WO2024089123A1

WO2024089123A1 - Method for providing control data for a drone

Info

Publication number: WO2024089123A1
Application number: PCT/EP2023/079820
Authority: WO
Inventors: Holger Hoffmann; Maik ZIES; Alberto DE LA RUA; Greg Robert KRUGER
Original assignee: Basf Agro Trademarks Gmbh
Priority date: 2022-10-25
Filing date: 2023-10-25
Publication date: 2024-05-02

Abstract

Computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: providing a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone; providing growth height data comprising the growth height of the plant cultivated on the agricultural field; providing distance data comprising the distance between the dropleg-type nozzle and the drone; providing nominal distance data based on the growth height data and the distance data by utilizing the control model; providing control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

Description

METHOD FOR PROVIDING CONTROL DATA FOR A DRONE

TECHNICAL FIELD

The present disclosure relates to a computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, a respective system/apparatus for providing such control data, a drone controlled by such control data, a use of a control model in such a method and/or system/apparatus.

TECHNICAL BACKGROUND

The general background of this disclosure is the treatment of plants in an agricultural area, which may be an agricultural field, a greenhouse, or the like. The treatment of plants, such as the cultivated crops, may also comprise the treatment of weeds present in the agricultural area, the treatment of the insects present in the agricultural area or the treatment of pathogens present in the agricultural area. The application of an agricultural product by drones is particularly difficult in practice, as the drone propulsion system may cause turbulences and thus may cause undesirable displacement of the agricultural product.

It has been found that a need exists to allow an application of an agricultural product on an agricultural field in a more reliable manner with a drone.

SUMMARY OF THE INVENTION

An aspect of the present disclosure relates to a computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: providing a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone; providing growth height data comprising the growth height of the plant cultivated on the agricultural field; providing distance data comprising the distance between the dropleg-type nozzle and the drone; providing nominal distance data based on the growth height data and the distance data by utilizing the control model; providing control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

In other words, an aspect of the present disclosure relates to a computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: providing a control model configured to determine a nominal distance between a plant cultivated on the agricultural field and the dropleg-type nozzle of the drone based on a growth height of the plant and the distance between the dropleg-type nozzle and the drone; providing growth height data comprising the growth height of the plant cultivated on the agricultural field; providing distance data comprising the distance between the dropleg-type nozzle and the drone; determining nominal distance data based on the growth height data and the distance data by utilizing the control model; providing control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

The growth height data and/or the distance data may be provided substantially during the drone is flying over the field, e.g. by respective distance sensors. The flight height may be controlled by using the control data in such a way that the distance between the nozzle and the height of the plant may be kept at a predefined value, e.g. about 2m. In another example the distance may be kept in a predefined range of values, e.g. between 2m and 3m.

The flight height data may be used to control and/or regulate the flight height of the drone, e.g. during applying a product to the plant.

The distance to the plant may be determined during the flight.

A further aspect of the present disclosure relates to a system and/or to a device for providing control data for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: a providing unit configured to provide a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone; a further providing unit configured to provide growth height data comprising the growth height of the plant cultivated on the agricultural field; a further providing unit configured to provide distance data comprising the distance between the dropleg-type nozzle and the drone; a further providing unit configured to provide nominal distance data based on the growth height data and the distance data by utilizing the control model; a further providing unit configured to provide control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

A further aspect of the present disclosure relates to an apparatus for providing control data for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, 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 a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone; providing growth height data comprising the growth height of the plant cultivated on the agricultural field; providing distance data comprising the distance between the dropleg-type nozzle and the drone; providing nominal distance data based on the growth height data and the distance data by utilizing the control model; providing control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

A further aspect of the present disclosure relates to a drone comprising a dropleg-type nozzle configured to be controlled by control data provided according to a computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field.

A further aspect of the present disclosure relates to a use of a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone in a computer-implemented method according to the present disclosure and/or in a system according to the present disclosure and/or in an apparatus according to the present disclosure.

A further aspect of the present disclosure relates to a computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the computer-implemented method according to the present disclosure in a system according to the present disclosure or in an apparatus according to the present disclosure.

The embodiments described herein relate to the method, the system, the apparatus, the application 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 ..initiating or causing to determine", “generating" also includes ..initiating or causing to generate" and “providing” also includes “initiating or causing to determine, generate, select, send or receive”. “Initiating or causing to perform an action” includes any processing signal that triggers a computing device to perform the respective action.

The method, device, system, application device, apparatus, computer program element, disclosed herein provide objective means to allow an application of an agricultural product on an agricultural field in a more reliable manner with a drone. It is an object of the present invention to provide a more robust, reliable way of treating an agricultural field with a drone.

