Method for generating area-specific application maps for field treatment with chemicals - Patents.com

Abstract

A method for generating an area-specific dispersion map (8) for treating a field with a chemical is provided. The method includes providing a hyper-model (1) including a chemical recommendation model (PRM) (2) and a biophysical parameter model (BPM) (3). The method further includes providing PRM input parameters (4) to the chemical recommendation model (2) and generating a PRM output (5) by the chemical recommendation model (2). The method also includes providing BPM input parameters (6) to the biophysical parameter model (3) and generating a BPM output (7) by the biophysical parameter model (3). Finally, the method includes generating an area-specific dispersion map (8) by the hyper-model (1) using at least a portion of the PRM output (5) and a portion of the BPM output (7). Additionally provided are a system (19) for generating the area-specific dispersion map (8), computer program elements, uses of the area-specific dispersion map (8), and agricultural equipment (23).

Description

[Technical field]

[0001] The present invention relates to digital agriculture. In particular, the present invention relates to a method for generating an area-specific application map for treating a field with a chemical, and a system for generating an area-specific application map. The present invention further relates to a computer program element, an area-specific application map, an area-specific control data and / or an area-specific control map, and the use of agricultural equipment. [Background technology]

[0002] Treatment of fields with chemicals such as fungicides, herbicides, insecticides, acaricides, molluscicides, nematicides, birdicides, piscicides, rodenticides, repellents, antimicrobials, biocides, safeners, plant growth regulators, urease inhibitors, nitrification inhibitors and / or denitrification inhibitors is commonly performed to increase field yields. Also, various models have been developed to determine the optimal type and amount of chemicals to be applied to a field. However, these models do not take into account regional differences within a field. Even if all areas of a field do not require the same amount or type of chemicals to be applied, this is still not included in the model. Summary of the Invention [Problem to be solved by the invention]

[0003] Therefore, an object of the present invention is to provide a method for generating a drug distribution map that takes into account regional differences. [Means for solving the problem]

[0004] The object of the present invention is solved by the subject matter of the independent claims, further embodiments of which are incorporated in the dependent claims.

[0005] According to a first aspect of the present invention, there is provided a method for generating an area-specific application map for treating a field with an agent.

[0006] In this context, the term "field" is to be understood as any area in which living organisms, in particular crop plants, are produced, cultivated, sown and / or are planned to be produced, cultivated or sown. The term "field" also includes horticultural and forestry fields. Suitable crops are onion (Allium cepa), pineapple (Ananas comosus), groundnut (Arachis hypogaea), asparagus (Asparagus officinalis), oats (Avena sativa), sugar beet (Beta vulgaris spec. altissima), rapa (Beta vulgaris spec. rapa), rapeseed (Brassica napus var. napus), rutabaga (Brassica napus var. napobrassica), Brassica rapa var.silvestris, Brassica oleracea, Brassica nigra, Tea plant (Camellia sinensis), Safflower (Carthamus tinctorius), Pecan (Carya illinoinensis), Lemon (Citrus limon), Orange (Citrus sinensis), Coffee plant (Coffea arabica), (Coffee plant (Coffea canephora), Coffee plant (Coffea liberica), Cucumis sativus, Corngrass (Cynodon dactylon), Carrot (Daucus carota), Oil palm (Elaeis guineensis), Wild strawberry (Fragaria vesca), Soybean (Glycine max), Upland cotton (Gossypium hirsutum), (Gossypium arboreum, Gossypium herbaceum, Gossypium vitifolium), sunflower (Helianthus annuus), rubber tree (Hevea brasiliensis), barley (Hordeum vulgare), hops (Humulus lupulus), sweet potato (Ipomoea batatas), Chinese walnut (Juglans regia), lentil (Lens culinaris), flax (Linum usitatissimum), tomato (Lycopersicon lycopersicum), apple (Malus spec.), cassava (Manihot esculenta), alfalfa (Medicago sativa), banana (Musa spec.), tobacco (Nicotiana tabacum) (N. rustica), olive (Olea europaea), rice (Oryza sativa), lima bean (Phaseolus lunatus), kidney bean (Phaseolus vulgaris), Norway spruce (Picea abies), pine (Pinus spec.), pistachio (Pistacia vera), pea (Pisum sativum), wild cherry (Prunus avium), peach (Prunus persica), pear (Pyrus communis), apricot (Prunus armeniaca), sweet cherry (Prunus cerasus), almond (Prunus dulcis) and plum (Prunus domestica), redcurrant (Ribes sylvestre), castor bean (Ricinus communis), sugar cane (Saccharum officinarum), rye (Secale cereale), white mustard (Sinapis alba), potato (Solanum tuberosum), sorghum (Sorghum bicolor (s. vulgare)), cocoa (Theobroma cacao), red clover (Trifolium pratense, bread wheat (Triticum aestivum), triticale (Triticale), durum wheat (Triticum durum), broad bean (Vicia faba), grape (Vitis vinifera), and maize (Zea mays). The most preferred crops are groundnut (Arachis hypogaea), sugar beet (Beta vulgaris spec. altissima), rapeseed (Brassica napus var.napus, Brassica oleracea, lemon (Citrus limon), orange (Citrus sinensis), Arabica coffee (Coffea arabica), robusta coffee (Coffea canephora), Liberica coffee (Coffea liberica), Corngrass (Cynodon dactylon), soybean (Glycine max), cotton (Gossypium hirsutum), green cotton (Gossypium arboreum), white cotton (Gossypium herbaceum), seaweed (Gossypium vitifolium), sunflower (Helianthus annuus), barley (Hordeum vulgare), oak walnut (Juglans regia), lentil (Lens culinaris), flax (Linum usitatissimum), tomato (Lycopersicon lycopersicum), apple (Malus spec.), alfalfa (Medicago sativa), tobacco (Nicotiana tabacum (N. rustica)), olive (Olea europaea), rice (Oryza sativa), lima bean (Phaseolus lunatus), common bean (Phaseolus vulgaris), pistachio (Pistacia vera), pea (Pisum sativum), almond (Prunus dulcis), sugar cane (Saccharum officinarum), rye (Secale cereale), potato (Solanum tuberosum), sorghum (Sorghum bicolor (s. vulgare)), triticale (Triticale), bread wheat (Triticum aestivum, durum wheat (Triticum durum), broad bean (Vicia faba), European grape (Vitis vinifera) and maize (Zea mays). Particularly suitable crops are cereals, maize, soybean, rice, rapeseed, cotton, potato, peanut or permanent crops.

