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WO2023094667A1 - Réduction d'application hors cible d'un produit agricole à un champ - Google Patents

Réduction d'application hors cible d'un produit agricole à un champ Download PDF

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Publication number
WO2023094667A1
WO2023094667A1 PCT/EP2022/083529 EP2022083529W WO2023094667A1 WO 2023094667 A1 WO2023094667 A1 WO 2023094667A1 EP 2022083529 W EP2022083529 W EP 2022083529W WO 2023094667 A1 WO2023094667 A1 WO 2023094667A1
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WO
WIPO (PCT)
Prior art keywords
data
treatment product
field
sprayer
target
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2022/083529
Other languages
English (en)
Inventor
Anja Simon
Tobias SCHWABEN
Christian Popp
Marcel Patrik KIENLE
Claude Taranta
Michael Krapp
Francis Paul DONALDSON
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BASF SE
Original Assignee
BASF SE
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BASF SE filed Critical BASF SE
Priority to EP22822175.0A priority Critical patent/EP4436374A1/fr
Publication of WO2023094667A1 publication Critical patent/WO2023094667A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
    • A01M7/0089—Regulating or controlling systems
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
    • A01B79/00—Methods for working soil
    • A01B79/005—Precision agriculture
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01C—PLANTING; SOWING; FERTILISING
    • A01C21/00—Methods of fertilising, sowing or planting
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01C—PLANTING; SOWING; FERTILISING
    • A01C23/00—Distributing devices specially adapted for liquid manure or other fertilising liquid, including ammonia, e.g. transport tanks or sprinkling wagons
    • A01C23/04—Distributing under pressure; Distributing mud; Adaptation of watering systems for fertilising-liquids

Definitions

  • the present invention relates to digital farming, and in particular to a computer-implemented method, an apparatus, a sprayer, a system, and a computer readable medium for reducing off- target application of a treatment product in a field.
  • a computer-implemented method for reducing off-target application of a treatment product to a field comprising: receiving data comprising sprayer configuration data of one or more sprayers in the field and geo-dependent environmental data in the field; receiving data comprising a target local application distribution of the treatment product, which is indicative of an area to which the treatment product is intended to be applied; providing a model relating the sprayer configuration data and the geo-dependent environmental data to a local application distribution of the treatment product; determining the local application distribution of the treatment product based on the model and the received data, wherein the determined local application distribution of the treatment product is indicative of an area which can be reached by the treatment product during a simulated application process under a condition defined by the sprayer configuration data and the geo-dependent environmental data; determining a off-target movement of the treatment product based on a difference between the determined local application distribution of the treatment product and the target local application distribution of the treatment product; determining one or more spray application parameters for reducing the determined off- target
  • a method is proposed to determine the application area of a treatment product based on digital available environmental data (e.g. wind direction, wind speed, temperature, moisture, etc.) or physical product data as well as sprayer configurations (e.g. nozzle type and - pressure, boom height, driving speed, etc.
  • the determined application area of the treatment product is compared to a target application area to determine possible off-target movement.
  • Control data can be generated based on the determined data, which are used to adjust the sprayer configurations or spray characteristics by use of e.g. drift-reducing agents such that the off-target movement is down to a minimal desired level.
  • the determined one or more spray application parameters may be provided to a farmer via a user interface such that the farmer can perform an action to achieve the desired minimal off-target levels.
  • a user interface such that the farmer can perform an action to achieve the desired minimal off-target levels.
  • the method as described herein may enable the farmer to effectively apply a treatment product by using the precision application technology and equipment. Through more accurate and precise spraying, reduction of off-target movement is possible. Therefore, a better performance and more targeted treatment application can be achieved, which is environmentally friendly.
  • the received data further comprises a physicochemical property of the treatment product.
  • a droplet size of the treatment product is determined based on the physicochemical property of the treatment product and the sprayer configuration data.
  • the received model further relates the droplet size of the treatment product to the local application distribution of the treatment product.
  • Data associated with physicochemical properties of the product may be retrieved from a database or the label of the product.
  • the database may be local or in the cloud.
  • the data associated with the product may comprise physicochemical properties, such as viscosity, surface tension of the product, wettability, technical properties, foam persistence, particle size distribution, emulsion droplet size, spontaneity of dispersion, suspensibility, also minimization of drift, volatility via tailor made DRA (drift reducing agents) respectively VRA (volatility reducing agents) agents, or any combination thereof.
  • the droplet size is a function of nozzle geometry, pressure of the chemical or biological chemicals at the nozzle, the surface tension of the chemical or biological chemical, and the viscosity of the chemical or biological chemicals and its dilution rate.
  • the droplet size may be determined by a droplet size model.
  • the inclusion of physicochemical properties as a further input of the model may improve the accuracy of determining the off-target movement, thereby allowing the sprayer to have a better performance and more targeted treatment application.
  • the received data further comprises crop data and/or weed data in the field.
  • the received model further relates the crop data and/or weed data to the local application distribution of the treatment product.
  • Crop data may comprise e.g. growth stage at specific time point, crop density, which may affect wind speed and therefore the off-target movement.
  • Weed data may comprise information on disease, pest, and/or weed pressures.
  • the received data further comprises geographical data of the field.
  • the received model further relates the geographical data of the field to the local application distribution of the treatment product.
  • Geographical data of the field may include, e.g. the exact position in the field, distance to buffer zones, distance to field edges. These parameters could decide on specific application parameters that will be selected during application as well as on the rate of the product that is used in dependent on the position of the field.
  • the received data further comprises a field description of the field.
  • the received model further relates the field description of the field to the local application distribution of the treatment product.
  • the field description may include e.g. inhomogeneity, such as increased moisture, drainage system, microclimate (e.g. temperature, drought, humidity), etc.
  • inhomogeneity such as increased moisture, drainage system, microclimate (e.g. temperature, drought, humidity), etc.
  • the step of determining one or more spray application parameters for reducing the determined off-target movement further comprises: comparing the determined off-target movement with a minimal desired off-target movement; and determining the one or more spray application parameters, and/or generating a warning signal indicating that an action should be taken, if the determined off-target movement is greater than or equal to the minimal desired off-target movement.
  • the optional control step may comprise the step of generating an objective function based on the maximum off-target movement and the determined off-target movement and minimizing the objective function by varying the drop size of the crop protecting chemicals.
  • the one or more sprayers in the field comprise a multi-tank system with a plurality of treatment products.
  • the one or more spray application parameters comprise a selection of a different treatment product or a different formulation to be applied to the field, based on an associated physicochemical property.
  • the sprayers may have one or more tanks. If the sprayer has a multi-tank system, it is possible to change the physicochemical properties by selecting a different treatment product. The change of the physicochemical properties can result in a change in the local application distribution of the treatment product.
  • the one or more spray application parameters comprises an adaption of water volume of the treatment product.
  • Water volume adaption can change the viscosity of the treatment product.
  • the change of the physicochemical properties of the treatment product can result in a change in the local application distribution of the treatment product.
  • the concentration of dilution can be adjusted or TM adjuvant (fluidifying or viscosifier) can be added.
  • the one or more spray application parameters comprise an adaption of sprayer configuration data.
  • the adaption of the sprayer configuration data comprises one or more of: an adaption of pressure at a nozzle; an adaption of speed of the one or more sprayers; an adaption of height of the boom of the one or more sprayers; and a selection of a different nozzle if the one or more sprayers comprise a revolver of different nozzles the use of a hooded sprayer.
  • Based on the droplet size model and the varied pressure at the nozzle a new droplet size may be determined. The new determined droplet size may then be used to determine the off-target movement based on the off-target movement model.
  • Varying the speed of the sprayer and or the height of the nozzle, the off-target movement may then be determined based on the varied speed of the sprayer and/or or the height of the nozzle, and/or another nozzle. This may be in particular applicable if the sprayer comprises a revolver of different nozzles or if the sprayer is equipped with a PWM (PulseWidthModulation) system.
  • PWM PulseWidthModulation
  • the model is in a cloud environment.
  • the received data is provided to the model in the cloud environment.
  • the control data is provided by the model in the cloud environment.
  • an apparatus for reducing off-target application of a treatment product to a field comprising one or more processing unit(s) to generate control data or providing one or more spray application parameters, wherein the processing unit(s) include instructions, which when executed on the one or more processing unit(s) perform the method steps of any one of the preceding claims.
  • a sprayer which comprises a sprayer unit and a control unit configured to receiving and implementing control data generated according to the method according to the first aspect and any associated example.