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 “drone” is to be understood broadly in the present case and comprises any air vehicle, e.g. an unmanned arial vehicle (UAV), a drone, or the like. The drone may be equipped with one or more treatment unit(s) and/or one or more monitoring unit(s). The drone may be configured to collect field data via the treatment and/or monitoring unit. The drone may be configured to sense field data of the agricultural field via the monitoring unit. The treatment device may be configured to treat the agricultural field via the treatment unit. Treatment unit(s) may be operated based on monitoring signals provided by the monitoring unit(s) of the treatment device. The treatment device may comprise a communication unit for connectivity. Via the communication unit the treatment device may be configured to provide or send field data, to provide or receive operation data and/or to provide or receive operation data.

The term “dropleg nozzle” is to be understood broadly in the present case and comprises any tube- or hose-like arrangement which may be arranged on a drone in such a way as to guide/hold at least one dispensing nozzle, with which the agricultural product may be dispensed on a crop or weed, out of the turbulence region or to hold/guide the dispensing nozzle into a section within the turbulence region which may still lead to an acceptable drift of the agricultural product. Such a tube or hose-like arrangement may be provided as flexible or rigid design. The tube or hose-like arrangement may be designed in such a way that the distance of the dispensing nozzle from the drone may be varied. All known types of nozzles can be used as dispensing nozzles.

The preferred plane for measuring the distance between the drone and the nozzle or between the drone and the plants is the rotor plane. Depending on the design/geometry of the drone, the center of gravity of the drone or a certain predefined plane on the drone may also be used.

“Agricultural field” as used herein refers to an agricultural field to be treated. The agricultural field may be any plant or plant cultivation area, such as a farming field, a greenhouse, or the like. A plant may be a plant, a crop, a weed, a volunteer plant, a plant from a previous growing season, a beneficial plant or any other plant present and/or growing on the agricultural field. The agricultural field may be identified through its geographical location or geo-referenced location data. The plant may be connected and/or enrooted to/in the field and extends or elevates substantially perpendicular to a surface of the field. A reference coordinate, a size and/or a shape may be used to further specify the agricultural field, in particular the location of the field and/or the plant. The agricultural field may define a ground plane or ground surface. The top of the plant and/or of a plurality of plants may define a plant plane or plant surface, substantially parallel to the ground plane.

“Field data” as used herein is to be understood broadly in the present case and comprises any data that may be obtained by the treatment device. Field data may be obtained from the treatment unit and/or the monitoring unit of the treatment device. Field data may comprise measuring data obtained by the treatment device. Measuring data may comprise data related to a field condition on the agricultural field and/or to an operation of the treatment device. Field data may comprise image data, spectral data, section data indicating flagged sections derived plant data, derived plant data, derived weed data, derived soil data, geographical data, trajectory data of the treatment device, measured environmental data (e.g. humidity, airflow, temperature, and sun radiation), and treatment data relating to the treatment operation.

The term “agricultural product” is understood to be any object or material useful for the treatment and applicable with a drone. In the context of the present disclosure, the term agricultural product may comprise:

- 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.

- biological products such as microorganisms useful as fungicide (biofungicide), herbicide (bioherbicide), insecticide (bioinsecticide), acaricide (bioacaricide), molluscicide (biomolluscicide), nematicide (bionematicide), avicide, piscicide, rodenticide, repellant, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or any combination thereof.

- fertilizer and nutrient,

- seed and seedling,

- water, and

- any combination thereof.

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 the drone. The control data may be provided by a control unit and may be configured to control one or more technical means of the drone, e.g. the drive control but is not limited thereto. In a preferred embodiment, the drone is controlled such that the distance of the dispensing nozzle from the plant is kept substantially constant, e.g. the drone is controlled during an application of an agricultural product at such a distance from the plants, e.g. an upper plant level, that the dispensing nozzle is spaced between 1 and 5 meters, more preferably 1 and 3 meters, and most preferably at a distance of about 2 meters from the plants.

The term “providing” as used herein is to be understood broadly in the present case and represents any providing, receiving, querying, measuring, calculating, determining, transmitting of data, but is not limited thereto. Data may be provided by a user via a user interface, depicted/shown to a user by a display, and/or received from other devices, queried from other devices, measured other devices, calculated by other device, determined by other devices and/or transmitted by other devices. The term “data” as used herein is to be understood broadly in the present case and represents any kind of data. Data may be single numbers/numerical values, a plurality of a numbers/numerical values, a plurality of a numbers/numerical values being arranged within a list, 2 dimensional maps or 3 dimensional maps, but are not limited thereto.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the agricultural product is a liquid agricultural product and/or powdered agricultural product.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the agricultural product is a plant protection product, preferably an herbicide product and/or a pesticide product. Especially with these products, it is desirable to apply the product as precisely as possible to the target area in order to minimize effects on other areas.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the distance of the dropleg-type nozzle and the drone is fixed or variable. Such variable distance may be provided, for example, by a retractable or extendable hose-type body of the dropleg-type nozzle.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the control data for the drone further comprise flight speed data for the drone.