[0007] It is to be understood that the term "area" refers to a sub-area or portion of the field, i.e. the field may be spatially divided into two or more areas, each of which may have different characteristics.

[0008] The term "application map" is to be understood as a map showing the two-dimensional spatial distribution of the amount, dose, type and / or format of agent applied to different areas within a field.

[0009] According to the method, a hypermodel is provided. In this context, a hypermodel is a model that includes at least two submodels and links the submodels. Said linking of the submodels may include linking of the outputs of the submodels and / or linking of the outputs of the submodels with the inputs of the submodels. In particular, the hypermodel can control the interdependencies of the submodels in an iterative procedure. For this purpose, the hypermodel may be configured to set initial input parameters of the submodels, such as predefined or standard values, then iteratively run the submodels, collect the outputs of the submodels and use said outputs of the submodels as inputs of the submodels in the next iteration. Furthermore, the hypermodel may be configured to stop the iterative procedure after a predefined number of iterations or after reaching a predefined accuracy. Finally, the hypermodel may be configured to collect the final results of the submodels, optionally transform them and output the results.

[0010] The hypermodel includes a pesticide recommendation model (PRM) and a biophysical parameter model (BPM). The pesticide recommendation model is provided with PRM input parameters. Based on the PRM input parameters, the pesticide recommendation model generates a PRM output. Similarly, the biophysical parameter model is provided with BPM input parameters. In this context, biophysical parameters are parameters related to physically measurable crop plant characteristics such as leaf area index, canopy density, tree height, biomass or chlorophyll content. Based on the BPM input parameters, the biophysical parameter model generates a BPM output. That is, the pesticide recommendation model and the biophysical parameter model are executed as part of the hypermodel. At least a part of the PRM output and a part of the BPM output are then used by the hypermodel to generate an area-specific application map. In particular, the area-specific components are obtained from the biophysical parameter model. Based on the area-specific application map, the field can be treated with pesticides such that each area of ​​the field is treated with an amount of pesticide and / or a selection of pesticides optimized for each area. Thus, the yield of the field can be optimized for each area, and the appropriate amount of chemical can be selected for each area. Areas of the field that require less chemical are treated with less chemical, which saves on chemical purchase costs and prevents unnecessary overuse of chemicals, which is more environmentally friendly. Meanwhile, fields that require more chemical are treated with more chemical, resulting in an increase in yield in a particular section that cannot be achieved with less chemical.

[0011] The method may be executed on a computing device, such as a tablet computer, a personal computer or a supercomputer. In particular, each part of the hypermodel can be executed on a separate processor, allowing the execution of the method to be parallelized and therefore faster.