  • a system which comprises an apparatus according to the second aspect and one or more sprayers according to the third aspect.
  • a computer program product comprising instructions which, when the program is executed by at least one processing unit, cause the at least one processing unit to carry out the steps of the method according to the first aspect and any associated example.
  • the term “field”, also referred to as agricultural field, may be understood to be any area in which organisms, particularly crop plants, are produced, grown, sown, and/or planned to be produced, grown or sown.
  • Crops plants may be Allium cepa, Ananas comosus, Arachis hypogaea, Asparagus officinalis, Avena sativa, Beta vulgaris spec, altissima, Beta vulgaris spec, rapa, Brassica napus var. napus, Brassica napus var. napobrassica, Brassica rapa var.
  • Theobroma cacao Trifolium pratense, Triticum aestivum, Triticale, Triticum durum, Vicia faba, Vitis vinifera and Zea mays.
  • Most preferred crops are Arachis hypogaea, Beta vulgaris spec, altissima, Brassica napus var.
  • Triticale Triticum aestivum
  • Triticum durum Triticum durum
  • Vicia faba Vitis vinifera and Zea mays
  • bus are not limited thereto.
  • Especially preferred crops are crops of cereals, corn, soybeans, rice, oilseed rape, cotton, potatoes, vegetable crops, peanuts or permanent crops.
  • off-target movement is generally divided into primary loss (direct loss from the application equipment before reaching the intended target) and secondary loss (indirect loss from the treated plants and/or soil) categories, wherein the off-target loss can occur through a variety of mechanisms.
  • Primary loss from spray equipment typically occurs as fine dust or spray droplets that take longer to settle and can be more easily blown off-target by wind.
  • Off-target movement of spray particles or droplets is typically referred to as ‘spray drift’.
  • Primary loss can also include when contaminated equipment is used to make an inadvertent application to a sensitive crop. Contamination may occur when one treatment product (i.e.
  • Off-target movement of a pesticide is not adequately cleaned from spray equipment and the contaminated equipment is later used to apply a different product to a sensitive crop resulting in crop injury.
  • Secondary loss describes off-target movement of a pesticide after it contacts the target soil and/or foliage and moves from the treated surface by means including airborne dust (e.g. crystalline pesticide particles or pesticide bound to soil or plant particles), volatility (i.e. a change of state from the applied solid or liquid form to a gas), or run-off in rain or irrigation water.
  • Off-target movement is typically mitigated by proper application technique (e.g. spray nozzle selection, nozzle height and wind limitations) and improved pesticide formulation.
  • treatment product may refer to any material useful for any kind of treatment possible on an agricultural field, including but not limited to seeding, fertilization, crop protection, growth regulation, harvesting, adding or removing of organisms - particularly crop plants - , as well as soil treatment, soil nutrient management, soil nitro-gen management, tilling, ploughing, and irrigation.
  • the treatment product may be applied as such or diluted. Further examples of the treatment product will be provided hereinafter, and particular with respect to the example shown in Fig. 3.
  • target local application distribution may refer to an area to which the treatment product is intended to be applied.
  • the target local application distribution may refer to one or more areas in the field that may contain any organism to which the treatment product is intended to be applied and to which the treatment product has a real-world impact (e.g. killing, removing, controlling, nutrient-providing effect, etc.). If two or more treatment products are intended to be applied to the field, the target local application distribution may further specify a respective area to which each treatment product is intended to be applied.
  • the target local application distribution of the treatment product may be provided by a user (e.g. farmer) via a user interface.
  • the user may select locations where a particular treatment (e.g. killing, removing, controlling, nutrient-providing effect, etc.) is desired and define these locations as the target local application distribution of the treatment product.
  • the target local application distribution of a treatment product may be derived from a field metadata that comprises data describing a particular field.
  • the target local application distribution of the treatment product may be derived from the information about previous treatments on the field.
  • the field metadata may comprise information about crop present on the field (e.g. indicated with crop ID), the crop rotation, the location of the field, previous treatments on the field, planned treatment time on the field, sowing time, etc.
  • the field metadata may be obtained from a database that stores the information about the field.
  • the term “determined local application distribution” may also be referred to as modelled, simulated, or virtual local application distribution of the treatment product.
  • the determined local application distribution of the treatment product is generated using the model that relates the received sprayer configuration data and the received geo-dependent environmental data to a local application distribution of the treatment product.
  • the determined local application distribution is indicative of an area, which can be reached by the treatment product during a simulated application process under a condition defined by the sprayer configuration data and the geo-dependent environmental data.
  • the term “off-target application”, also referred to as off-target movement may be understood as displacement of the treatment product during and after application relative to a target area to which the treatment product is intended to be applied.
  • the off-target application is determined based on the difference between the determined local application distribution and the target local application distribution.
  • the determined off-target application is thus indicative of displacement of the treatment product during a simulated application process e.g. under a condition defined by the sprayer configuration data and the geo-dependent environmental data relative to a target area to which the treatment product is intended to be applied.
  • the off-target application in general reflects the off-target movement, which is the movement of droplets of the treatment product through the air, during or after application, to a site other than the intended target area. Although off-target movement may occur as vaporized treatment product from the application site, it is usually the physical movement of very small drops from the target area at the time of application.
  • the term “sprayer”, also referred to as agricultural sprayer, may refer to the equipment used for applying liquid substances to agricultural crops. These liquid substances could be fertilizer, herbicides, pesticides, or any aqueous solutions that are made by dissolving liquids.
  • the sprayer may be releasably attached or directly mounted to a ground-based platform (e.g. tractor pulled or self-propelled sprayer shown in Fig. 6) or to a non-ground-based platform (e.g. drone, aircraft, helicopter).
  • the sprayer machine typically includes a storage tank or multiple tanks for the liquid to be applied, the tank being filled as required by the operator.
  • the sprayer machine may be semipermanently connected by a pipe to a local (field-based) browser wherein the applied liquid is supplied via a pipe from the browser to the sprayer continuously as the latter is repeatedly moved across the crop field.
  • the sprayer can be as well a ground-based autonomous vehicle
  • the term “the physicochemical properties of the treatment product” may refer to the chemical and physical properties of agricultural spray liquids, which can affect droplet size and, therefore, off-target movement.
  • the physicochemical properties of the treatment product may include, but are not limited to, viscosity, Henry-coefficient, static surface tension, dynamic surface tension, vapor pressure, concentrations, potential mixing partners and other ingredients in the spray solution, density, evaporation rate of the fluid.
  • the physicochemical properties of the treatment product may refer to the physicochemical characteristics of the complete spray mixture (e.g. herbicide formulation plus adjuvant) that are off-target movement-determining.
  • the physicochemical properties may have a strong effect on the off-target movement.
  • physiochemical properties like the viscosity affect strongly the surface tension of a treatment product, such that the droplet size and therefore the weight of the droplets of the treatment product can be e.g. increased.
  • the weight or size of the droplets significantly affects the off-target movement, i.e. a reduction makes the off-target movement, both in e.g. windy and non-windy environmental situations, greater.
  • the physicochemical properties are not limited to the above mentioned example.
  • the term “sensor” may be understood to be any kind of physical or virtual device, module or machine capable of detecting or receiving real-world information and sending this real-world information to another system, including temperature sensor, humidity sensor, moisture sensor, pH sensor, pressure sensor, soil sensor, crop sensor, water sensor, and cameras.
  • Fig. 1 illustrates a typical environmental setup.
  • Fig. 2 illustrates a block diagram of an exemplary apparatus for reducing off-target application of a treatment product to a field.
  • Fig. 3 illustrates a flow chart describing a computer-implemented method for reducing off-target application of a treatment product to a field.
  • Fig. 4 schematically shows a data flow in the apparatus.
  • Fig. 5 illustrates a flow chart describing a further computer-implemented method for reducing off-target application of a treatment product to a field.
  • Fig. 6 schematically illustrates a treatment management system.
  • Fig. 7 illustrates an example of a smart sprayer system for controlling application of a treatment product to a field.
  • Fig. 1 illustrates a typical environmental setup including a field 100, which is surrounded by a buffer zone 200, where the application of a treatment product is forbidden, and a protected environment 300.
  • the term “field” is also referred to as agricultural field, which is understood to be any area in which organisms, particularly crop plants, are produced, grown, sown, and/or planned to be produced, grown or sown.
  • Off-target movement in application of a treatment product may be critical. Extensive off-target movement may lead to pollution of areas (e.g. the buffer zone 200 shown in Fig. 1) where application of the treatment product is prohibited.
