide based on an application rate model or algorithm, if an application rate needs adaptations on a given detector system, e.g., camera sensor and nozzle position. The different striped and shaded of areas show for which parts of the field which application rate was used for the variable application rate application. The application rate can be varied e.g. by providing a plurality of nozzles (multi nozzle head) which can be selectively activated. Depending on the number of activated nozzles the application rate varies. Thus, each nozzle can be operated in an on / off mode, which may simplify the operation and keep the spray characteristic in shape, as partially activated nozzles often tend to have a non-uniform spray characteristic. Alternatively, pulse width modulation (PWM) nozzles may be used to vary the use rates.

[0113] As shown in Figure 6 (by different shading etc.) the measurement sub-areas 110 have different GAI- class values, i.e., GAI-class 1 to 3 to define the rate of use for an agricultural product based on a GAI-class / rate model or look-up table separately for every nozzle or boom section. These measurement sub areas 110 may be areas that extend in a length being defined by a preselected / pre-defined cycles (e.g., 10 or 15 images). The green area index is used to make a decision, if the application rate for applying the agricultural product on the application sub-area need to be changed or not. The repetition rate of the measurement and image sampling and thus the overlap of the sub-areas 110 may also be adapted to the processing speed of the measurement and evaluation equipment. If the measurement and processing speed is comparatively high, more images may be acquired and processed. Additionally or alternatively, the available activation time or the response time of the application sub-devices, e.g., the nozzles, may also be taken into account when setting the repetition rate of the measurement and image sampling and thus the overlap of the measurement sub-areas 110. The application device 100 may comprise one or more spray lines on a sprayer boom 130, wherein such a sprayer boom 130 may comprise one or more detection systems 14. Such a detection system 14 may in turn comprise one or more cameras units and one or more light emitting diode (LED) lightning / illumination units. A detection system 14 may be assigned to one or more application sub-devices, e.g., a nozzle unit and / or a boom section of the sprayer boom 130 of the application device 100. The LED lightning / illumination units are particularly provided as a flash unit that is directed towards the ground. Such a setup is particularly useful since an image may be captured synchronized with a flash triggering. In this way, the ground or the plants may be illuminated sufficiently so that shadowing by plants or parts of plants may be avoided as far as possible. Such shadowing may be problematic, since such shadows are often interpreted as green leaf area by image recognition software. The data of one camera system may serve at the same time two different spray lines for example to guide a spot spray of herbicides and in the second spray line a VRA spray of PGR's and fungicides with a two tank sprayer system.

[0114] The illustrated GAI driven application or use rates 1 , 2 und 3 are determined after the application device 100 has passed the respective area 110, 11 1. GAI driven application or use rate 2 may be considered as an exemplary base GAI driven application or use rate, whereas GAI driven application or use rate 1 is an adaption of the base GAI driven application or use rate by increasing and GAI driven application or use rate 3 is an adaption of the base GAI driven application or use rate by decreasing the base GAI driven application or use rate. Green area index (GAI) driven use or application rate determination can be carried our either before entering the region of interest (ROI) or after having entered the ROI. In the first case the following steps are proceeded: Recording the previous ROI with e.g. a camera and determining the GAI in the previous ROI from the recording; Deriving a GAI driven application or use rate for the preceding ROI from the GAI, taking into account the preceding GAI and the past GAI; Comparing the previous GAI with the GAI just determined and determining the application or application or use rate for the previous ROI. The application rate determination remains unchanged while the previous ROI is being traversed (in an example, the current GAI could be compared with the GAI while the ROI is being traversed). In the second case the following steps are proceeded: Recording the ROI ahead with e.g. a camera and determining the GAI in the ROI ahead from the recording; Deriving a GAI driven application or use rate from the GAI of the ROI just passed (past) and GAI driven application or use rate even further in the past GAI driven application or use rate; Comparing the GAI driven application or use rate of the past ROI GAI driven application or use rate with the previous GAI driven application or use rate and determine an application or application or use rate. During the crossing of the previous ROI, the application or application or use rate remains unchanged (in an example, the current GAI could be compared with the GAI while the ROI is being crossed).

[0115] Referring again to Figure 6, the application or spray boom during application may be located at a particular position, which is allocated to a particular sub-area. Sub-areas the application boom has already passed can be considered as previous sub-areas which are already sprayed. When the application or spray boom arrives at a sub-area to be sprayed, the application rate for this current sub-area is determined. For this purpose, the detection is carried out for the current sub-area and makes the current spraying decision for this sub-area. The current GAI in the current sub-area is taken into account and the application rate is then set and no longer changed while driving over the current sub-area. Also the GAI of the previous sub-area can be considered for determining the application rate for the current sub-area. In a further embodiment, also the GAI of the sub-area before the previous sub-area can be considered, i.e. the last both sub-areas, for determining the application rate for the current sub-area. When considering GAI of a plurality of sub-areas, the relation of GAI between the different sub-areas can be considered for setting the application rate for the current sub-area. The GAI of the different sub-areas can be averaged, or a gradient thereof can be determined. Depending on the relation, an average and / or a grandient the application rate can be set.

[0116] Figure 7 illustrates an example diagram in which the green leaf area (GLA) from red- green-blue (RGB) images is plotted versus the green area index (GAI) data measured by experiments of a crop of winter oilseed rape. As can be taken from Figure 7 the plotted green leaf area (GLA) from red-green-blue (RGB) images versus the green area index (GAI) data measured by experiments may form a regression line having a regression value of 0.8551 .