[0012] According to one embodiment, the hyper-model further comprises a growth stage model (GSM). In this context, growth stages may include germination, sprouting, shoot development, leaf development, side shoot formation, tillering, stem elongation or rosette growth, shoot development, development of harvestable reproductive plant parts, bolting, inflorescence emergence, heading, flowering, fruit development, fruit and seed ripening or maturation, cell senescence and onset of dormancy. To the growth stage model, GSM input parameters are provided and the growth stage model generates GSM outputs based on said GSM input parameters. The growth stage model is also executed as part of the hyper-model and adds information about the crop growth stage to the hyper-model.

[0013] At least a portion of the GSM output may be used as an input parameter for the PRM. That is, the chemical recommendation model may depend on the growth stage of the crop. As an example, the use of some chemicals may be tied to a particular growth stage of the crop, e.g., some chemicals are most effective when applied to seedlings, while others are most effective when applied to flowering crops. If the growth stage is received as an input, the chemical that best fits the current or expected growth stage of the crop may be recommended.

[0014] According to one embodiment, the hyper-model further includes a disease infection risk model (DIRM). To the disease infection risk model, DIRM input parameters are provided. Based on the DIRM input parameters, the disease infection risk model generates a DIRM output. The disease infection risk model is also executed as part of the hyper-model to add information to the hyper-model regarding the risk of the crop being infected with a disease and / or the risk of the disease affecting the crop and therefore the yield of the field.

[0015] The input parameters of DIRM include at least some of the GSM outputs, i.e., the disease infection risk model depends on the crop growth stage. This improves the disease infection risk model because the vulnerability of crops to disease infection varies with the crop growth stage.

[0016] Furthermore, the PRM input parameters include at least a part of the DIRM output, i.e., the pesticide recommendation model depends on the disease infection risk of the crop, which improves the pesticide recommendation model because different crops should be sprayed in a field with different disease infection risks.

[0017] According to one embodiment, the area-specific application map includes a number of selected chemicals for the field and a chemical amount per area. That is, for each area of ​​the field, the area-specific application map provides one or more chemicals to treat the area and a corresponding chemical amount. The chemical amount is given, for example, as a weight or volume of chemical per unit area. The field includes a number of areas, and each area may be a polygonal cell of the field. More specifically, these areas may be square cells of the field. As an example, each square may correspond to a pixel of the satellite image.

[0018] According to one embodiment, the agent includes at least one of the group consisting of chemicals, biological agents, fertilizers, nutrients, and water. In particular, a combination of agents and / or substances may be used. The agents and / or combinations thereof may be labeled with an agent ID so that a user and / or agricultural equipment can select the agent and / or combination based on the agent ID. The chemical may be a fungicide, herbicide, insecticide, acaricide, molluscicide, nematicide, birdcide, pisciicide, rodenticide, repellent, antibacterial agent, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof. The biological agent may be a microorganism useful as a fungicide (bio-fungicide), herbicide (biohericide), insecticide (biopesticide), acaricide (biomicticide), molluscicide (biomolluscidicide), nematocide (bionematocide), birdcide, fishcide, rodenticide, repellent, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof. The agent increases the yield of the field, for example, by preventing disease and / or by supporting the growth of the crop.

[0019] According to one embodiment, the GSM input parameters include at least one of the group consisting of crop, variety, variety characteristics, raw weather data, sowing date, and growth stage observations. Variety refers to the variety of the crop and may be provided as a variety identifier or trade name of the crop. Variety characteristics refer to specific characteristics of the crop variety and may be provided as deviations from a "base" crop, for example. Raw weather data may include air temperature, soil temperature, precipitation, and sunshine hours. Growth stage observations are observations of the actual growth stage of the crop in the field. The observations may be obtained, for example, by a user and manually entered, or obtained by automatic observations in the field.

[0020] The GSM output includes the distribution of growth stages throughout the season, particularly at daily resolution. Growth stages can be provided, for example, on the BBCH scale, which provides numerical codes for crop growth stages such as germination, emergence, shoot development, leaf development, side shoot formation, tillering, stem elongation or rosette growth, shoot development, harvestable reproductive plant parts, bolting, inflorescence emergence, heading, flowering, fruit development, fruit and seed ripening or maturity, and cell senescence, onset of dormancy, etc.

[0021] As an example, a growth stage model can take crop and sowing date as input parameters and generate growth stage as output, e.g., based on a look-up table. Of course, the more sophisticated the model and the more input parameters, the more accurate the growth stage prediction will be.

