WO2024017728A1

WO2024017728A1 - Variable pesticide application data

Info

Publication number: WO2024017728A1
Application number: PCT/EP2023/069293
Authority: WO
Inventors: Hubert Schmeer; Bjoern Kiepe; Carvin Guenther SCHEEL; Clemens Christian DELATREE; Matthias Gerber; Holger Hoffmann
Original assignee: Basf Agro Trademarks Gmbh
Priority date: 2022-07-22
Filing date: 2023-07-12
Publication date: 2024-01-25

Abstract

Computer-implemented method for providing pesticide application data for applying a pesticide product onto an agricultural field, comprising: providing soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; providing pesticide product data comprising at least information on the application of a pesticide product with respect to the at least one soil property parameter; providing variable pesticide application data for applying a pesticide product on the agricultural field at least based on the soil property data and the pesticide product data.

Description

VARIABLE PESTICIDE APPLICATION DATA

TECHNICAL FIELD

The present disclosure relates to a computer-implemented method for providing variable pesticide application data for applying a pesticide product in a variable manner depending on soil properties onto an agricultural field, an application device for applying a pesticide product on an agricultural field, a system for providing variable pesticide application data for applying a pesticide product on an agricultural field, an apparatus for providing variable pesticide application data for applying a pesticide product on an agricultural field, a use of soil property data, soil moisture data, pesticide product data, variable pesticide application data and/or control data for controlling a pesticide application device and a computer program element.

TECHNICAL BACKGROUND

The general background of this disclosure is the treatment of an agricultural field. The treatment of an agricultural field comprises the treatment of the soil of an agricultural field, a greenhouse, or the like, by pesticide products, e.g. soil herbicide products, e.g. in order to control unwanted weed plants.

In common agricultural practice, pesticide products, e.g. soil herbicide products, are applied on agricultural fields on basis of experience, expertise, and knowledge of the farmers, in particular by interpreting single soil parameters or weather parameters in order to make a decision for or against the application of pesticide products on a field level. This application of pesticide products provides a spatially non-optimal treatment/application of pesticide products on an agricultural field is provided.

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

SUMMARY OF THE INVENTION

In one aspect of the present disclosure, a computer-implemented method for providing variable pesticide application data, e.g. soil herbicide application data, for applying a pesticide product, e.g. a soil herbicide product, onto an agricultural field is presented, comprising: providing soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; providing pesticide product data, e.g. soil herbicide product data, comprising at least information on the application of a pesticide product, e.g. a soil herbicide product, with respect to the at least one soil property parameter; providing variable pesticide application data, e.g. variable soil herbicide application data, for applying a pesticide product, e.g. a soil herbicide product, on the agricultural field at least based on the soil property data and the pesticide product data, e.g. the soil herbicide product data.

In a further aspect, an application device for applying a pesticide product on an agricultural field is presented, wherein variable pesticide application data are provided by a method disclosed herein is presented.

In a further aspect, a system for providing variable pesticide application data for applying a pesticide product on an agricultural field is presented, comprising: a providing unit configured to provide soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; a providing unit configured to provide pesticide product data comprising at least information on the application of a pesticide product with respect to the at least one soil property parameter; a providing unit configured to provide variable pesticide application data for applying a pesticide product on the agricultural field at least based on the soil property data and the pesticide product data.

In a further aspect, an apparatus for providing variable pesticide application data for applying a pesticide product on an agricultural field is presented, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon computerexecutable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: providing soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; providing pesticide product data comprising at least information on the application of a pesticide product with respect to the at least one soil property parameter; providing variable pesticide application data for applying a pesticide product on the agricultural field at least based on the soil property data and the pesticide product data. In a further aspect, a use of soil property data, soil moisture data, pesticide product data, soil herbicide product data, variable pesticide application data, variable soil herbicide application data and/or control data for controlling a pesticide, e.g. a soil herbicide, application device by a method disclosed herein is presented.

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

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

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

The method, device, system, computer program element, disclosed herein provide an efficient, sustainable and robust way for providing variable pesticide application data in order to increase the effectivity, i.e. the application of the pesticide product in a correct rate with a correct amount, of the application of pesticide product on the agricultural field. The beneficial effect is provided by considering/including a plurality of relevant variables for the pesticide application into the providing of the variable pesticide application data.

It is an object of the present invention to provide an efficient, sustainable and robust way for providing variable pesticide application data in form of spatially varied application maps in order to increase the effectivity of the application of the pesticide product. In a preferred embodiment this allows the use of an optimum dose, e.g. of a soil herbicide product, depending on the soil texture and soil organic matter and further soil parameter avoiding unnecessary treating and/or over treatment of the agricultural field, and saving money and amounts of treating/pesticide products.