  • an apparatus and a method is proposed to reduce off-target application a treatment product to a field, which may allow use of the treatment product on the largest field area without contaminating the buffer zones
  • Fig. 2 illustrates a block diagram of an exemplary apparatus 10 for reducing off-target application of a treatment product to a field.
  • the apparatus 10 includes an input unit 12, one or more processing units 14, and an output unit 16.
  • the apparatus 10 may comprise various physical and/or logical components for communicating and manipulating information, which may be implemented as hardware components (e.g., computing devices, processors, logic devices), executable computer program instructions (e.g., firmware, software) to be executed by various hardware components, or any combination thereof, as desired for a given set of design parameters or performance constraints.
  • hardware components e.g., computing devices, processors, logic devices
  • executable computer program instructions e.g., firmware, software
  • the apparatus 10 may be embodied as, or in, a device or apparatus, such as a server, workstation, or mobile device.
  • the apparatus 10 may comprise one or more microprocessors or computer processors, which execute appropriate software.
  • the processing unit 14 of the apparatus 10 may be embodied by one or more of these processors.
  • the software may have been downloaded and/or stored in a corresponding memory, e.g., a volatile memory such as RAM or a non-volatile memory such as flash.
  • the software may comprise instructions configuring the one or more processors to perform the functions described herein.
  • the apparatus 10 may be implemented with or without employing a processor, and also may be implemented as a combination of dedicated hardware to perform some functions and a processor (e.g., one or more programmed microprocessors and associated circuitry) to perform other functions.
  • a processor e.g., one or more programmed microprocessors and associated circuitry
  • the functional units of the apparatus 10, e.g., the input unit 12, the one or more processing units 14, and the output unit 16 may be implemented in the device or apparatus in the form of programmable logic, e.g., as a Field- Programmable Gate Array (FPGA).
  • FPGA Field- Programmable Gate Array
  • each functional unit of the apparatus may be implemented in the form of a circuit.
  • the apparatus 10 may also be implemented in a distributed manner.
  • some or all units of the apparatus 10 may be arranged as separate modules in a distributed architecture and connected in a suitable communication network, such as a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) network, Internet, LAN (Local Area Network), Wireless LAN (Local Area Network), WAN (Wide Area Network), and the like.
  • 3GPP 3rd Generation Partnership Project
  • LTE Long Term Evolution
  • Internet such as a 3rd Generation Partnership Project (LTE) network
  • LAN Local Area Network
  • Wireless LAN Local Area Network
  • WAN Wide Area Network
  • the processing unit(s) 14 may execute instructions to perform the method described herein, which will be explained in detail with respect to the embodiment shown in Fig. 3.
  • the input unit 12 and the output unit 14 may include hardware and/or software to enable the apparatus 10 to receive a data input, and to communicate with other devices and/or a network.
  • the input unit 12 may receive the data input via a wired connection or via a wireless connection.
  • the output unit 16 may also provide cellular telephone communications, and/or other data communications for the apparatus 10.
  • Fig. 3 illustrates a flow chart describing a computer-implemented method 400 for reducing off- target application of a treatment product to a field.
  • the treatment product may be any material useful for any kind of treatment possible on an agricultural field, including but not limited to seeding, fertilization, crop protection, growth regulation, harvesting, adding or removing of organisms - particularly crop plants - , as well as soil treatment, soil nutrient management, soil nitro-gen management, tilling, ploughing, and irrigation.
  • the treatment product may be applied as such or diluted.
  • the treatment product may include chemical products, such as fungicide, herbicide, insecticide, acaricide, molluscicide, nematicide, avicide, piscicide, rodenticide, repellant, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof.
  • chemical products such as fungicide, herbicide, insecticide, acaricide, molluscicide, nematicide, avicide, piscicide, rodenticide, repellant, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof.
  • the treatment product may include biological products, such as microorganisms useful as fungicide (biofungicide), herbicide (bioherbicide), insecticide (bioinsecticide), acaricide (bioacaricide), molluscicide (biomolluscicide), nematicide (bionematicide), avicide, piscicide, rodenticide, repellant, pheromone, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof.
  • biological products such as microorganisms useful as fungicide (biofungicide), herbicide (bioherbicide), insecticide (bioinsecticide), acaricide (bioacaricide), molluscicide (biomolluscicide), nematicide (bionematicide), avicide, piscicide, rodenticide, repellant, pheromone, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor
  • the treatment product may include biostimulants, which may contain substance(s) and/or micro-organisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance/benefit nutrient uptake, nutrient efficiency, tolerance to abiotic stress, and crop quality.
  • biostimulants may include amino acids, seaweed-based products, humic and fulvic acids, or any combination thereof.
  • the treatment product may include fertilizer and nutrient, seed and seedling, water, or any combination thereof.
  • the treatment product may include adjuvants (built-in or tank-mixed) like anti-rebouncing agents, spreaders, uptake enhancers or water-conditioners or any combination thereof.
  • adjuvants built-in or tank-mixed
  • the treatment product may include a combination of different products as described above.
  • a device such as apparatus 10 as illustrated in Fig. 2, receives data comprising sprayer configuration data of one or more sprayers in the field and geo-dependent environmental data in the field.
  • the device may be embodied as, or in, a mobile device, which may include any type of wireless device such as consumer electronics devices, smart phones, tablet personal computers, wearable computing devices, personal digital assistants (PDAs), laptop computers, and/or any other like physical computing device that is able to connect to a communications network.
  • a mobile device may include any type of wireless device such as consumer electronics devices, smart phones, tablet personal computers, wearable computing devices, personal digital assistants (PDAs), laptop computers, and/or any other like physical computing device that is able to connect to a communications network.
  • the device may be embodied as, or in, a workstation or a remote server.
  • the remote server refers to one or more computers or one or more computer servers that are located away from the farm or the geographical location.
  • the remote server is operated by a supplier of at least a part of the field specific data.
  • the remote server may thus be located several kilometres or more from the geographical location.
  • the remote server may even be located in a different country.
  • the remote server may even be at least partially implemented as a cloud based service or platform.
  • the term may even refer collectively to more than one computers or servers located on different locations.
  • the cloud-based service may be accessible from various client devices through a thin client interface such as a web browser, a mobile app, or a desktop app.
  • the received data comprises sprayer configuration data of one or more sprayers in the field.
  • the one or more sprayers may include ground sprayers and/or aerial sprayers.
  • Exemplary sprayer configuration data may include, but is not limited to, nozzle type, nozzle diameter, spray angle, nozzle orientation, nozzle height relative to ground, boom height, speed of the nozzle relative to the ground, spray solution viscosity, pressure, flow rate, with an optional fan, fan speed, fan orientation, and the formulation of the treatment product.
  • the spray configuration data of the one or more sprayers in the field may be provided by a user (e.g. a farmer) via a user interface.
  • the spray configuration data may be retrieved from the configuration file of the one or more sprayers in the field.
  • the received data further comprises geo-dependent environmental data in the field.
  • the geodependent environmental data may include, but is not limited to, wind direction, wind speed, temperature, moisture.
  • Environmental data may be derived in an on-line or off-line process.
  • the environmental data may comprise local-specific weather data, which may obtained from data sources, e.g. those that are available such as from weather services and Google Maps.
  • the environmental data may be gathered by the sensors in the field and transferred wirelessly to the device.
  • the sprayer configuration data and the geo-dependent environmental data may be transmitted to the device (e.g. mobile device, workstation, remote server, etc.) in any suitable way, such as in a suitable communication network, such as a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) network, Internet, LAN (Local Area Network), Wireless LAN (Local Area Network), WAN (Wide Area Network), and the like.
  • a suitable communication network such as a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) network, Internet, LAN (Local Area Network), Wireless LAN (Local Area Network), WAN (Wide Area Network), and the like.
  • the device may optionally receive one or more of the following data: crop data, weed/pathogen data, physicochemical properties of the treatment product, geographical data of the field, and field description. This will be explained hereinafter and in particular with respect to the example shown in Fig. 4.
  • the device receives data comprising a target local application distribution of the treatment product, which is indicative of an area to which the treatment product is intended to be applied. If two or more treatment products are intended to be applied to the field, the received data may comprise information indicating a respective area to which each treatment product is intended to be applied.
  • the target local application distribution of the treatment product may be provided by a user (e.g. farmer) via a user interface.
  • a user e.g. farmer
  • the user may select locations where a particular treatment (e.g. killing, removing, controlling, nutrient-providing effect, etc.) is desired and define these locations as the target local application distribution of the treatment product.