[0117] Figure 8 illustrates exemplarily the different possibilities to receive and process field data, in particular green leaf area (GLA) data and / or green area index (GAI) data. For example, field data can be obtained by all kinds of agricultural equipment 300, 310, 320, 330. The field data may be obtained by land-based agricultural equipment, 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 and / or 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).

[0118] 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.

[0119] 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, 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 different nodes using different equipment / data processing units.

[0120] 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

Claims1. Computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area (111 ) of an agricultural field (120), comprising: providing green area index data for at least one measurement sub-area (110), wherein the green area index data are provided by at least one detection system (14), wherein the at least one detection system (14) is configured to detect a green leaf area of a crop canopy in at least one measurement sub-area of an agricultural field (120) while an application device is moving through the agricultural field (120); providing a variable application rate model (15) configured to provide variable application rate data for applying the agricultural product on the application sub-area (111 ) at least based on the green area index data of the at least one measurement sub-area; providing variable application rate data for applying the agricultural product on the application sub-area (111 ) at least based on the green area index data by utilizing the variable application rate model (15).

2. Computer-implemented method according to claim 1 , wherein the at least one measurement sub-area (110) substantially corresponds to the application sub-area (111 ).

3. Computer-implemented method according to claim 1 , wherein the at least one measurement sub-area (110) comprises at least one previous measurement sub-area (110), wherein the at least one previous measurement sub-area (110) and the application sub-area (111 ) are located at different positions in the agricultural field (120).

4. Computer-implemented method according to claim 1 , wherein the at least one measurement sub-area (110) comprises an actual / current measurement sub-area and at least one previous measurement sub-area, wherein the at least one measurement sub-area (110) and the application sub-area (111 ) at least partially overlap.

5. Computer-implemented method according to claims 3 or 4, wherein the method further comprises: averaging of the provided green area index data for at least of two measurement sub-areas; providing variable application rate data for applying the agricultural product on the application sub-area (111 ) at least based on the averaged green area index data by utilizing the variable application rate model (15).

6. Computer-implemented method according to any one of the preceding claims, wherein the at least one detection system (14) comprises at least one camera unit configured for capturing image data of the at least one measurement sub-area (110) of the agricultural field (120).

7. Computer-implemented method according to claim 6, wherein the at least one camera unit is configured to provide red green blue-images and / or Near Infrared Images and / or Red Edge Images and / or multi-spectral images, and wherein the at least one detection system (14) is adapted to detect or determine the green leaf area based on the red green blue-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images.

8. Computer-implemented method according to claim 7, wherein the red green blue-images and / or the Near Infrared Images and / or the Red Edge Images and / or multi-spectral images are converted into binary images, and wherein the at least one detection system (14) is configured to detect or determine green area index data based on the binary images.

9. Computer-implemented method according to any one of the preceding claims, wherein the at least one detection system (14) comprises a conversion model (16) configured to convert green leaf area measured in the measurement sub-area (110) into green area index data for the at least one measurement sub-area (110).

10. Computer-implemented method according to any one of the preceding claims, wherein the variable application rate model (15) comprises at least one lookup table or algorithm in which a relationship between a green area index and an application rate for the agricultural product is provided.

11. Computer-implemented method according to any one of the preceding claims, wherein providing the variable application rate data for applying the agricultural product on the application sub-area (111 ) comprises: providing base application rate data for the application sub-area (111 ); providing the variable application rate data for applying the agricultural product on the application sub-area (111 ) based on the base application rate data and the averaged green area index data or the green area index data by utilizing the variable application rate model (15).

12. Computer-implemented method according to any one of the preceding claims, wherein the at least one detection system (14) is assigned to one or more application sub-devices configured to apply the agricultural product on application sub-area (111 ), wherein the application sub-devices are particularly single nozzle units and / or boom sections of a sprayer boom (130) or single outlets or sections of outlets of a spreader.

13. Computer-implemented method according to any one of the preceding claims, wherein the agricultural product is a crop protection product is particularly a fungicide product and / or a plant growth regulator product, an insecticide, a biological fungicide or insecticide, a plant growth stimulant, a defoliant / desiccant, a bumdown herbicide or a liquid fertilizer product and in case of a spreader a solid fertilizer.

14. An apparatus for providing variable application rate data for applying an agricultural product on an application sub-area (111 ) of an agricultural field (120), the apparatus comprising: a computing node; and a computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the computing node, cause the apparatus to perform the following steps:providing green area index data for at least one measurement sub-area, wherein the green area index data are provided by at least one detection system (14), wherein the at least one detection system (14) is configured to detect a green leaf area of a crop canopy in at least one measurement sub-area (110) of an agricultural field (120) while an application device is moving through the agricultural field (120); providing a variable application rate model (15) configured to provide variable application rate data for applying the agricultural product on the application sub-area (111 ) at least based on the green area index data of the at least one measurement sub-area (110); providing variable application rate data for applying the agricultural product on the application sub-area (111 ) at least based on the green area index data by utilizing the variable application rate model (15).

15. Application device (100) for applying an agricultural product on an application subarea of an agricultural field (120), wherein variable application rate data are provided according to any one of the claims 1 to 14.

Images