These and other objects, which become apparent upon reading the following description, are solved by the subject matters of the independent claims. The dependent claims refer to preferred embodiments of the invention. The term agricultural field as used herein is to be understood broadly in the present case and presents any area, i.e. surface and subsurface, of a soil to be treated 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, or the like. A plant may be a crop, a weed, a volunteer plant, a crop from a previous growing season, a beneficial plant or any other plant present on the agricultural field. The agricultural field may be identified through its geographical location or geo-referenced location data. A reference coordinate, a size and/or a shape may be used to further specify the agricultural field.

The term variable pesticide application data as used herein is to be understood broadly in the present case and presents any data providing information about a spatially resolved application of a pesticide product on the agricultural field. Further, the variable pesticide application data may include suitability data giving information about at least one pesticide product suitable for an application on the agricultural field at least based on the soil property data and pesticide product data. Furthermore, the variable pesticide application data may include dose rate data.

The dose rate is the quantity of radiation absorbed or delivered per unit time. The dose rate for applying a pesticide product on the agricultural field may be provided for the surface or subareas of the agricultural field. Furthermore, the variable pesticide application data may be provided by a pesticide application map, e.g. a soil herbicide application map. The pesticide application map may be a 2-dimensional application map. The variable pesticide application data may comprise instructions, tasks for application devices, and/or applicators to guide a soil property dependent variable rate application of the pesticide product.

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

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

The term pesticide product as used herein is to be understood broadly and encompasses any herbicide, fungicide, insecticide or mixtures thereof, i.e. the present disclosure is not limited to a specific kind of pesticide product. The term soil herbicide product as used herein is to be understood broadly in the present case and presents any herbicide material to be applied onto an agricultural field. Herbicides can specifically be referred to as selective or non-selective herbicides. A selective herbicide controls specific weed species, while leaving the desired crop relatively unharmed. In contrast, a non-selective herbicides, e.g. called total weed killers, kill all plant material with which they come into contact. A herbicide may be at least one one of the following, but is not limited thereto: acetamides, amides, aryloxyphenoxypropionates, benzamides, benzofuran, benzoic acids, benzothiadiazinones, bipyridylium, carbamates, chloroacetamides, chlorocarboxylic acids, cyclohexanediones, dinitroanilines, dinitrophenol, diphenyl ether, glycines, imidazolinones, isoxazoles, isoxazolidinones, nitriles, N- phenylphthalimides, oxadiazoles, oxazolidinediones, oxyacetamides, phenoxycarboxylic acids, phenylcarbamates, phenylpyrazoles, phenylpyrazolines, phenylpyridazines, phosphinic acids, phosphoroamidates, phosphorodithioates, phthalamates, pyrazoles, pyridazinones, pyridines, pyridinecarboxylic acids, pyridinecarboxamides, pyrimidinediones, pyrimidinyl(thio)benzoates, quinolinecarboxylic acids, semicarbazones, sulfonylaminocarbonyltriazolinones, sulfonylureas, tetrazolinones, thiadiazoles, thiocarbamates, triazines, triazinones, triazoles, triazolinones, triazolocarboxamides, triazolopyrimidines, triketones, uracils, ureas. Further, a herbicide may be, but are not limited thereto, lipid biosynthesis inhibitors, acetolactate synthase inhibitors (ALS inhibitors), photosynthesis inhibitors, protoporphyrinogen-IX oxidase inhibitors, bleacher herbicides, enolpyruvyl shikimate 3-phosphate synthase inhibitors (EPSP inhibitors), glutamine synthetase inhibitors, 7,8-dihydropteroate synthase inhibitors (DHP inhibitors), mitosis inhibitors, inhibitors of the synthesis of very long chain fatty acids (VLCFA inhibitors), cellulose biosynthesis inhibitors, decoupler herbicides, auxinic herbicides, auxin transport inhibitors, and/or other herbicides selected from the group consisting of bromobutide, chlorflurenol, chlorflurenol-methyl, cinmethylin, cumyluron, dalapon, dazomet, difenzoquat, difenzoquat- metilsulfate, dimethipin, DSMA, dymron, endothal and its salts, etobenzanid, flamprop, flamprop-isopropyl, flamprop-methyl, flamprop-M-isopropyl, flamprop-M-methyl, flurenol, flurenol-butyl, flurprimidol, fosamine, fosamine-ammonium, indanofan, indaziflam, maleic hydrazide, mefluidide, metam, methiozolin, methyl azide, methyl bromide, methyl-dymron, methyl iodide, MSMA, oleic acid, oxaziclomefone, pelargonic acid, pyributicarb, quinoclamine, tetflupyrolimet, triaziflam, tridiphane, and their agriculturally acceptable salts, amides, Isoxaflutole, Flufenacet, S-Metolachlor, Pendimethalin, Acetochlor, Pyroxasulfone, Cloransulam-methyl, Imazamethayr, Dimethenamid-P, Metamitrion, Ethofumesate, Quimerac, Prosulfocarb, Chlortoluron, Cinmethylin, Pendimethalin, esters or thioesters.