  • the target local application distribution of a treatment product may be derived from a field metadata that comprises data describing a particular field.
  • the target local application distribution of the treatment product may be derived from the information about previous treatments on the field.
  • the device provides a model that relates the sprayer configuration data and the geo-dependent environmental data to a local application distribution of the treatment product.
  • the model may be implemented in a client device, such as a mobile device or a workstation. In such cases, the model may be provided locally without the need to communicate with a remote server.
  • the model may be implemented in a remote server, e.g. on cloud computing that supports decentral processing.
  • Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.
  • the model is in a cloud environment, and the received data may be transmitted wirelessly to the model in the cloud environment.
  • the model may be a droplet distribution model to simulate ballistic or fluid dynamics of droplets and sprays.
  • the model may be a pre-trained machine-learning model.
  • the machinelearning model has been pre-trained on training data to learn a mapping between input examples and the target variable.
  • the training data may be obtained from historic data recorded in one or more fields.
  • Each input example may include particular spray configuration data and the geo-dependent environment data.
  • the corresponding target variable may include data indicative of a local application distribution of the treatment product sensed by one or more sensors in the field.
  • Training of the machine-learning model may include the following steps of receiving the training data, applying the machine-learning model to the training data, in one or more iterations. As a result, of this application the pre-trained machine-learning model is then obtained, which can then be used in deployment.
  • new sprayer configuration data and new geo-dependent environmental data can be applied to the pre-trained machine-learning model to obtain a predicted local distribution of the treatment product for this new data.
  • a neural-network model also referred to as artificial neural networks (AN Ns)
  • AN Ns artificial neural networks
  • other machine learning techniques such as support vector machines, maximum likelihood, random forest, or other may be used instead of neural networks.
  • the model may be a simulation model, a physical model or a prediction model, but is not limited thereto.
  • the device determines the local application distribution of the treatment product based on the model and the received data.
  • the determined local application distribution of the treatment product is indicative of an area, which can be reached by the treatment product during a simulated application process under a condition defined by the sprayer configuration data and the geo-dependent environmental data.
  • the device may apply a droplet distribution model to simulate ballistic or fluid dynamics of droplets and sprays based on the sprayer configuration data and geo-dependent environmental data.
  • the device may apply a pre-trained machine-learning model to relate the sprayer configuration data and the geo-dependent environmental data to a local application distribution of the treatment product.
  • the device determines an off-target movement of the treatment product based on a difference between the determined local application distribution of the treatment product and a target local application distribution of the treatment product.
  • the off-target movement represents a displacement of the treatment product relative to a target area during application.
  • the off-target movement may be understood as treatment product droplets that do not reach target area during application process.
  • a larger off-target movement may relate to a larger displacement of applied treatment product.
  • the received data may further comprise a physicochemical property of the treatment product. There are several approaches to integrate the physicochemical property of the treatment product into the model.
  • a droplet size of the treatment product is determined based on the physicochemical property of the treatment product and the sprayer configuration data.
  • the received model further relates the droplet size of the treatment product to the local application distribution of the treatment product.
  • the device may be initialized by selection of a treatment product to be applied to the field.
  • data associated with physicochemical properties of the product may be retrieved from a database.
  • the database may be local or in the cloud.
  • the data associated with the product may comprise one or more of the following physicochemical properties: viscosity, surface tension of the product, wettability, tack properties, foam persistence, particle size distribution, emulsion droplet size, spontaneity of dispersion, suspensibility, and reduction of off-target movement and volatility via tailor made off-target movement-reducing agents and/orvolatility reducing agents .
  • the device may provide a droplet size model.
  • the droplet size is a function of the sprayer configuration (e.g. nozzle geometry, speed of the nozzle relative to the ground, etc.) and the physicochemical properties of the treatment product (e.g.
  • the device may provide an off-target movement model associating the droplet size, the geo-dependent environmental data (e.g. wind speed), and the sprayer configuration (e.g. the location of the nozzle above ground, the speed of the nozzle above ground, etc.) to an off-target movement distance from the nozzle.
  • the device determines the off-target movement for the droplets dependent on the droplet size based on the droplet size model and the off-target movement model.
  • the machine-learning model may have been trained on training data to learn a mapping between input examples and the target variable.
  • Each input example may include particular spray configuration data, geodependent environment data, and physicochemical properties of a treatment product.
  • the target variable may include data indicative of a local application distribution of the treatment product captured by one or more sensors in the field.
  • the received data may further comprise crop data and/or weed data in the field.
  • the received model further relates the crop data and/or weed data to the local application distribution of the treatment product.
  • the received crop data may comprise crop information that may affect e.g. the wind speed and therefore the off-target movement.
  • Exemplary crop information may include, but is not limited to, growth stage at a specific time point and crop density (i.e. number of crops present per unit area of the field).
  • the received weed data may include information on disease, pest, and/or weed pressure, which may affect e.g. the wind speed and therefore the off-target movement.
  • the weed data may be obtained from e.g. a historic weed map. There are several approaches to include the crop data and/or weed data into the model.
  • the geo-dependent environmental data (e.g. wind speed) may be modified by the crop data and/or weed data.
  • the model may determine the local application distribution of the treatment product based on the sprayer configuration data and the modified geo-dependent environmental data.
  • the crop and/or weed data may be provided as a further input to the pre-trained machine-learning model to determine the local application distribution of the treatment product.
  • the received data may further comprise geographical data of the field.
  • the received model may further relate the geographical data of the field to the local application distribution of the treatment product.
  • Geographical data of the field may include, but is not limited to, the exact position in the field, distance to buffer zones, distance to field edges, and field surroundings.
  • the field surroundings may include information relevant to the off-target movement, such as topography and hedges. These parameters could decide on specific application parameters that will be selected during application as well as on the rate of the product that is used in dependent on the position of the field.
  • the received data may further comprise a field description of the field.
  • the received model may further relate the field description of the field to the local application distribution of the treatment product.
  • the field description may include, but is not limited to, inhomogeneity, such as increased moisture, drainage system, microclimate, etc.
  • the received geo-dependent environmental data may be modified based on the field description to reflect the inhomogeneity of e.g. moisture and temperature in the field. The modified geo-dependent environmental data can be used to generate a new off-target movement.
  • the device determines one or more spray application parameters for reducing the determined off-target movement.
  • the one or more spray application parameters may comprise an adaption of sprayer configuration data.
  • Varying the pressure at the nozzle(s) can change the droplet size, which in turn changes the off- target movement. For example, a new droplet size may be determined based on the droplet size model and the varied pressure at the nozzle. The new determined droplet size may then be used to determine the off-target movement based on the off-target movement model. If a pretrained machine-learning model is used, it is possible to modify the sprayer configuration data input of the pre-trained machine learning model to determine a new off-target movement.
  • the speed of the one or more sprayers it is possible to vary the speed of the one or more sprayers.
  • the off-target movement may then be determined based on the varied speed of the sprayer utilizing the droplet distribution model or the pre-trained machine-learning model.
  • the off-target movement may then be determined based on the varied height of the one or more sprayers utilizing the droplet distribution model or the pre-trained machine-learning model.
  • nozzle flow rate it is possible to select a different nozzle flow rate. This may be applicable if the sprayer comprises a revolver of different nozzles or if the sprayer is equipped with a PWM system, which may be automatically selected.
  • the one or more spray application parameters may comprise an adaption of water volume of the treatment product.
  • the water volume adaption can change the viscosity of the treatment product.
  • Based on the droplet size model and the varied viscosity a new droplet size may be determined.
  • the new determined droplet size may then be used to determine the off-target movement based on the off-target movement model.
  • a pre-trained machine-learning model it is possible to modify the input (i.e. the physicochemical properties of the treatment product) of the pre-trained machine-learning model to determine a new off-target movement estimate.
  • the concentration of dilution can be adjusted.
  • TM adjuvants e.g. fluidifying or viscosifier
  • the one or more sprayers in the field may comprise a multi-tank system with a plurality of treatment products.
  • the one or more spray application parameters may comprise a selection of a different treatment product or a different formulation to be applied to the field, based on an associated physicochemical property. For example, it is possible to choose a specific treatment product based on its physicochemical properties to reduce the off-target movement.
  • the one or more spray application parameters may include a combination of different options as described above.
  • the device provides, via a user interface, the determined one or more spray application parameters.
  • the determined off-target movement may also be provided via the user interface, for example on a field map.