[0022] The DIRM input parameters include at least one of the group consisting of crop, predecessor crop, variety, variety characteristics, raw weather data, sowing date, infection rules, tillage and disease observations. The predecessor crop relates to a crop planted in the field at the beginning of the season or in the previous year. The predecessor crop information may include the date the predecessor crop was planted in the field. The infection rules may include any kind of rules describing the infection of the crop, taking into account for example the growth stage of the crop, the weather and / or the occurrence of the pathogen. The tillage may include any kind of tillage information, such as dates and details of the tillage performed in the field. The disease observations may be entered manually, for example by a user, or obtained by automated observations, for example taken by a fixed or non-fixed camera in the field.

[0023] DIRM outputs include disease transmission data, in particular disease transmission risk, and disease transmission events, in particular past, present, and future. Disease transmission risk and / or events include disease type, date of infection or illness, and severity of disease and / or infection.

[0024] As an example, a disease infection risk model may take crops and crop growth stages as input parameters and generate as output the disease infection risk for at least one disease or infection. For this purpose, a table containing multiple crops and their infection risks as a function of growth stage may be used. Again, the more sophisticated the model and the more input parameters, the more accurate the disease infection risk predictions will be.

[0025] PRM input parameters include at least one of the following groups: crop, variety, variety characteristics, indicators, drug registration, drug effectiveness requirements, and observational data. Indicators refer to the justification for using a drug and include, among other things, the effectiveness of a particular drug to suppress a disease or promote crop growth. Drug registration refers to the drug registration and includes information under what conditions a drug may be used. Drug effectiveness requirements include further requirements for a drug to be efficient, such as the stage of crop growth or weather conditions.

[0026] The PRM output includes a plurality of selected pesticides and pesticide amounts. In particular, the PRM output can provide different alternatives that can be used. Preferably, the PRM output further includes the dependence of the plurality of selected pesticides and / or pesticide amounts on the biophysical parameters. By using the PRM output with said dependence on the biophysical parameters in conjunction with the BPM output, the hyper-model can determine the recommended pesticide and pesticide amount for each section of the field.

[0027] As an example, a drug recommendation model can take crop, growth stage, and disease infection risk as input parameters and generate a recommended drug as output. In a simple implementation, a look-up table of recommended drugs can be used to generate the output. Again, the more sophisticated the model and the more input parameters, the more accurate the drug recommendation.

[0028] The BPM input parameters include remote image data of the field, said remote image data being in particular multispectral image data. The remote image data may be provided by a satellite, an aircraft and / or a drone. In particular, pixels of the remote image data may correspond to areas of the field.

[0029] BPM outputs include area-specific distributions of biophysical parameters, in particular Leaf Area Index and / or Canopy Density. As an example, Leaf Area Index can be defined as the one-sided green leaf area per unit land surface area. As another example, Canopy Density can be defined as the projection of green leaf area per unit land surface area.

[0030] As an example, a biophysical parameter model can take multispectral image data as input and generate a leaf area index as output, where the leaf area index can be calculated as a simple function from the multispectral image data. Again, the more sophisticated the model and the more input parameters, the more accurate the biophysical parameters will be.

[0031] To generate area-specific dispersion maps, the hypermodel can, for example, combine pesticide recommendations from the pesticide recommendation model with leaf area index from the biophysical parameter model to generate area-specific dispersion maps. In a simple implementation, pesticides can be taken directly from the pesticide recommendation model and the amount of pesticide per area is calculated as a function of leaf area index.

[0032] According to one embodiment, the growing stage model is a process model. In this context, a process model is a model where a user provides certain functions of parameters and / or dependencies between parameters. These functions and / or dependencies may be simple functions and may be based on past observations. Alternatively or additionally, the growing stage model may be a machine learning model such as a decision tree, a computer implemented neural network, an artificial neural network, or any combination thereof. To train the machine learning model, the training data is split into two parts, one for training and the other for testing, for example 90% of the data for training and 10% for testing. When training and testing the machine learning model, the mean absolute error may be used as an evaluation metric. In particular, the mean absolute error may refer to the error on the BBCH scale for a given day or the time error for a given BBCH code.

[0033] According to one embodiment, the disease transmission risk model is a process model or a machine learning model. The mean absolute error, which may be used as an evaluation metric for the machine learning model, may refer to the disease incidence on a given day.

[0034] According to one embodiment, the drug recommendation model is a process model or a machine learning model. The mean absolute error, which may be used as an evaluation metric for the machine learning model, may refer to the amount of a given drug applied to a field.

[0035] According to one embodiment, the biophysical parameter model is a process model or a machine learning model. The mean absolute error that may be used as an evaluation index for the machine learning model may refer to the leaf area index and / or the canopy density.