The term soil property data of an agricultural field as used herein is to be understood broadly in the present case and presents any data defining, describing or indicating the properties of the soil. The soil property data may be provided, presented or depicted as a 2-dimensional map of the agricultural field. The soil property data may include soil property parameters like soil organic matter data, soil texture data, cation exchange capacity, total carbon content data, organic carbon content data, inorganic carbon content data, pH-value data of the soil, soil type data, soil texture data, soil temperature data, soil surface temperature data, soil density data, water holding capacity data, soil conductivity data and/or topography data of the agricultural field but is not limited thereto. The soil property parameters may be provided, determined or estimated by physical measurements, e.g. temperature measurements, and/or chemical measurements or chemical experiments/tests, e.g. determination of the pH-value. Further, the soil property parameter may include plant parameters like NDVI, LAI, biomass index or soil organic matter, but is not limited thereto. The soil property data may be historical data and/or current data. The soil property data may be provided by geophysical measurements/analysis and/or modelling but is not limited thereto.

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

The term pesticide product data as used herein is to be understood broadly in the present case and presents any data/information about the pesticide product. Exemplary, soil herbicide product data may include the name of the herbicide, the properties of the herbicide formulation in terms of the weed spectrum covered and in particular of the active ingredients, the application information with respect to the at least one soil property parameter and preferably a soil moisture. Further, the pesticide product data may comprise the application time/ season, i.e. summer, winter, autumn, and spring, information about environmental compatibility, sustainability and impact (e.g. buffer zones). Also warnings with respect to health of the user may be included in the pesticide product data.

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

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

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

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

The term pesticide application model as used herein is to be understood broadly in the present case and presents any model, i.e. numerical, statistical, forward, backward, prediction model, being usable for modelizing the pesticide application. The pesticide application model may be a trainable model.

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

The term application device used herein is to be understood broadly in the present case and represents any device being configured to provide/spread seeds, plants and/or fertilizers onto the soil of an agricultural field. The application device may be configured to traverse the agricultural field. The application device may be a ground or an air vehicle, e.g. a rail vehicle, a robot, an aircraft, an unmanned aerial vehicle (UAV), a drone, or the like. The application device can be an autonomous or a non-autonomous application device.

In an embodiment of the method for providing variable pesticide application data, the pesticide product is an herbicide product, preferably a soil herbicide product.

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

In an embodiment of the method for providing variable pesticide application data, providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on precipitation data of the agricultural field, wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the precipitation data.

In an embodiment of the method for providing variable pesticide application data, providing variable pesticide application data for applying a pesticide product on the agricultural field further comprises determining whether a soil moisture value and/or a precipitation value of the agricultural field is equal or below a predefined threshold and in case the determined soil moisture value and/or precipitation value is equal or below the predefined threshold, providing pesticide application data indicating non-application data.

In an embodiment of the method for providing variable pesticide application data, in a first step pesticide product data for different/pre-selected pesticide products may be provided comprising information and/or recommendations about soil moisture values and/or precipitation values required/beneficial for a sufficient efficacy of the pesticide product. In a further step, a soil moisture value and/or a precipitation value may be provided/determined, e.g. based on weather and/or sensor data. Based on the soil moisture value and/or a precipitation value it may be determined whether or not the agricultural field may be treated at all, e.g. in case the soil moisture value and/or a precipitation value is very low. In case, the soil moisture value and/or the precipitation value is sufficient high, it may be determined which pesticide products might be applied in view of the soil moisture value and/or a precipitation value. In this respect, different pesticide products may be provided for different soil moisture values ranges and/or precipitation value ranges.

In an embodiment of the method for providing variable pesticide application data, the variable pesticide application data comprises at least one of the following: application time data comprising at least one time window for applying a pesticide product on the agricultural field; suitability data about at least one pesticide product suitable for an application on the agricultural field at least based on the soil property data and preferably the soil moisture data; dose rate data comprising at least one dose rate for applying a pesticide product on the agricultural field, wherein dose rates for applying a pesticide product are preferably provided for sub-areas of the agricultural field; spatial variation data related to sub-field areas of the agricultural field; and/or at least one pesticide application map.