  • the user interface may be a mobile user interface, which is the graphical and usually touch-sensitive display on a mobile device, such as a smartphone or tablet. This allows easy recognition for a framer how far the off-target movement will go and allows immediate action on the spraying activities based on the determined one or more spray application parameters (e.g. the water volume, the speed of the sprayer, the height of the nozzle, the nozzle configuration, a selection of a different nozzle, and/ or a selection of a different treatment product or a different formulation to be applied to the field).
  • the determined one or more spray application parameters e.g. the water volume, the speed of the sprayer, the height of the nozzle, the nozzle configuration, a selection of a different nozzle, and/ or a selection of a different treatment product or a different formulation to be applied to the field.
  • the device may provide control data suitable for adjusting the one or more sprayers in the field based on the determined one or more spray application parameters.
  • the control data may comprise setpoints for the water volume, the speed of the sprayer, the height of the nozzle, the nozzle configuration, a selection of a different nozzle, and/ or a selection of a different treatment product or a different formulation to be applied to the field.
  • the control data may be transferred to a sprayer control unit that manipulates the sprayer. In this way, the sprayers in the field may be automatically adjusted based on the control data to reduce the off-target movement.
  • the device may compare the determined off-target movement with a minimal desired off-target movement, and then determine the one or more spray application parameters, and/or generating a warning signal indicating that an action should be taken, if the determined off-target movement is greater than or equal to the minimal desired off-target movement.
  • the minimal desired off-target movement may be determined based on the location of the sprayer on the field, such that the buffer zones (e.g. buffer zone 200 shown in Fig. 1) will not be sprayed.
  • the minimal desired off-target movement may then be compared with the determined off-target movement. When the determined off-target movement exceeds the minimal desired off-target movement, a signal may be generated and provided.
  • the signal may be a warning signal indicating that the farmer shall take actions or a control signal initiating a control step.
  • the device may compare the determined off-target movement with a predefined threshold, which may be less than the minimal desired off-target movement, and then determine the one or more spray application parameters, and/or generating a warning signal indicating that an action should be taken, if the determined off-target movement is greater than or equal to the predefined threshold.
  • a predefined threshold may be provided by a user or by the system, which may define the threshold e.g. as two-thirds of the minimal desired off- target movement.
  • the device may determine one or more sprayer application parameters such that the off-target movement can be minimized to the lowest possible value. For example, the device may adjust various spray application parameters, determine associated off-target movement according to the method as disclosed herein, and select a combination of sprayer application parameters that can lead to an off-target movement with the lowest value.
  • Fig. 4 schematically shows a data flow in the apparatus 10.
  • data sources 20 are made available, wherein the data sources can be for example user device 22, database 24 and/or sensor 26.
  • user input device is understood to be a computer, a smartphone, a tablet, a smartwatch, a monitor, a data storage device, or any other device, by which a user, including humans and robots, can input or transfer data to the apparatus 10.
  • input database is understood to be any organized collection of data, which can be stored and accessed electronically from a computer system, and from which data can be inputted or transferred to the apparatus 10.
  • sensor is understood to be any kind of physical or virtual device, module or machine capable of detecting or receiving real-world information and sending this real-world information to another system, including temperature sensor, humidity sensor, moisture sensor, pH sensor, pressure sensor, soil sensor, crop sensor, water sensor, and cameras.
  • data which originated from one of the data sources 20 are optionally preprocessed in the data preprocessing section 30, wherein such data preprocessing may include data calibration, data transformation (e.g. into a different format), data correction, data validation, and data verification.
  • the data which originated from one of the data sources 20 and which has been optionally preprocessed in the data preprocessing section 30 are inputted into the apparatus 10.
  • the input data includes sprayer configuration data 40 and geo-dependent environmental data 42.
  • the input data may further include for example crop data 44, weed or pathogen or pest data 46, physicochemical properties of the treatment product 48, geographical data of the field 50, and field description of the field 52.
  • the above-mentioned data are processed by the apparatus 10 in the data processing section 14 using a model 60, such as a pre-trained machine-learning model or a droplet distribution model.
  • a model 60 such as a pre-trained machine-learning model or a droplet distribution model.
  • the final outputs of the model in the data processing section 14, which are in form of the control data suitable for adjusting the one or more sprayers in the field, are transferred from the apparatus 10 to the data output layer 16 and for example outputted on a user device 72, in a output database 74 or as a control file 76.
  • user output device is understood to be a computer, a smartphone, a tablet, a smartwatch, a monitor, a data storage device, or any other device, by which a user, including humans and robots, can receive data from apparatus 10.
  • output database is understood to be any organized collection of data, which can be stored and accessed electronically from a computer system, and which can receive data, which is outputted or transferred from the apparatus 10.
  • control file is understood to be any binary file, data, signal, identifier, code, image, or any other machine- readable or machine-detectable element useful for controlling a machine or device, for example an agricultural treatment device.
  • Fig. 5 illustrates a flow chart describing a computer-implemented method for reducing off-target application of a treatment product to a field according to another example of the present disclosure.
  • the physicochemical properties of the treatment product is also considered.
  • the device may be initialized by selection of a treatment product to be applied to the field.
  • a digital representation of the treatment product may be requested.
  • Data associated with physicochemical properties of the treatment product may be retrieved from a database or the label of the product.
  • the database may be local or in the cloud.
  • the data associated with the product may comprise one or more of the following physicochemical properties: viscosity, static and dynamic surface tension of the product, concentration, Henry-coefficient, vapor pressure, potential mixing partners and other ingredients in the spray solution, wettability, foam persistence, particle size distribution, spontaneity of dispersion, suspensibility, and reduction of off-target movement and volatility via tailor made off-target movement-reducing agents or volatility reducing agents.
  • the device also receives sprayer configuration data of one or more sprayers in the field, and the geo-dependent environmental data.
  • the sprayer configuration data includes the height of the boom above ground, the nozzle spacing, the nozzle type, e.g. drift reducing nozzles spray pressure, the driving speed respectively moving speed of the nozzles, the application timing like daytime or inversion, and mechanical measures like covers, and/or air-assistant booms, but is not limited thereto.
  • the geo-dependent environmental data included information about the temperature, the relative humidity, the UV intensity and/or the wind speed. Additionally, or alternatively the geo-dependent environmental data further include field condition data and adjacent field area data.
  • the field condition data include the high and structure of canopy within the field and in specific geocoordinate in the field like GS crop or the vitality of crop and/or surroundings of field and potential physical impact on wind, e.g. hedges etc.
  • Adjacent field area data include neighbouring areas like water bodies, crops or sensitive species having possible additional threshold setting to avoid residues through off-target movement.
  • the received physicochemical properties of the treatment product, sprayer configuration data, and geo-dependent environmental data are then input in the model.
  • the model is a two-step model.
  • the device may provide a droplet size model.
  • the droplet size is a function of the sprayer configuration (e.g. nozzle geometry, speed of the nozzle relative to the ground, etc.) and the physicochemical properties of the treatment product (e.g. pressure of the treatment product at the nozzle, the surface tension of the treatment product, and the viscosity of the treatment product, the dilution rate of the treatment product, etc.).
  • the device may provide an off-target movement model associating the droplet size, the geo-dependent environmental data (e.g. wind speed), and the sprayer configuration (e.g. the location of the nozzle above ground, the speed of the nozzle above ground, etc.) to an off-target movement distance from the nozzle.
  • the model is a pre-trained machine learning model, which has been trained to relate the received data to a local application distribution of the treatment product.
  • the device determines the off-target movement by comparing the determined local application distribution of the treatment product with the received target application data.
  • a minimal desired off-target movement may be determined based on the location of the sprayer on the field, such that the buffer zones (e.g. buffer zone 200 shown in Fig. 1) will not be sprayed or affected.
  • the minimal desired off-target movement may then be compared with the determined off-target movement-.
  • the sprayer configuration data and/or the physicochemical properties of the treatment product may be modified.
  • the modified sprayer configuration data e.g. adjusting of nozzle e.g.
  • an off-target movement reducing nozzle adjusting the spray pressure and modifying nozzle speed or other spray application parameters, adjusting air-assistant spraying, adjusting boom height control, modified nozzle height, etc.
  • the physicochemical properties of the treatment product e.g. change of water volume, selection of a different treatment product, adding of an off-target movement reducing agent, etc.
  • This can be achieved by generating an objective function based on the maximum off-target movement and the determined off-target movement and minimizing the objective function by varying the sprayer configuration and/or the physicochemical properties of the treatment product.