[0036] According to one embodiment, at least a portion of the GSM output may be used as some BPM input parameters, for example the growth stage on the BBCH scale may be used as a BPM input parameter. Modeling of biophysical parameters may be improved by using growth stage predictions as inputs.

[0037] According to one embodiment, at least a part of the DIRM output is used as some GSM input parameters, for example the predicted disease infection events may be used as GSM input parameters, thus including the impact of the disease on the crop growth stages in the field.

[0038] According to one embodiment, at least a portion of the DIRM output may be used as some BPM input parameters, for example predicted disease or infection events may be used as BPM input parameters, which further includes the effect of the disease on the biophysical parameters.

[0039] According to one embodiment, at least some of the PRM outputs may be used as some GSM input parameters, for example recommended pesticides may be used as GSM input parameters, thus modeling the growth stages of the crop while taking into account the application of pesticides to the field.

[0040] According to one embodiment, at least some of the PRM outputs may be used as some DIRM input parameters, for example recommended pesticides may be used as DIRM input parameters, so that disease progression can be modeled taking into account the application of pesticides to the field.

[0041] According to one embodiment, at least a portion of the PRM output may be used as some BPM input parameters, for example, a recommended chemical may be used as a BPM input parameter, taking into account the effect of applying the chemical to the field on a biophysical parameter such as the leaf area index.

[0042] According to one embodiment, the hypermodel further comprises another model, to which input parameters are provided and which generates an output based on the input parameters. GSM, DIRM, PRM and / or BPM outputs may be used as some input parameters to the other model and outputs from the other model may be used as some GSM, DIRM, PRM and / or BPM input parameters. An example of such another model is a weather model, where weather affects not only biophysical parameters but also growth stages, disease infection risk, drug recommendations.

[0043] According to one embodiment, the method further comprises generating zone-specific control data and / or zone-specific control maps configured for use in controlling agricultural equipment spraying the field with crops. The zone-specific control maps may include, for example, nozzle pressures to be used in each zone of the field. The zone-specific control data may include, for example, nozzle pressures to be used based on a distance on a predetermined trajectory that the agricultural equipment is required to follow. Alternatively, the agricultural equipment may be configured to generate control signals for treating the field, in particular for spraying a pesticide, based on the zone-specific application map. The pesticide is then sprayed on the field in accordance with the zone-specific application map.

[0044] According to one embodiment, the method further includes determining a common formulation of a plurality of chemicals for the field by the hypermodel. In this context, a "common formulation" may be one chemical or usually a mixture of chemicals that are applied to the entire field at different application rates. Using only one common formulation requires less equipment than using different chemicals or different formulations of said chemicals for every zone of the field. The common formulation may be determined, for example, as the average or median of the chemical amount or concentration over the entire field. Using the common formulation, the zone-specific application map specifies the amount of the common formulation per unit area to be applied for each zone of the field. The amount of the common formulation per unit area may be between a minimum and a maximum value. The amount of the common formulation per unit area may also be zero, i.e. no chemical is applied to each zone of the field.

[0045] According to another aspect of the invention, there is provided a system for generating an area-specific scatter map, the system being configured to perform the method described above. In particular, the system includes at least one input interface for providing input parameters, the input parameters including GSM input parameters, DIRM input parameters, PRM input parameters and BPM input parameters. The system further includes at least one processing device configured to generate the area-specific scatter map, and at least one output interface for outputting the area-specific scatter map, the area-specific control data and / or the area-specific control map. The output interface may be a network interface adapted to broadcast the hypermodel output to agricultural equipment.

[0046] According to another aspect of the invention there is provided a computer program element arranged to carry out the method as described above when executed by a processor in the system as described above.

[0047] According to another aspect of the present invention, there is provided a use of an area-specific application map, area-specific control data and / or area-specific control map for spraying a chemical in a field, where the area-specific application map, area-specific control data and / or area-specific control map are generated according to the above-mentioned method. By spraying the chemical in accordance with the area-specific application map, area-specific management data and / or area-specific management map, an optimal amount of the chemical is sprayed in the field. In particular, the type and amount of the chemical is sufficient to bring about a good yield in the field. Also, the amount of the chemical is not excessive, which saves costs and is environmentally friendly.

[0048] According to another aspect of the present invention, there is provided an agricultural equipment, the agricultural equipment being equipped to apply a chemical to a field and configured to be controlled by an area-specific application map, area-specific control data and / or an area-specific control map provided by the above-mentioned method, so that the type and amount of chemical is sufficient to bring about a good yield in the field, and the amount of chemical is not excessive, which saves costs and is environmentally friendly.