In an embodiment of the method for providing variable pesticide application data, the variable pesticide application data comprises: combined application data comprising variable pesticide application data for applying a pesticide product on the agricultural field and spot application data for applying a further agricultural product. Such a spot application may be performed as so called on/off application or as a variable application of the further agricultural product. The further agricultural product may be a further herbicide, a foliar herbicide, a fungicide, etc. The spatial information where such a spot application may be applied on the agricultural field may be provided by means of sensing means, e.g. remote sensing means and/or device/machine mounted sensor means, e.g. cameras. The sensor data for such a spot application may be acquired before or also directly while driving through the agricultural field.

In an embodiment of the method for providing variable pesticide application data, the soil property data comprises at least one of the following soil property parameters: soil organic matter data and/or soil texture data; total carbon content data, organic carbon content data, inorganic carbon content data and/or pH-value data of the soil; soil type data, soil texture data, soil temperature data, soil surface temperature data, soil density data, and/or water holding capacity data of the soil in the agricultural field; soil conductivity data; and/or topography data of the agricultural field.

In an embodiment of the method for providing variable pesticide application data, the pesticide product is a soil pesticide product and the soil herbicide products are, inter alias, Isoxaflutole, Flufenacet, Dimethenamid-P, S-Metolachlor, Pendimethalin, Aclonifen, Acetochlor, Pyroxasulfone, Cloransulam-methyl, Imazamethayr, Dimethenamid-P, Metamitrion, Ethofumesate, Quimerac, Prosulfocarb, Chlortoluron, Cinmethylin, Pendimethalin and/or a combination thereof. Generally, the term soil herbicide product is not limited to herbicide products which are only absorbed via the roots of the plants (i.e. are active via the soil), this term also includes herbicide products that are at least partially active via the soil. Preferably, the herbicide products are significantly active via the soil, e.g. are absorbed via the roots of the plant.

In an embodiment of the method for providing variable pesticide application data, the method further comprises the step of providing weed distribution data comprising actual weed distribution data and/or historical weed distribution data, wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the weed distribution data. By including the weed distribution data into the providing/determination of the variable pesticide application data, an efficient, sustainable and robust way for providing variable pesticide application data can be provided. Therefore, the effectivity of the application of the pesticide products can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved.

In an embodiment of the method for providing variable pesticide application data, the method further comprises the step of providing crop data comprising information about crops planned to be planted on the agricultural field, wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the crop data. By including the crop data into the providing/determination of the variable pesticide application data, an efficient, sustainable and robust way for providing variable pesticide application data can be provided. Therefore, the effectivity of the application of the pesticide can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved.

In an embodiment of the method for providing variable pesticide application data, the method further comprises the step of providing historical treatment data comprising information about treatments performed in previous seasons on the agricultural field, preferably comprising information about the mode of the historical treatment action as factor in resistance management, wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the historical treatment data. By including the historical treatment data into the providing/determination of the variable pesticide application data, an efficient, sustainable and robust way for providing variable pesticide application data can be provided. Therefore, the effectivity of the application of the pesticide can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved.

In an embodiment of the method for providing pesticide herbicide application data, when providing the variable pesticide application data, the available input data are weighted and/or a trained pesticide application model is used to provide the variable pesticide application data. A weighting of input parameters and/or the use of a trained soil herbicide application model enables a highlighting of several factors/data being more important for the variable pesticide application data. Therefore, an efficient, sustainable and robust way for providing variable pesticide application data can be provided and the effectivity of the application of the pesticide products can be increased. Further, unnecessary treating and/or over treatment of the agricultural field can be avoided, and money and amounts of treating products can be saved.

In an embodiment of the method for providing variable pesticide application data, the method further comprises the step of providing control data for at least one pesticide application device for applying a pesticide on the agricultural field. The providing of control data enables a semiautomatic or fully automatic control of the application device, wherein the control data e.g. includes the drive control and the control of application of pesticide products. Thereby, man power and costs can be reduced and the degree of automation on the agricultural fields can be increased.

BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

Figure 3 illustrate an example embodiment of a distributed computing environment;

Figure 4 illustrates a flow diagram of a computer-implemented method for providing variable soil herbicide application data;

Figure 5 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 4;

Figure 6 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 5;

Figure 7 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 6; Figure 8 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 7;

Figure 9 illustrates a schematic illustration of a system for providing variable soil herbicide application data;

Figure 10 illustrates an optional workflow for a treatment decision and a treatment product selection based on the predicted cumulative precipitation amount;

Figure 11 illustrates an exemplary workflow for determining variable rate application data for the treatment of an agricultural field with a soil herbicide product;

Figure 12 illustrates a further exemplary workflow to determine variable rate application data for the treatment of an agricultural field with a soil herbicide product;

Figure 13 illustrates a schematic illustration of a variable application of a soil herbicide product and a further agricultural product at different application times;

Figure 14 illustrates a schematic illustration of a variable application of a soil herbicide product and a further agricultural product at the same application time; and

Figure 15 illustrates exemplarily the different possibilities to receive and process field data.

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. Notably, in the following examples, the pesticide product is a soil herbicide product. However, the present disclosure is not limited to such soil herbicide product, which is used in the following embodiments only as an explanatory example.

Figures 1 to 3 illustrate different computing environments, central, decentral and distributed. The methods, apparatuses, computer elements of this disclosure may be implemented in decentral or at least partially decentral computing environments. In particular, for data sharing or exchange in ecosystems of multiple players different challenges exist. Data sovereignty may be viewed as a core challenge. It can be defined as a natural person’s or corporate entity’s capability of being entirely self-determined with regard to its data. To enable this particular capability related aspects, including requirements for secure and trusted data exchange in business ecosystems, may be implemented across the chemical value chain. In particular, chemical industry requires tailored solutions to deliver chemical products in a more sustainable way by using digital ecosystems. Providing, determining or processing of data may be realized by different computing nodes, which may be implemented in a centralized, a decentralized or a distributed computing environment.

Figure 1 illustrates an example embodiment of a centralized computing system 20 comprising a central computing node 21 (filled circle in the middle) and several peripheral computing nodes 21.1 to 21. n (denoted as filled circles in the periphery). The term “computing system” is defined herein broadly as including one or more computing nodes, a system of nodes or combinations thereof. The term “computing node” is defined herein broadly and may refer to any device or system that includes at least one physical and tangible processor, and/or a physical and tangible memory capable of having thereon computer-executable instructions that are executed by a processor. Computing nodes are now increasingly taking a wide variety of forms.

Computing nodes may, for example, be handheld devices, production facilities, sensors, monitoring systems, control systems, appliances, laptop computers, desktop computers, mainframes, data centers, or even devices that have not conventionally been considered a computing node, such as wearables (e.g., glasses, watches or the like). The memory may take any form and depends on the nature and form of the computing node.

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

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

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

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

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

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

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

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

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

Figure 3 illustrates an example embodiment of a distributed computing environment 40. In this description, “distributed computing” may refer to any computing that utilizes multiple computing resources. Such use may be realized through virtualization of physical computing resources. One example of distributed computing is cloud computing. “Cloud computing” may refer a model for enabling on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). When distributed, cloud computing environments may be distributed internationally within an organization and/or across multiple organizations. In this example, the distributed cloud computing environment 40 may contain the following computing resources: mobile device(s) 42, applications 43, databases 44, data storage and server(s) 46. The cloud computing environment 40 may be deployed as public cloud 47, private cloud 48 or hybrid cloud 49. A private cloud 47 may be owned by an organization and only the members of the organization with proper access can use the private cloud 48, rendering the data in the private cloud at least confidential. In contrast, data stored in a public cloud 48 may be open to anyone over the internet. The hybrid cloud 49 may be a combination of both private and public clouds 47, 48 and may allow to keep some of the data confidential while other data may be publicly available.

Figure 4 illustrates a flow diagram of a computer-implemented method for providing variable soil herbicide application data.

In a first step, the soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field is provided. The soil property data are provided indicating soil property parameters like soil organic matter data, soil texture data, cation exchange capacity, total carbon content data, organic carbon content data, inorganic carbon content data, and pH- value data of the soil. The soil property data may be provided by a soil model having data of geophysical measurements as an input. In an optional second step, soil moisture data of the agricultural field are provided. The moisture data may be provided by remote sensing data, in particular by satellite remote sensors, in a 2- dimensional map having the unit of the water content/moisture content of g/m³.

In a third step, the soil herbicide product data are provided. The soil herbicide product data include the name of the herbicide, the properties of the herbicide, in particular of the active ingredients, and the application information with respect to the at least one soil property parameter and optionally a soil moisture. The soil herbicide product data are provided/queried from a database and/or data system.

In a fourth step, the variable soil herbicide application data for applying a soil herbicide product on the agricultural field are provided. The variable soil herbicide application data are provided/determined at least based on the soil property data, preferably the soil moisture data, and the soil herbicide product data.