It is preferred that the drone does not exceed a maximum speed of 70 kph. It has been found that thereby undesirable turbulence effects caused by the drone speed may be avoided, i.e. a desirable homogeneous application of the product may be provided in a more robust way.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the method is further comprising: providing a drift model configured to determine a drift of the agricultural product at least based on turbulences caused by the drone; and wherein the control model is further configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone further based on the output of the drift model. The control data for the drone can additionally be provided in dependence of a drift model. By means of such a drift model, drone-specific control data can be provided, i.e. control data that are adapted to a respective drone and/or that take into account the respective/current operating state of a respective drone. Here, too, the aim is to ensure that the product can be applied to the plant as precisely as possible.

In an example the drift model may be adapted to determine turbulences and/or areas between the ground and/or plant where substantially no ground effects appear. The drift model may use a boundless model. The boundless model and/or the drift model may be adapted to determine a drift of a product leaving the nozzle under the influence of turbulences. The drift model may simulate a predefined configuration of the drone and/or the flight conditions like the flight height. The model may be continuously fed with current data during the flight of the drone, e.g. flight height and/or distance of the nozzle and the drone. The current data may be detected by sensors. The drift model may employ physical models and/or simulations, e.g. described by mathematical formulas, in order to provide substantially current and/or real time data. In another example the model may comprise a lookup table.

The drift model may be further configured to determine a drift of the agricultural product based on drone parameters. The drone parameters may comprise substantially fix dimension parameter of the drone such as the distance of the nozzle from the drone and/or dynamic parameter such as the distance between the field and/or the plant and the drone. The dynamic parameter may vary due to the fact that the growth height of a plant may differ.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the drift model is further configured to determine a drift of the agricultural product based on weather data. By taking into account such weather data (e.g. wind, air pressure, humidity, etc.), the accuracy with which the product can be applied to the plant can be further improved. In a further embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the drift model may be further configured to process and consider further parameters, like the flight speed when determining a drift of the agricultural product.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the drift model is further configured to determine a drift of the agricultural product based on drone parameters, wherein the drone parameters preferably comprise rotor data, drone load and/or drone weight.

In an embodiment of the computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, the agricultural product is a plant protection product and the control model is further configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a weed data.

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 control data for a drone;

Figure 5 illustrates a system for providing control data for a drone;

Figure 6 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.

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 control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field. In a first step, a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone are provided. In a further step, growth height data comprising the growth height of the plant cultivated on the agricultural field are provided. In a further step, distance data comprising the distance between the droplegtype nozzle and the drone are provided. In a further step, nominal distance data based on the growth height data and the distance data by utilizing the control model are provided. Finally, control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone are provided. The distance between the dropleg-type nozzle and the drone may substantially be known and constant. In an example the distance between the dropleg-type nozzle and the drone may be determined by a distance sensor such as a radar sensor, a GPS sensor, a position sensor, a LIDAR sensor and/or ultrasound sensor.

The reference for the distance measurement may be the rotor plane of the drone. A distance sensor may also be used to determine the distance between the drone and the plant, field and/or a respective surface. The height of a plant may relate to the average height of a plurality of plants being in close proximity.

The nominal distance may refer to a distance related to a reference such as a rotor plane of the drone. The rotor plane may be substantially the plane described by a single rotor and/or by a plurality of rotors. The rotor plane may substantially be parallel to a ground surface.

Figure 5 illustrates a system 10 for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field. The system 10 comprises a first providing unit 11 configured to provide a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone; a second providing unit 12 configured to provide growth height data comprising the growth height of the plant cultivated on the agricultural field; a third providing unit 13 configured to provide distance data comprising the distance between the dropleg-type nozzle and the drone; a fourth providing unit 14 configured to provide nominal distance data based on the growth height data and the distance data by utilizing the control model; and a fifth providing unit 15 configured to provide control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