[0049] These and other aspects of the invention will be apparent and elucidated with reference to a number of embodiments described hereinafter by way of example only and with further reference to the accompanying drawings, in which: FIG. [Brief description of the drawings]

[0050] [Figure 1] 1 illustrates the workflow of one embodiment of a hypermodel. [Diagram 2] 1 illustrates a workflow of another embodiment of a hypermodel. [Diagram 3] 13 illustrates a workflow of yet another embodiment of a hyper-model. [Figure 4] 13 illustrates a workflow of yet another embodiment of a hypermodel. [Diagram 5] 13 illustrates a workflow of yet another embodiment of a hyper-model. [Figure 6] 1 shows an example of a region-specific scatter map. [Figure 7] 1 illustrates a schematic diagram of a system for generating area specific scatter maps and agricultural equipment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0051] It should be noted that these figures are purely schematic and are not drawn to scale. In these figures, elements corresponding to elements already described may be given the same reference numerals. Examples, embodiments or any features, whether or not shown as non-limiting, should not be understood as limiting the invention as claimed.

[0052] Figure 1 shows the workflow of one embodiment of a hyper-model 1 for generating zone-specific application maps for treating a field with agents, which may be chemicals, biologicals, fertilizers, nutrients and water. It is to be understood that a field zone is a subfield zone or part of the field, i.e. the field is divided into a number of said zones.

[0053] The hypermodel includes a drug recommendation model (PRM) 2 and a biophysical parameter model (BPM) 3. PRM input parameters 4, such as crop, variety, variety characteristics, indicators, drug registrations, drug efficacy requirements, and / or observational data, are provided to the drug recommendation model 2. Based on the PRM input parameters 4, the drug recommendation model 2 generates a PRM output 5, including, for example, a number of selected drugs and drug amounts. Preferably, the PRM output 5 is provided depending on the biophysical parameters of the crop.

[0054] BPM input parameters 6 are provided to the biophysical parameter model 3. The BPM input parameters 6 may comprise remote image data, in particular multispectral image data, of the field. The remote image data may be provided by satellite, aircraft and / or drone. In particular, the BPM input parameters 6 are area-specific, i.e. the remote image data has a resolution of at least the size of the field area. Based on the BPM input parameters 6 the biophysical parameter model generates a BPM output 7. The BPM output 7 may comprise area-specific distributions of biophysical parameters, in particular Leaf Area Index and / or Canopy Density.

[0055] Based on the PRM output 5 and the BPM output 7, the hyper-model 1 generates an area-specific dispersal map 8. To do this, the hyper-model 1 can exploit the biophysical parameter dependence of the PRM output 5 and combine it with the BPM output. Additionally or alternatively, the hyper-model 1 can use a general dependence of dispersal rate on biophysical parameters, such as a linear dependence on leaf area index, to generate the area-specific dispersal map 8 from the PRM output 5 and the BPM output 7.

[0056] Another embodiment of the hyper-model 1 is shown in FIG. 2. In addition to the hyper-model 1 of FIG. 1, this hyper-model 1 includes a growth stage model (GSM) 9. GSM input parameters 10, such as crop, variety, variety characteristics, raw weather data, sowing date, and growth stage observations, are provided to the growth stage model 9. Based on the GSM input parameters 10, the growth stage model 9 generates a GSM output 11. The GSM output 11 may include the distribution of growth stages through the season, in particular with a daily resolution. The GSM output 11 is also used as part of the PRM input parameters 4, i.e. the chemical recommendation model 2 depends on the growth stage of the crop. As a result, the chemical recommendation model 2 can recommend the chemical that best suits the actual growth stage of the crop.

[0057] Yet another embodiment of the hyper-model 1 is shown in Fig. 3. In addition to the hyper-model 1 of Fig. 2, this hyper-model 1 includes a disease infection risk model (DIRM) 12. DIRM input parameters 13 such as crop, preceding crop, variety, variety characteristics, raw weather data, sowing date, infection rule, tillage, disease observations, etc. are provided to the disease infection risk model 12. Based on the input parameters 13, the disease infection risk model 12 generates a DIRM output 14. The DIRM output 14 includes disease infection data, in particular disease infection risk and disease infection events. The data may be provided for the past, present, or future.

[0058] Instead of making the GSM output 11 part of the PRM input parameters 4 provided by the hyper-model 1 of Figure 2, the DIRM output 14 is used in this embodiment as part of the PRM input parameters 4, i.e. the drug recommendation model 2 depends on the disease infection risk of the crop, thus further improving the drug recommendation model 2.

[0059] Also, the GSM output 11 is used as part of the DIRM input parameters 13, i.e. the disease transmission risk model 12 is dependent on the crop growth stage, thus further improving the disease transmission risk model 12.