Figure 5 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 4.

Beside the steps of Figure 4, the further embodiment of the computer implemented method as depicted in Figure 5 comprises a further step of providing weed distribution data and including the weed distribution data into the providing/determination of the variable soil herbicide application data. The weed distribution data are historical data and indicates high-density area of weed on the agricultural field. The weed distribution data are provided by camera based analysis methods.

Figure 6 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 5.

Beside the steps of Figure 5, the further embodiment of the computer implemented method as depicted in Figure 6 comprises a further step of providing crop data and including the crop data into the providing/determination of the variable soil herbicide application data. The crop data are provided by a user via a user interface and includes information about the crops planned to be planted on the agricultural field.

Figure 7 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 6. Beside the steps of Figure 6, the further embodiment of the computer-implemented method as depicted in Figure 7 comprises a further step of providing historical treatment data, wherein the historical treatment data are included into the providing of the variable soil herbicide application data. The historical treatment data are provided as a 2-dimensional map of the treatment being made in the last agricultural season and are provided by a database system.

Figure 8 illustrates a flow diagram of a further computer-implemented method for providing variable soil herbicide application data of Figure 7.

Beside the steps of Figure 7, the further embodiment of the computer implemented method as depicted in Figure 8 comprises a further step of providing control data for at least one soil herbicide application device for applying a soil herbicide on the agricultural field. The control data are provided by a control data providing unit based on the provided variable soil herbicide application data.

Figure 9 illustrates a schematic illustration of a system 10 for providing variable soil herbicide application data.

The system 10 for providing variable soil herbicide application data for applying a soil herbicide product on an agricultural field comprises a providing unit 11 for providing soil property data, preferably a providing unit 12 for providing soil moisture data, a providing unit 13 for providing soil herbicide product data, and a providing unit 14 for providing variable soil herbicide application data. The providing unit 11 is configured to provide variable soil property data, receives the soil property data from a data source, e.g. from a measurement, a database and/or a data system, and provides the soil property data to the system 10 for further proceeding. The optional providing unit 12 is configured to provide the soil moisture data. The providing unit 12 receives the soil moisture data, e.g. from satellite meta data, and provides the soil moisture data to the system 10 for further proceeding. The providing unit 13 is configured to provide variable soil herbicide product data. The providing unit 13 receives the soil herbicide product data from a database and/or a data system and provides the soil herbicide product data to the system 10 for further proceeding. The providing unit 14 is configured to provide/determine variable soil herbicide application data. The variable soil herbicide application data are provided/determined based on the soil property data, preferably the soil moisture data, and the soil herbicide product data. The providing unit 14 provides the provided variable soil herbicide application data to the system for further proceeding. Figure 10 illustrates an optional exemplary workflow for a treatment decision and a treatment product selection based on the predicted cumulative precipitation amount within the next 5 days. Time window, thresholds for precipitation amount, data layers and treatment products are only illustrative and may vary depending on field characteristics, region, soil properties, treatment product category, weather conditions and/or climatic setting. The treatment decision and the treatment product selection can also be based on soil property data and/or the current and predicted soil moisture. As shown in Figure 10, in a first step weed spectrum data are gathered and a pre-selection of suitable soil herbicide products for such the determined weed spectrum is provided. In a further step, weather data for the agricultural field is provided and the predicted cumulative precipitation amount for the next five days is determined. Based on this cumulative precipitation amount, it is decided whether or not the agricultural field is to be treated. For example, in case the cumulative precipitation amount is below a predefined amount, e.g. here 6 mm, it is decided not to treat the agricultural field, since the efficacy of soil herbicides dependents on a sufficient precipitation to transport the soil herbicides to the roots of the plants. In case, the cumulative precipitation amount is above the predefined amount, it can further be decided to select a specific soil herbicide dependent on the cumulative precipitation amount. In the shown example, it is decided to select an amid-based soil herbicide, if the cumulative precipitation amount is not between 6 and 20 mm and to select a dimethenamid based soil herbicide, if the cumulative precipitation amount is between 6 and 20 mm.

Figure 11 illustrates an exemplary workflow for determining variable rate application data for the treatment of an agricultural field with a soil herbicide product. This workflow can be performed following the workflow shown in Figure 10. In this example, the rate at which a treatment product is applied is based on soil texture and soil organic matter (SOM). Treatment rates, SOM thresholds and soil texture categories are only illustrative and may vary depending on field characteristics, region, soil properties, treatment product category, weather conditions and/or climatic setting. The variable rate application data can also be based on other soil properties and/or on weed distribution data to indicate parts of an agricultural with problematic weeds or a high density of weeds (“hot spots”). As shown, based on the provided soil texture data and soil organic matter data, respective amounts of the soil herbicide product are determined.