Turbulences which may occur at different flight heights of a drone are illustrated in the publication "The computational fluid dynamic modeling of downwash flow field for a six-rotor UAV" (Zheng et al. in Frontiers of Agricultural Science and Engineering, 2018, Volume 5, pages 159-167). Full reference is made to the explanations in this publication together with the illustrations shown therein. In particular, regarding the calculation of such a downwash and the associated turbulences, reference is made to this publication and the further references cited therein. On page 165 of this publication, the turbulences from a multi-rotor drone at increasing altitudes are exemplarily shown, wherein the exemplary altitudes above the ground are: a = 1 m, b = 2 m and c = 3 m . d shows a boundless model with no ground effect. Based on the turbulence effects shown on page 165 of this publication, the concept of the present disclosure is to get the exit orifice of the nozzle down below the turbulent air created by the rotors or at least in an area/section of the turbulent air, which may still be acceptable. In other words, in a non limiting example a system combining a drone and a nozzle may be created in such a way that the exit orifice of the nozzle is located between a turbulence generated by the rotors of the drones and the field and/or plant. In another example the nozzle may reach into the plant, between the top of the plant and the surface of the field. The turbulence is generated during the flight, in particular during the rotor of the drone is working.

The application height, i.e. the distance between the nozzle and the target area is typically about 2.0 m (+/- 0.3 m) above the target, e.g. crop or weed plants. Thus, depending on the turbulences caused by the specific drone, a respective length of the dropleg of the dropleg-type nozzle may be provided. In the optimal case, a flight altitude of the drone may be provided that leads to turbulence as shown in Figure 6d (and/or Figure 8d of the Zheng publication) and the dropleg has a length that leads to an application of the product at a height of about 2 m above the target area, wherein the dropleg has guided/hold the dispensing nozzle outside of the turbulence area. By means of the present disclosure, an application by a drone may be provided, which may look like a ground boom application. In other words, the drone may be adapted in such a way that when the drone flies on a boundless height, in particular the substantially lowest height where boundless operation may be possible, the distance between nozzle and drone is such that the exit orifice of the nozzle is substantially out of the turbulence.

The height for flying the drone for applying the product is then chosen such that the distance between exit orifice of the nozzle and the target area, e.g. the top of plants, has a predefined value, e.g. substantially 2m.

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 control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: providing a control model configured to determine a nominal distance between a plant cultivated on the agricultural field and the dropleg-type nozzle of the drone based on a growth height of the plant and the distance between the dropleg-type nozzle and the drone; providing growth height data comprising the growth height of the plant; providing distance data comprising the distance between the dropleg-type nozzle and the drone; providing nominal distance data based on the growth height data and the distance data by utilizing the control model; providing control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.

2. Computer-implemented method according to claim 1, wherein the agricultural product is a liquid agricultural product, dry agricultural product and/or powdered agricultural product.

3. Computer-implemented method according to claim 1 or claim 2, wherein the agricultural product is a plant protection product, preferably an herbicide product and/or a pesticide product.

4. Computer-implemented method according to any one of the preceding claims, wherein the distance of the dropleg-type nozzle and the drone is fixed or variable.

5. Computer-implemented method according to any one of the preceding claims, wherein the control data for the drone further comprise flight speed data for the drone.

6. Computer-implemented method according to any one of the preceding claims, wherein the control data for the drone comprise flight height data for the drone providing a flight height of the drone between 1.0 and 3.0 m, preferably between 1.5 and 2.5 m and most preferably 2.0 m, above a target area. Computer-implemented method according to any one of the preceding claims, further comprising: providing a drift model configured to determine a drift of the agricultural product at least based on turbulences caused by the drone; and wherein the control model is further configured to determine the nominal distance between a plant and the dropleg-type nozzle of the drone further based on the output of the drift model. Computer-implemented method according to claim 7, wherein the drift model is further configured to determine a drift of the agricultural product based on weather data. Computer-implemented method according to claim 7 or claim 8, wherein the drift model is further configured to determine a drift of the agricultural product based on drone parameters, wherein the drone parameters preferably comprise rotor data, drone load and/or drone weight. Computer-implemented method according to any one of the preceding claims, wherein the agricultural product is a plant protection product and the control model is further configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a weed data. System for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: a providing unit configured to provide a control model configured to determine a nominal distance between a plant cultivated on the agricultural field and the droplegtype nozzle of the drone based on a growth height of the plant and a distance between the dropleg-type nozzle and the drone; a further providing unit configured to provide growth height data comprising the growth height of the plant; a further providing unit configured to provide distance data comprising the distance between the dropleg-type nozzle and the drone; a further providing unit configured to provide nominal distance data based on the growth height data and the distance data by utilizing the control model; a further providing unit configured to provide control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone. An apparatus for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural 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 method of one of claims 1 to 10. Drone comprising a dropleg-type nozzle configured to be controlled by control data provided according to any one of the claims 1 to 10. Use of a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone in a computer-implemented method according to any one of the claims 1 to 10 and/or in a system according to claim 11 and/or in an apparatus according to claim 12. Computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the computer- implemented method according to any one of the claims 1 to 10 in a system according to claim 11 or in an apparatus according to claim 12.