[0060] Yet another embodiment of the hyper-model 1 is shown in Fig. 4. In addition to the hyper-model 1 of Fig. 3, this hyper-model 1 includes another model 15, for example a weather model. In this example, input parameters 16 of the other model 15 are provided, for example past and current weather data and satellite images. The output 17 generated by the other model 15 based on the input parameters 16 may include past weather data, actual weather data, and especially weather forecasts, in this example. The output 17 of the other model 15 is used as part of the input parameters 10, 13, 4, 6 of the growth stage model 9, the disease infection risk model 12, the drug recommendation model 2, and the biophysical parameter model 3, respectively. All of the models benefit from accurate weather data.

[0061] Yet another embodiment of the hyper-model 1 is shown in FIG. 5. In addition to the hyper-model 1 of FIG. 3, the GSM output 11 is used as part of the PRM input parameters 4 and as part of the BPM input parameters 6. Furthermore, the DIRM output 14 is used as part of the GSM input parameters 10 and the BPM input parameters 6. Furthermore, the PRM output 5 is used as part of the GSM input parameters 10, the DIRM input parameters 13, and the BPM input parameters 6. Finally, the BPM output 7 is used as part of the GSM input parameters 10, the DIRM input parameters 13, and the PRM input parameters 4. The accuracy of the hyper-model 1 is further improved since the models all depend at least to some extent on the output of the other models. The interdependence between the different models can be achieved by iteratively running the different models. As an example, in a first run, no interdependence between the models is used, where the input parameters coming from the output of the other model may be set to some standard or predetermined value. Then, in a second run, the output of the model from the first run is used as input parameters to generate a new, more accurate output. This procedure may be repeated, for example, until it converges to a level where additional runs do not significantly change the results.

[0062] Some or all of the models, i.e., the drug recommendation model 2, the biophysical parameter model 3, the growth stage model 9, the disease transmission risk model 12 and / or the other models 15, may be implemented as process models. In this context, process models are models where the user provides specific functions and / or dependencies between parameters. That is, these models include multiple algorithms that take input parameters and generate output parameters, where these algorithms may be programmed based on phenomenological observations and / or may include simulations.

[0063] Alternatively or additionally, some or all of the models may be implemented as machine learning models. Examples of machine learning models include decision trees, computer implemented neural networks, artificial neural networks, or any combination thereof. Training data for these models may be obtained from observations and measurements taken in past seasons. To train the machine learning models, the training data is split into two parts, one for training and the other for testing, e.g., 90% of the data for training and 10% for testing. When training and testing the machine learning models, the mean absolute error may be used as an evaluation metric.

[0064] FIG. 6 shows an example of the area-specific spray map 8. A number of areas 18.1-18.6 of the field are shown with different hatching. For each area 18.1-18.6, one type of pesticide or combination of pesticides and the amount of said pesticide to be sprayed in that particular area are shown. Alternatively, one common recipe to be sprayed in the field may be determined by the hyper model 1. In this case, the areas 18.1-18.6 of the area-specific spray map 8 may show only the amount of said common recipe to be sprayed in the field.

[0065] 7 shows a system 19 for generating area specific scatter maps 8. The system 19 includes an input interface 20 for providing input parameters, where GSM input parameters 10, DIRM input parameters 13, PRM input parameters 4 and BPM input parameters 6 are provided.

[0066] The processing unit 21 of the system 20 is configured to generate an area-specific spray map 8 using the hyper-model 1 according to the above description. The area-specific spray map 8 is then broadcast by an output interface 22 of the system 19. The broadcast may be performed via a network connection and / or the Internet. The area-specific spray map 8 is received by the agricultural equipment 23. Using the area-specific spray map 8, the agricultural equipment performs an area 18-specific spray of the agent to the field. Thus, the type and amount of agent is sufficient to bring a good yield to the field, and the amount of agent is not excessive, which saves costs and is environmentally friendly.

[0067] It should be noted that several embodiments of the present invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method type claims, whereas other embodiments are described with reference to apparatus type claims. However, those skilled in the art will infer from the above and following description that, unless otherwise noted, any combination of features belonging to one subject matter, as well as any combination between features relating to different subject matters, is disclosed in the present application. However, the combination of all features may have more synergistic effects than the mere sum of the features.

[0068] While the present invention has been illustrated and described in detail in the drawings and the above description, such illustration and description are illustrative or exemplary and are not intended to be limiting. The present invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the dependent claims. In the claims, the term "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. The presence of reference signs in the claims should not be interpreted as limiting the scope thereof.

