Patent Application
20240407352
The present disclosure relates to a computer-implemented method for monitoring the treatment of an agricultural field by an agricultural machine in real-time, a computing apparatus, a control unit, a treatment device an agricultural machine and a computer program or computer readable non-volatile storage medium for performing the method.
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 actual crops or the like, may also comprise the treatment of weed present in the agricultural area, the treatment of the insects present in the agricultural area, the treatment of the soil of the agricultural area as well as the treatment of pathogens present in the agricultural area.
In existing systems, cases may occur where reduced data handling and computing capabilities are present.
Therefore, there is a need to provide means for improving treatment monitoring and data aggregation, particularly in terms of comparability. It is accordingly an object of the present invention to provide more efficient and/or effective means for monitoring and treating an agricultural field. This object is solved by the subject-matter of the independent claims.
According to a first aspect of the present disclosure, a computer-implemented method for monitoring the treatment of an agricultural field by an agricultural machine in real-time is provided, wherein the agricultural machine comprises at least one sensor device and at least one treatment component, comprises the steps of providing location-specific sensor data of the agricultural field from the at least one sensor device, analyze the location-specific sensor data with respect to at least one treatment indicator and provide a treatment savings parameter, wherein the treatment savings parameter relates to an amount of treatment based on the location-specific sensor data in relation to an amount of treatment based on a reference treatment. In this way, the real-time data is aggregated with data from a reference treatment and comparison and data processing is facilitated. Further use by the one or other agricultural machines is enhanced.
A further aspect of the present disclosure relates to a computer-implemented method for monitoring the treatment of an agricultural field with a pesticide product by an agricultural machine, wherein the agricultural machine comprises at least one sensor device and at least one treatment component comprising at least one nozzle, the method comprising the steps: providing location-specific sensor data of the agricultural field from the at least one sensor device; analyzing the location-specific sensor data with respect to at least one harmful organism as one treatment indicator; generating location-specific control data for the at least one treatment component based on the analyzed location-specific sensor data; providing a pesticide savings parameter in real-time, wherein the pesticide savings parameter relates to an amount of pesticide product based on the location-specific sensor data in relation to an amount of pesticide product based on a reference treatment with a pesticide product.
In a further aspect of the present disclosure, the pesticide product is a herbicide product, the sensor device comprises at least one optical sensor and a number of weed plants and/or a density of weed is the at least one treatment indicator.
In a further aspect of the present disclosure, the method comprises additionally the step: adopting the control data for the at least one treatment component such that the pesticide savings parameter is set to 0; or such that an application is made with a predetermined application rate, i.e. a flat-application rate; or such that the threshold value for the application of the pesticide product is decreased or increased. A threshold value is understood to mean the value of the treatment indicator from which a treatment with a pesticide product takes place. If, for example, the treatment indicator is a weed density, then the threshold value can be used to specify at which weed density value an application with a herbicide takes place.
Reference treatment comprises data from a different treatment, for example, data from a flat, i.e. uniform, treatment, historic data from the same agricultural field, data from a nearby agricultural field or from another agricultural field sharing relevant traits.
The method may comprise additionally the step of generating location-specific control data based on the location-specific sensor data for at least one treatment component. In this way, the aggregated data can be directly used to facilitate the control data generation.
In a further aspect of the present disclosure, the location-specific control data relates to a location-specific on/off-operation of at least one treatment component. In this way, having a binary option only, data processing and transmission requirements are reduced.
In another aspect of the present disclosure, the method comprises additionally the step of controlling the at least one treatment component based on the location-specific control data. In this way, the aggregated data can be directly used to facilitate the control of the treatment component.
In another aspect of the present disclosure, the amount of treatment based on a reference treatment is not location-specific. In this way, the requirements for the needed data storage and processing means are reduced.
In another aspect of the present disclosure, the method comprises additionally the step of displaying the treatment savings parameter on a display unit. In this way, monitoring the treatment is facilitated.
In an even further aspect of the present disclosure, the method comprises additionally the step of updating the treatment savings parameter in real time. In this way, monitoring the treatment is facilitated, allowing for real-time adjustments and reactions.
In another aspect of the present disclosure, the treatment savings parameter is stored in a map of the agricultural field, in particular in a location-specific map of the agricultural field. In this way, further use for other agricultural machines or later treatments is facilitated.
In another aspect of the present disclosure, a computing apparatus is disclosed, especially a distributed computing system, comprising a communication interface for receiving and sending data, the computing apparatus being configured to receive location-specific sensor data via the communication interface, to analyze the location-specific sensor data with respect to at least one treatment indicator and to generate control data and send out the control data via the communication interface.