  • the process may be implemented in one or more iterations until the predicted off-target movement is controlled (e.g. the predicted off-target movement less than the maximum allowable off-target movement). Then, the control data is generated based on the modified sprayer configuration data and/or the modified physicochemical properties of the treatment product. When the determined off-target movement does not exceed the maximum allowable off-target movement, no adjustment of the sprayer configuration data and/or the physicochemical properties of the treatment product is provided.
  • a predefined threshold may be provided e.g. by a user, by the manufacturer of the treatment product, by government organs or by the system.
  • the predefined threshold may be less than the maximum allowable off-target movement.
  • the predefined threshold may then be compared with the determined off-target movement.
  • the sprayer configuration data and/or the physicochemical properties of the treatment product may be modified.
  • the modified sprayer configuration data and/or the physicochemical properties of the treatment product e.g. change of water volume, selection of a different treatment product, etc.
  • the process may be implemented in one or more iterations until the predicted off-target movement is controlled (e.g. the predicted off-target movement less than the predefined threshold). Then, the control data is generated based on the modified sprayer configuration data and/or the modified physicochemical properties of the treatment product.
  • the device may modify the sprayer configuration data and/or the physicochemical properties of the treatment product.
  • the modified sprayer configuration data e.g. modified nozzle speed, modified nozzle height, etc.
  • the physicochemical properties of the treatment product e.g. change of water volume, selection of a different treatment product, etc.
  • the process may be implemented in one or more iterations until the predicted off-target movement is minimized.
  • the control data is generated based on the modified sprayer configuration data and/or the modified physicochemical properties of the treatment product.
  • Fig. 6 schematically illustrates a treatment management system 500.
  • the treatment management system 500 may comprise a sprayer 510, a data management system 520, a field management system 530, and a client computer 540.
  • the sprayer 510 may be e.g. ground robots with variable-rate applicators, aerial sprayers, or other variable-rate applicators for applying a treatment product to the field 100
  • the treatment product may be at least one out of a group or a mixture of at least two out of the group, the group consisting of chemical products, biological products, fertilizers, nutrients, biostimulants, water, tank-mix adjuvants, compatibilizers, water conditioners and pH-modifiers.
  • the products may be solo products, may be pre-mixes of products or may be all-in- one combinations of several products.
  • the products may be provided as liquids or as solids.
  • the chemical products may be fungicides, herbicides, insecticides, acaricides, molluscicides, nematicides, avicides, piscicides, rodenticides, repellants, pheromones, bactericides, biocides, safeners, plant growth regulators, urease inhibitors, nitrification inhibitors or denitrification inhibitors, or any combination thereof.
  • the biological products may be microorganisms acting as fungicide (biofungicide), herbicide (bioherbicide), insecticide (bioinsecticide), acaricide (bioacaricide), molluscicide (biomolluscicide), nematicide (bionematicide), avicide, piscicide, rodenticide, repellant, pheromone, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor or denitrification inhibitor.
  • Tank-mix adjuvants may be products that amongst other functionalities such as increasing bioperformance of the products also act as compatibilizers preventing the phase separation in a tank or that ease the mixing compatibility of other products and are particularly used when the product is a mixture.
  • Compatibilizers may be used in a mixture or alone and ease the mixture of other products or modify the mixture of other products such that the mixture achieves certain properties, such as sprayability, reduced surface tension or adjusted droplet size distribution.
  • Herbicides in particular herbicidal active ingredient and mixtures may be at least one of the following classes: 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,
  • herbicides respectively the herbicidal active ingredients
  • Preferred herbicides may be Kixor, Tirexor (and other PPO mode of actions classes), Glufosinate-ammonium and L-Glufosinate, Glyphosate-salts, Dicamba as most important and mixtures containing at least one of them.
  • Slightly minor preferred herbicides and mixtures may be 2.4-D (choline, DMA, esters), Dicamba (BAPMA, DGA, Na, K), Acetochlor, Atrazine, Bentazone, Bicyclopyrone, Carfentrazone-E, Clethodim, Clomazone, Clopyralid, Cloransulam, DFFP, Dimethenamid-P, Flumioxazin, Fomesafen, Glufosinate-Ammonium, Glyphosate (K, I PA), Glyphosate-Potassium-Salt, Imazamox, Imazethapyr, Isoxaflutole, Mesotrione, Metribuzin, Pendimethalin, Pyroxasulfone, Quizalofop, Saflufenacil, S-Metolachlor, Sulfentrazone, Tembotrione, Topramezone, Trifl udim oxazin, Metazach
  • Minor preferred herbicides may be Chlorimuron-E, Diflufenican, Diquat, Flufenacet, Flumetsulam, Fluthiacet, Halauxifen, Halosulfuron, Lactofen, Paraquat, Rimsulfuron, Thifensulfuron, Tolpyralate, Quizalofop-P-E, Fenoxaprop, Flurochloridone, Aclonifen, Propaquizafop, L-Glufosinate Ammonium/Topramezone, L-Glufosinate Ammonium/Mesotrione, Atrazine/Mesotrione/S-Metolachlor, Fluthiacet-M/Pyroxasulfone, L-Glufosinate Ammonium/Tembotrione, Acetochlor/Fomesafen, GLY/GFA, L-Glufosinate Ammonium/Acetochlor, Atrazine/Mesotrione, L-Gluf
  • High minor preferred herbicides and mixtures may be Florasulam, Flumiclorac, Fluroxypyr, Haloxyfop-P-M, Nicosulfuron, Tribenuron, S-Metolachlor and Terbuthylazine, Benoxacor, Mesotrione and S-Metolachlor, Cyprosulfamide, Foramsulfuron, lodosulfuron-M- Na/Thiencarbazone-M, Dicamba/Nicosulfuron/Rimsulfuron, Cyprosulfamide, Flufenacet, Foramsulfuron, Terbuthylazine and Thiencarbazone-M, Mesotrione, S- Metolachlor/Terbuthylazine, Mesotrione/Terbuthylazine, Mesotrione/Nicosulfuron, L-Glufosinate Ammonium/Bicyclopyrone, Glyphosate-lsopropyl-Amine
  • An insecticide may be at least one substance being selected from the group consisting of: Triazemate, aldicarb, alanycarb, benfuracarb, carbaryl, carbofuran, carbosulfan, methiocarb, methomyl, oxamyl, primicarb, propoxur, thiodicarb, acephate, azinphos-ethyl, azinphos-methyl, chlorfenvinphos,, chlorpyrifos, chlorpyrifos-methyl, demeton-S-methyl, diazinon, dichlorvos/DDVP, dicrotophos, dimethoate, disulfoton, ethion, fenitrothion, fenthion, isoxathion, malathion, methamidaphos, methidathion, mevinphos, monocrotophos, oxymethoate, oxydemeton- methyl, parathion, parathion-methyl,
  • a fungicide may be at least one substance being selected from the group consisting of: - mode of action group A (nucleic acids metabolism): phenylamides, hydroxy-(2-amino- )pyrimidines, heteroaromatics, carboxylic acids
  • - mode of action group B (Cytoskeleton and motor protein): Methyl Benzimidazole Carbamates, N-phenyl carbamates, benzamides (toluamides), thiazole carboxamide, phenylureas, benzamides (pyridinylmethyl benzamides), cyanoacrylates, aryl-phenyl ketones
  • - mode of action group C pyrimidinamines, pyrazole-M ET1 , quinazoline, Succinate dehydrogenase inhibitors, Qol (Quinone outside Inhibitors) fungicides, Qil (Quinone inside Inhibitors) fungicides, dinitrophenyl crotonates, 2,6-dinitro-anilines, organo tin compounds, thiophene-carboxamides, QoSI fungicides (Quinone outside Inhibitor, stigmatellin binding type)
  • - mode of action group D protein synthesis: Anilino-Pyrimidines, enopyranuronic acid antibiotic, hexopyranosyl antibiotic, glucopyranosyl antibiotic, tetracycline antibiotic
  • - mode of action group E (signal transduction): azanaphthalenes, phenylpyrolles, dicarboximides
  • - mode of action group F lipid synthesis or transport I membrane integrity or function: phosphor thiolates, Dithiolanes, aromatic hydrocarbons, 1 ,2,4-thiadiazoles, carbamates, Polyene, piperidinyl-thiazole-isoxazolines
  • - mode of action group G sterol biosynthesis in membrane: Demethylation Inhibitors (SBI: Class I), morpholines (SBI: Class II), KetoReductase Inhibitors (SBI: Class III), thiocarbamates (SBI Class IV), allylamines (SBI Class IV),
  • - mode of action group H cell wall biosynthesis: Polyoxins, Carboxylic Acid Amides
  • - mode of action group P host plant defence induction: benzo thiadiazole (BTH), benzisothiazole, thiadiazole carboxamide, polysaccharides, plant extracts, phosphonates, isothiazole
  • - mode of action group U (Unknown mode of action): cyanoacetamide oxime, phthalamic acids, benzotriazines, benzene-sulfonamides, pyridazinones, phenylacetamide, guanidines, thiazolidine, pyrimidinone-hydrazones, 4-quinolyl-acetate, tetrazolyloxime, glucopyranosyl antibiotic
  • - mode of action group M (Chemicals with multi-site activity): inorganic electrophiles, dithiocarbamates and relatives (eletrophiles), phthalimides (electrophiles), chloronitriles (phthalonitriles), sulfamides (electrophiles), bis-guanidines (membrane disruptors, detergents), triazines, quinones (anthraquinones) (electrophiles), quinoxalines (electrophiles), maleimide (electrophiles), thiocarbamate (electrophiles), or a combination thereof.