Claims

1. A method for generating an area-specific application map (8) for treating a field with a chemical, comprising the steps of: Drug Recommendation Model, PRM (2) and Biophysical parameter model, including BPM (3) Providing a hypermodel (1); providing PRM input parameters (4) to said drug recommendation model (2) to generate a PRM output (5) by said drug recommendation model (2); providing BPM input parameters (6) to said biophysical parameter model (3) to generate a BPM output (7) by said biophysical parameter model (3); generating the area-specific scatter map (8) with the hyper-model (1) using at least a portion of the PRM output (5) and a portion of the BPM output (7).

2. The hyper-model (1) further includes a growth stage model, GSM (9), The method further comprises providing GSM input parameters (10) to the growing stage model (9) to generate a GSM output (11) by the growing stage model (9); 2. The method of claim 1, wherein said PRM input parameters (4) optionally include at least a portion of said GSM output (11).

3. The hyper-model (1) further includes a disease infection risk model, DIRM (12), The method further comprises providing DIRM input parameters (13) including at least a portion of the GSM output (11) to the disease transmission risk model (12) to generate a DIRM output (14) by the disease transmission risk model (12); 3. The method of claim 2, wherein said PRM input parameters (4) include at least a portion of said DIRM output (14).

4. The method according to claim 1, wherein the area-specific distribution map (8) includes a plurality of selected drugs for a field and drug amounts per area (18), the areas (18) being in particular polygonal cells, more particularly square cells.

5. 10. The method of claim 1, wherein the agent comprises at least one of the group consisting of chemicals, biological agents, fertilizers, nutrients, and water.

6. said GSM input parameters (10) including at least one of the group consisting of crop, variety, variety characteristics, raw meteorological data, sowing date, and growth stage observations; said GSM output (11) containing the distribution of growth stages through the seasons, in particular with daily resolution; The DIRM input parameters (13) include at least one of the group consisting of crop, preceding crop, variety, variety characteristics, raw weather data, sowing date, infection rules, tillage, and disease observations; said DIRM output (14) including disease and infection data, in particular disease and infection risks, disease and infection events, in particular past, present and future data; The PRM input parameters (4) include at least one of the group consisting of crops, varieties, variety characteristics, indications, drug registrations, drug efficacy requirements, and observational data; the PRM output (5) includes a plurality of selected drugs and drug amounts; 2. The method according to claim 1, wherein the BPM input parameters (6) comprise remote imagery data, in particular multispectral imagery data, of the field, in particular provided by a satellite, an aircraft and / or a drone, and / or the BPM output (7) comprises area-specific distributions of biophysical parameters, in particular Leaf Area Index and / or Canopy Density.

7. The growth stage model (9) is a process model or a machine learning model, The disease infection risk model (12) is a process model or a machine learning model, The drug recommendation model (2) is a process model or a machine learning model, The method of claim 1 , wherein the biophysical parameter model (3) is a process model or a machine learning model.

8. using at least a portion of said GSM output (11) as part of said BPM input parameters (6); using at least a portion of said DIRM output (14) as part of said GSM input parameters (10); using at least a portion of said DIRM output (14) as part of said BPM input parameters (6); using at least a portion of said PRM output (5) as part of said GSM input parameters (10); using at least a portion of said PRM output (5) as part of said DIRM input parameters (13); and using at least a portion of said PRM output (5) as part of said BPM input parameters (6).

9. 2. The method according to claim 1, wherein the hyper-model (1) comprises further models (15), in particular meteorological models.

10. The method of claim 1 , further comprising generating area-specific control data and / or an area-specific control map configured for use in controlling agricultural equipment that applies the agent to the field.

11. 2. The method of claim 1, further comprising determining a common formulation of a pesticide for the field using the hyper-model (1), and wherein the area-specific application map (8) specifies an amount per unit area of ​​the common formulation to be applied for each area (18) of the field.

12. A system for generating area-specific scatter maps (8) configured to carry out the method according to claim 1, comprising: at least one input interface (20) providing input parameters including at least one of the group consisting of the GSM input parameters (10), the DIRM input parameters (13), the PRM input parameters (4) and the BPM input parameters (6); at least one processing unit (21) configured to generate said area-specific scatter map (8); and at least one output interface (22) for outputting at least one of the group consisting of said area-specific scatter map (8), area-specific control data, and said area-specific control map.

13. A computer program element configured to perform the method of claim 1 when executed by a processor in a system (19) according to claim 12.

14. Use of an area-specific application map (8), area-specific control data and / or area-specific control map generated according to the method of claim 1 for applying a chemical to a field.

15. Agricultural equipment equipped to spray a field and configured to be controlled by an area-specific spray map (8), area-specific control data and / or area-specific control map provided by the method of claim 1.

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