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

• SOM < 1.5% - 1.4 l/ha;

• 1.5% < SOM < 3% - 1.7 l/ha; and

• SOM > 3% - 2 l/ha. In case, the soil texture has been determined as “fine”, the following exemplary application/treatment rates are provided for the variable application of the soil herbicide product:

• SOM < 1.5% - 1.7 l/ha; and

• SOM > 1.5% - 2.0 l/ha.

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

Figure 12 illustrates an exemplary workflow to determine variable rate application data for the treatment of an agricultural field with a soil herbicide product. In this example, the variable rate application data is determined by soil texture data, soil organic matter data and optional weed distribution data to indicate parts of the agricultural field with a high density of weeds (“hot spots”). Two sample points indicate locations in the agricultural field where soil texture and soil organic matter content are the same, but where the weed distribution is different which is why the final treatment rate is different as indicated by the variable rate application data. The variable application rate data can also be represented as a grid that corresponds to the nozzles or sections of a sprayer. The soil herbicide can be applied separately according to the variable rate data or in combination with the application of a foliar herbicide. The shown values, categories and thresholds are only illustrative and may vary depending on field characteristics, region, soil properties, treatment product category, weather conditions and/or climatic setting. Dashed lines indicate optional elements.

Figure 13 illustrates a schematic illustration of a combined application of a residual soil herbicide product by means of a variable application at a first time ti , e.g. in an early growth stage and a spot application of a second agricultural product, e.g. a foliar herbicide product, at a second time t2. Such a spot application may be performed as so called on/off application or as a variable spot application of the second agricultural product, e.g. the foliar herbicide product. In an example, the residual soil herbicide product is applied at BBCH 11/12 and the foliar herbicide product is applied at BBCH 15/16. In the shown example of the combined application, it is possible that “riskier” decisions can be made with respect to the application rates of the residual soil herbicide product, if the foliar herbicide product can, if necessary, compensate for a wrong decision with respect to the residual herbicide product. Risk in this context can mean, for example, that a too low application rate is selected for the residual soil herbicide product. If, for example, it turns out that the soil herbicide has been applied with an insufficient application rate, a certain compensation can be provided by a later spot application of a foliar herbicide.

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

Figure 14 illustrates a schematic illustration of a variable application of a residual soil herbicide product and a spot application of a foliar herbicide product at the same time. Both herbicide products may have reinforcing and/or complementing effects allowing a reduction of the required/needed amount of agricultural products for treating an agricultural field. Also here, finding the optimal quantities with respect to such reinforcing and/or complementing effects may be subject of optimizations and/or training models/algorithms. In an example, the variable application of the residual soil herbicide product and the spot application of the foliar herbicide product may be repeated several times at different times, e.g. in sequences at different plant growth stages.

For example, field data can be obtained by all kinds of agricultural equipment 300 (e.g. a tractor 300) as so-called as-applied maps by recording the application rate at the time of application. It is also possible that such agricultural equipment comprises sensors (e.g. optical sensors, cameras, infrared sensors, soil sensors, etc.) to provide, for example, a weed distribution map. It is also possible that during harvesting the yield (e.g. in the form of biomass) is recorded by a harvesting vehicle 310. Furthermore, corresponding maps/data can be provided by land-based and/or airborne drones 320 by taking images of the field or a part of it. Finally, it is also possible that a geo-referenced visual assessment 330 is performed and that this field data is also processed. Field data collected in this way can then be merged in a computing device 340, where the data can be transmitted and computed, for example, via any wireless link, cloud applications 350 and/or working platforms 360, wherein the field data may also be processed in whole or in part in the cloud application 350 and/or in the working platform 360 (e.g., by cloud computing).

Aspects of the present disclosure relates to computer program elements configured to carry out steps of the methods described above. The computer program element might therefore be stored on a computing unit of a computing device, which might also be part of an embodiment.

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

The present disclosure has been described in conjunction with a preferred embodiment as examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims. Notably, in particular, the any steps presented can be performed in any order, i.e. the present invention is not limited to a specific order of these steps. Moreover, it is also not required that the different steps are performed at a certain place or at one node of a distributed system, i.e. each of the steps may be performed at a different nodes using different equipment/data processing units.

In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims.