  • At least one fungicide is selected from the group consisting of: Ametoctradin (Initium); Azoxystrobin; Boscalid; Carbendazim; Chlorothalonil; Copper; Copper; Cymoxanil; Difenoconazole;
  • Dimethomorph Dimoxystrobin; Dithianon; Dodemorph-acetate; Epoxiconazole; Fenpropimorph; Fludioxonil; Fluquinconazole; Fluxapyroxad (Xemium); Folpet; Folpet; Fosetyl-AI; Iprodione; KHP (Potassium Hydrogen Phosphonate); Kresoxim-methyl; Mancozeb; Metalaxyl;
  • the treatment product may include biostimulants, which may contain substance(s) and/or micro-organisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance/benefit nutrient uptake, nutrient efficiency, tolerance to abiotic stress, and crop quality.
  • biostimulants may include amino acids, seaweed-based products, humic and fulvic acids, or any combination thereof.
  • the treatment product may include fertilizer and nutrient, seed and seedling, water, or any combination thereof.
  • the treatment product may include adjuvants (built-in or tank-mixed) like anti-rebouncing agents, spreaders, uptake enhancers or water-conditioners or any combination thereof
  • the sprayer 510 is embodied as smart farming machinery.
  • the smart farming machinery 510 may be a smart sprayer and includes a connectivity system 512.
  • the connectivity system 512 may be configured to communicatively couple the smart farming machinery 510 to the distributed computing environment. It may be configured to provide data collected on the smart farming machinery 510 to the data management system 520, the field management system 530, and/or the client computer 540 of the distributed computing environment.
  • the data management system 520 may be configured to send data to the smart farming machinery 510 or to receive data from the smart farming machinery 510. For instance, as detected maps or as applied maps comprising data recorded during application on the field 100 may be sent from the smart farming machinery 510 to the data management system 520.
  • the data management system 520 may comprise georeferenced data of different fields and the associated treatment map(s).
  • the field management system 520 may be configured to provide a control protocol, an activation code or a decision logic to the smart farming machinery 510 or to receive data from the smart farming machinery 510. Such data may also be received through the data management system 520.
  • the field computer 540 may be configured to receive a user input and to provide a field identifier and an optional treatment specifier to the field management system 530.
  • the field identifier may be provided by the sprayer 510.
  • the optional treatment specifier may be determined using e.g. growth stage models, weather modelling, neighbouring field incidences, etc.
  • the field management system 530 may search the corresponding agricultural field and the associated treatment map(s) in the data management system 520 based on the field identifier and the optional treatment specifier.
  • the field computer 540 may be further configured to receive client data from the field management system 530 and/or the smart farming machinery 510. Such client data may include for instance application schedule to be conducted on certain fields with the smart farming machinery 510 or field analysis data to provide insights into the health state of certain fields.
  • the treatment device 510, the data management system 520, the field management system 530, and the client computer 540 may be associated with a network.
  • the network may be the internet.
  • the network may alternatively be any other type and number of networks.
  • the network may be implemented by several local area networks connected to a wide area network.
  • the network may comprise any combination of wired networks, wireless networks, wide area networks, local area networks, etc.
  • the apparatus 10 shown in Fig. 2 may be embodied as, or in, the field management system 530 to perform the above-described method to provide a control data to the smart farming machinery 510.
  • the field management system 530 may receive the sprayer configuration data from the sprayer 510 via the connectivity system 512.
  • the field management system 530 may receive geo-dependent environmental data (e.g. temperature, moisture, humidity, and/ or wind speed) form one or more sensors installed on the sprayer 510 to monitor environmental data.
  • the field management system 530 may receive geo-dependent environmental data from weather services.
  • the field management system 530 then provides a model 60 to relate the received data to a local application distribution of the treatment product, and then determines adapted sprayer configurations based on a comparison between the determined local application distribution and a local target application of the treatment product.
  • Control data may be provided by the field management system 530 to the sprayer 510 to adapt the sprayer configurations to reduce the off-target movement.
  • the control data may be provided by the field management system 530 to the client computer 540 to reduce the off-target movement.
  • Fig. 7 illustrates an example of a smart sprayer system 600 for controlling application of a treatment product to a field.
  • the smart sprayer system 600 may be releasable attached or directly mounted to a ground-based platform (e.g. tractor or self-propelled sprayer shown in Fig. 6) or to a non-ground-based platform (e.g. drone, aircraft, helicopter).
  • a ground-based platform e.g. tractor or self-propelled sprayer shown in Fig. 6
  • a non-ground-based platform e.g. drone, aircraft, helicopter.
  • the sprayer system 600 comprises a sprayer unit 610, a sensor unit 620, a control unit 630, a processing unit 640, a pump 650, and a reservoir 660.
  • the sprayer unit 610 may comprises a boom 612 with spray nozzle arrangement 614 arranged along the boom of the sprayer unit 610.
  • the spray nozzle arrangement 614 may be arranged fixed or movable along the boom in regular or irregular intervals.
  • Each spray nozzle 616 may arranged together with a controllable valve (e g pulse width modulation PWM) to regulate fluid release from the spray nozzle arrangement 614 to the field 100.
  • the sensor unit 620 may comprise one or more sensors which may be arranged fixed or movable along the boom 612 in regular or irregular intervals.
  • the sensor unit 620 may be configured to sense one or more conditions of the field.
  • the sensor unit 620 may comprise an optical sensors providing an image of the field.
  • Suitable optical sensors may be multispectral cameras, stereo cameras, IR cameras, CCD cameras, hyperspectral cameras, ultrasonic or LIDAR (light detection and ranging system cameras).
  • the sensor unit 620 may include further sensors to measure humidity, temperature, wind, or any other suitable condition on the field.
  • the tank valves (not shown) and the nozzle valves (not shown) of the sprayer unit 610 and the sensor unit 620 may be communicatively coupled to the control unit 630.
  • the control unit 630 may be wired to the respective components.
  • sensor unit 620, the tank valves or the nozzle valves of the sprayer unit 610 may be wirelessly connected to the control unit 530.
  • more than one control unit 630 may be distributed in the sprayer system or the tractor and communicatively coupled to the sprayer unit 610 and the sensor unit 620.
  • the processing unit 640 may be configured to determine the sprayer control settings.
  • the sprayer control settings may be determined based on a control file.
  • the pump 650 may be configured to pump the treatment product from the reservoir 660.
  • the smart sprayer system 600 may comprise one or more reservoir(s), also referred to as pre-mixing unit(s).
  • the one or more reservoirs 660 are in fluid communication with the nozzle arrangement 614 through common fluidic line, which distributes the mixture as released from the reservoir(s) to the spray nozzle arrangement 614.
  • each reservoir may hold a respective treatment product to be released on the field 100.
  • This may include biologically active or supplementary ingredients like a herbicide mixture, individual ingredients of a herbicide mixture, a selective herbicide for specific weeds, a fungicide, a fungicide mixture, ingredients of a fungicide mixture, ingredients of a plant growth regulator mixture, a plant growth regulator, water, oil, or any other formulation agent.
  • This may include biological products, such as microorganisms useful as fungicide (biofungicide), herbicide (bioherbicide), insecticide (bioinsecticide), acaricide (bioacaricide), molluscicide (biomolluscicide), nematicide (bionematicide), avicide, piscicide, rodenticide, repellant, pheromone, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof.
  • biostimulants such as amino acids, seaweed-based products, humic and fulvic acids, or any combination thereof.
  • Each reservoir 560 may further comprise a controllable valve (not shown) to regulate fluid release from the reservoirs to the fluid lines.
  • the smart sprayer system 600 may comprise a sensor for detecting the off-target movement. Alternatively or additionally, the sensor may be present in the buffer zone.
  • the smart sprayer system 600 may receive configuration data provided by the field management system 530 shown in Fig. 6.
  • the control data may comprise setpoints for the water volume, the speed of the sprayer, the height of the nozzle, the nozzle configuration, a selection of a different nozzle, and/or a selection of a different treatment product or a different formulation to be applied to the field.
  • the processing unit 640 can determine the sprayer control settings and then transmits the sprayer control settings to the control unit 630.
  • the control unit 630 controls the sprayer unit 610 based on the sprayer control settings.
  • control unit 630 may control the controllable valve to allow the selected treatment product to be released from the reservoir to the fluid lines.
  • control unit 530 may control the controllable valve to allow water to be released from the reservoir to the fluid lines.
  • control unit 530 may control the nozzle valves to allow the selected nozzle to spray.
  • a computer program or a computer program element is provided that is characterized by being adapted to execute the method steps of the method according to one of the preceding embodiments, on an appropriate system.
  • the computer program element might therefore be stored on a computer unit, which might also be part of an embodiment of the present invention.
  • This computing unit may be adapted to perform or induce a performing of the steps of the method described above. Moreover, it may be adapted to operate the components of the above described apparatus.
  • the computing unit can be adapted to operate automatically and/or to execute the orders of a user.
  • 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 of the invention.
  • This exemplary embodiment of the invention covers both, a computer program that right from the beginning uses the invention and a computer program that by means of an up-date turns an existing program into a program that uses the invention.
  • the computer program element might be able to provide all necessary steps to fulfil the procedure of an exemplary embodiment of the method as described above.
  • a computer readable medium such as a CD-ROM
  • 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.
  • 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.
  • 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.
  • 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 invention.
  • Kixor is applied in a (pre-plant) burndown application to control small dicot weeds (e.g. GS12).
  • Sharpen product (SC formulation) is applied at a use rate of 1.0 fl ozs/A.
  • An adjuvant will be potentially added.
  • the adjuvant is methylated seed oil MSO at 1 % v/v).
  • the spray solution will be prepared using a water volume of 10 gallons of water per acre.
  • As a sprayer a conventional ground sprayer will be used, wherein the ground sprayer is equipped with air-induction nozzles (e.g. Type ID-120-02) at a pressure of 87psi for providing medium to coarse droplet size.
  • the driving speed is 8 mph.
  • the application will be performed at a temperature of 70F and 4 mph wind speed. Due to the phys-chem properties of Kixor and the corresponding spray solution and in addition the favorable application conditions, the secondary loss prediction is below the base threshold for minimal secondary loss, therefore no additional measures are needed.
  • Glufosinate-ammonium is applied in a POST-application in glofosinate- resistant corn to control small-seeded broadleaf weeds.
  • Liberty 280SL as formulated Glufosinate-ammonium, is applied at a use rate of 28 fl ozs/A.
  • the spray solution will be prepared using a water volume of 15 gallons of water per acre.
  • As sprayer a conventional ground sprayer will be used equipped with air-induced nozzles (i.e. LU-120-06) at a pressure of 29 psi, providing medium to coarse droplet size.
  • the driving speed is 11 mph and the boom height is 24 inch.
  • the application will be performed at a temperature of 80F and 6 mph wind speed. Due to the application conditions, the secondary loss can be further reduced by additional measures. For this a reduced boom height ⁇ 20 inch and an automated boom height controller and furthermore the addition of a drift-reducing agent is recommended.

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  • Life Sciences & Earth Sciences (AREA)
  • Soil Sciences (AREA)
  • Environmental Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Insects & Arthropods (AREA)
  • Pest Control & Pesticides (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Catching Or Destruction (AREA)

Abstract

La présente demande concerne l'agriculture numérique. Afin de réduire l'application hors cible d'un produit de traitement, un procédé et un appareil sont proposés pour déterminer la zone d'application d'un produit de traitement sur la base de données environnementales numériquement disponibles (par exemple, sens du vent, vitesse du vent, température, humidité, etc.) ou de données de produit physique ainsi que de configurations de pulvérisateur (par exemple type et pression de buse, hauteur de flèche, vitesse de conduite, etc.). La zone d'application déterminée du produit de traitement est comparée à une zone d'application cible pour déterminer un éventuel mouvement hors cible. Des données de commande peuvent être générées sur la base des données déterminées, qui sont utilisées pour ajuster les configurations de pulvérisateur ou les caractéristiques de pulvérisation à l'aide par exemple d'agents de réduction de dérive de telle sorte que le mouvement hors cible est abaissé à un niveau minimal souhaité. En variante ou de plus, le ou les paramètres d'application de pulvérisation déterminés peuvent être fournis à un agriculteur par l'intermédiaire d'une interface utilisateur de telle sorte que l'agriculteur peut effectuer une action afin d'atteindre les niveaux minimaux souhaités hors cible.
PCT/EP2022/083529 2021-11-26 2022-11-28 Réduction d'application hors cible d'un produit agricole à un champ Ceased WO2023094667A1 (fr)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118655785A (zh) * 2024-08-16 2024-09-17 农业农村部南京农业机械化研究所 一种露地蔬菜肥药精准喷施智能感知与决策系统及其方法
WO2025125319A1 (fr) * 2023-12-14 2025-06-19 Basf Se Bouton de superproduction pour une nouvelle chimie
WO2025125349A1 (fr) * 2023-12-14 2025-06-19 Basf Se Service de sélection d'agent pour l'industrie chimique
WO2025125347A1 (fr) * 2023-12-14 2025-06-19 Basf Se Service de structuration d'agent pour l'industrie chimique
WO2025125327A1 (fr) * 2023-12-14 2025-06-19 Basf Se Service de sélection d'agent pour l'industrie chimique
WO2025163074A1 (fr) * 2024-01-30 2025-08-07 Basf Se Procédé et appareil pour générer un état de surveillance et/ou de commande pour une application de pulvérisation agricole

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130103211A1 (en) * 2011-10-25 2013-04-25 Agco Corporation Dynamic spray buffer calculation
US20180054983A1 (en) * 2016-08-25 2018-03-01 Iowa State University Research Foundation, Inc. System and method for predicting wind direction and speed to better control drift
US10252285B2 (en) * 2017-08-08 2019-04-09 Deere & Company Mobile drift sensor for agricultural spraying
US20200039647A1 (en) * 2017-03-24 2020-02-06 Basf Agro Trademarks Gmbh Drift correction during the application of crop protection agents
WO2020239771A1 (fr) * 2019-05-30 2020-12-03 Agco International Gmbh Système de commande de pulvérisateur agricole

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130103211A1 (en) * 2011-10-25 2013-04-25 Agco Corporation Dynamic spray buffer calculation
US20180054983A1 (en) * 2016-08-25 2018-03-01 Iowa State University Research Foundation, Inc. System and method for predicting wind direction and speed to better control drift
US20200039647A1 (en) * 2017-03-24 2020-02-06 Basf Agro Trademarks Gmbh Drift correction during the application of crop protection agents
US10252285B2 (en) * 2017-08-08 2019-04-09 Deere & Company Mobile drift sensor for agricultural spraying
WO2020239771A1 (fr) * 2019-05-30 2020-12-03 Agco International Gmbh Système de commande de pulvérisateur agricole

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2025125319A1 (fr) * 2023-12-14 2025-06-19 Basf Se Bouton de superproduction pour une nouvelle chimie
WO2025125349A1 (fr) * 2023-12-14 2025-06-19 Basf Se Service de sélection d'agent pour l'industrie chimique
WO2025125347A1 (fr) * 2023-12-14 2025-06-19 Basf Se Service de structuration d'agent pour l'industrie chimique
WO2025125327A1 (fr) * 2023-12-14 2025-06-19 Basf Se Service de sélection d'agent pour l'industrie chimique
WO2025163074A1 (fr) * 2024-01-30 2025-08-07 Basf Se Procédé et appareil pour générer un état de surveillance et/ou de commande pour une application de pulvérisation agricole
CN118655785A (zh) * 2024-08-16 2024-09-17 农业农村部南京农业机械化研究所 一种露地蔬菜肥药精准喷施智能感知与决策系统及其方法

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