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

Claims

1. Computer-implemented method for providing pesticide application data for applying a pesticide product onto an agricultural field, comprising: providing soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; providing pesticide product data comprising at least information on the application of a pesticide product with respect to the at least one soil property parameter; providing variable pesticide application data for applying a pesticide product on the agricultural field at least based on the soil property data and the pesticide product data.

2. Computer-implemented method according to claim 1, further comprising: generating and/or providing control data for controlling a pesticide product application device based on the variable pesticide application data.

3. Computer-implemented method according to claim 1 or claim 2, wherein the pesticide product is an herbicide product, preferably a soil herbicide product.

4. Computer-implemented method according to claim 1, further comprising: providing soil moisture data of the agricultural field, wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the soil moisture data, wherein the soil moisture data is provided by/as: a soil moisture model at least based on historical weather data, actual weather data and/or predicted weather data; remote soil moisture sensing data, preferably provided from satellite data; proximal soil moisture sensing data, preferably provided by humidity/soil moisture sensors attached to an agricultural device; and/or a soil moisture model at least based on moisture measurements taken at various locations in the field and/or by built-in on-line soil sensors.

5. Computer-implemented method according to any one of the preceding claims, further comprising: providing precipitation data of the agricultural field, wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the precipitation data. Computer-implemented method according to any one of the preceding claims, further comprising: determining whether a soil moisture value and/or a precipitation value of the agricultural field is equal or below a predefined threshold and in case the determined soil moisture value and/or precipitation value is equal or below the predefined threshold, providing pesticide application data indicating non-application. Computer-implemented method according to any one of the preceding claims, wherein the variable pesticide application data comprises at least one of the following: application time data comprising at least one-time window for applying a pesticide product on the agricultural field; suitability data about at least one pesticide product suitable for an application on the agricultural field at least based on the soil property data and the soil moisture data; dose rate data comprising at least one dose rate for applying a pesticide product on the agricultural field, wherein dose rates for applying a pestivcide product are preferably provided for sub-areas of the agricultural field; spatial variation data related to sub-field areas of the agricultural field; at least one pesticide application map; and/or combined application data comprising variable pesticide application data for applying a pesticide product on the agricultural field and spot application data for applying a further agricultural product. Computer-implemented method according to any one of the preceding claims, wherein the soil property data comprises at least one of the following soil property parameters: soil organic matter data and/or soil texture data; total carbon content data, organic carbon content data, inorganic carbon content data and/or pH-value data of the soil; soil type data, soil texture data, soil temperature data, soil surface temperature data, soil density data, and/or water holding capacity data of the soil in the agricultural field; soil conductivity data; and/or topography data of the agricultural field.

9. Computer-implemented method according to any one of the preceding claims, wherein the pesticide product is a soil herbicide product which has a high residual activity via root uptake, wherein the soil herbicide product is preferably Isoxaflutole, Flufenacet, Aclonifen, Dimethenamid-P, S-Metolachlor, Pendimethalin, Acetochlor, Pyroxasulfone, Cloransulam- methyl, Imazamethayr, Dimethenamid-P, Metamitrion, Ethofumesate, Quimerac, Prosulfocarb, Chlortoluron, Cinmethylin, Pendimethalin and/or a combination thereof.

10. Computer-implemented method according to any one of the preceding claims, further comprising: providing weed distribution data comprising actual weed distribution data and/or historical weed distribution data; and wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the weed distribution data; and/or providing crop data comprising information about crops planned to be planted on the agricultural field; and wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the crop data; and/or providing historical treatment data comprising information about treatments performed in previous seasons on the agricultural field, preferably comprising information about the mode of the historical treatment action as factor in resistance management; and wherein providing variable pesticide application data for applying a pesticide product on the agricultural field is further based on the historical treatment data.

11. Computer-implemented method according to any one of the preceding claims, wherein, when providing the variable pesticide application data, the available input data are weighted and/or a trained pesticide application model is applied to provide the variable pesticide application data.

12. Application device for applying a pesticide product on an agricultural field, wherein the variable pesticide application data are provided by a method according to any one of claims 1 to 11.

13. An apparatus for providing variable pesticide application data for applying a pesticide product on an agricultural field, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: providing soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; - providing pesticide product data comprising at least information on the application of a pesticide product with respect to the at least one soil property parameter; providing variable pesticide application data for applying a pesticide product on the agricultural field at least based on the soil property data and the pesticide product data.

14. Use of soil property data, soil moisture data, pesticide product data, variable pesticide application data and/or control data for controlling a pesticide application device in a method according to any one of claims 1 to 11. 15. Computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the method according to any one of the claims 1 to 11 in an apparatus according to claim 13.