A treatment indicator can comprise at least one characteristic of the soil, the plant cover, the weather, life forms, cultivation phase, time, the treatment, in particular the type of treatment or the treatment product, and any other agricultural relevant parameter. It is used to determine the type and amount of treatment, which should be executed.
In another aspect of the present disclosure, a control unit for operating a treatment device for applying a treatment product to an agricultural field is disclosed. The control unit comprises a communication interface for sending and receiving data and the treatment device comprises at least one treatment component, wherein the control unit is configured receive control data and to provide control data to control the at least one treatment component.
In another aspect of the present disclosure, a treatment device for applying a treatment product to an agricultural field is disclosed. The treatment device comprises at least one treatment component and at least one sensor device. The treatment device is adapted to perform any of the above methods.
In another aspect of the present disclosure, an agricultural machine is disclosed. The agricultural machine comprises a computing apparatus, a control unit and a treatment device and is adapted to perform any of the above methods.
In another aspect of the present disclosure, a computer program or computer readable non-volatile storage medium is disclosed, comprising computer readable instructions, which when loaded and executed by a computing apparatus perform the methods of any of the above methods and/or control the treatment device and/or the agricultural machine.
The term “agricultural machine” is to be understood broadly in the present case and comprises any machine configured to treat an agricultural field. The agricultural machine may be configured to traverse the agricultural field. The agricultural machine may be a ground or an air vehicle, e.g. a tractor, a rail vehicle, a robot, an aircraft, an unmanned aerial vehicle (UAV), a drone, or the like. The agricultural machine may by equipped with one or more treatment devices. The treatment device may be configured to collect field data via treatment components and/or sensor devices. The treatment device may be configured to sense field data of the agricultural field via the sensor device. The treatment device may be configured to treat the agricultural field via the treatment component. The agricultural machine may be equipped with or operatively coupled with different apparatuses, for example a geolocation device, a communication interface, a sensor device, a control unit, a communication device, computing means and the like. Additionally, the agricultural machine may comprise a sensor device for acquiring a measure of the amount of treatment and/or treatment product applied to the agricultural field.
Treatment component(s) may be operated based on sensor signals provided by the sensor device(s) of the treatment device. The treatment device may comprise a communication interface and/or unit for connectivity. Via the communication unit the treatment device may be configured to provide, receive or send field data, to provide, send or receive operation data and/or to provide, send or receive control data.
The term “real-time” is to be understood broadly in the present case and is preferably understood as not more than 15 minutes, more preferably not more than 10 minutes, most preferably not more than 5 minutes, particularly preferably not more than 2 minutes, particularly more preferably not more than 60 seconds, particularly most preferably not more than 30 seconds, particularly not more than 15 seconds, particularly for example not more than 5 seconds, for example not more than 2 seconds. In other words, the pesticide savings parameter should be updated and displayed in said real time.
Such a real-time update makes it possible to intervene relatively quickly in the application of the pesticide or any other product. For example, it is possible to switch to a so-called flat application, so that no more pesticide savings are achieved, but a high efficacy can be assumed. Alternatively, it is possible to adapt the threshold values in order to influence the application of the product.
The term “pesticide” and/or “pesticide product” as used herein is to be understood broadly and encompasses any herbicide, fungicide, insecticide, acaricide, molluscicide, nematicide, avicide, piscicide, rodenticide, repellant, bactericide, biocide, safener, plant growth regulator, urease inhibitor, nitrification inhibitor, denitrification inhibitor, or mixtures thereof, i.e. the present disclosure is not limited to a specific kind of pesticide product. The term “herbicide product” as used herein is to be understood broadly in the present case and presents any herbicide material to be applied onto an agricultural field. Herbicides can specifically be referred to as selective or non-selective herbicides. A selective herbicide controls specific weed species, while leaving the desired crop relatively unharmed. In contrast, a non-selective herbicides, e.g. called total weed killers, kill all plant material with which they come into contact. A herbicide may be at least one one of the following, but is not limited thereto: acetamides, amides, aryloxyphenoxypropionates, benzamides, benzofuran, benzoic acids, benzothiadiazinones, bipyridylium, carbamates, chloroacetamides, chlorocarboxylic acids, cyclohexanediones, dinitroanilines, dinitrophenol, diphenyl ether, glycines, imidazolinones, isoxazoles, isoxazolidinones, nitriles, N-phenylphthalimides, oxadiazoles, oxazolidinediones, oxyacetamides, phenoxycarboxylic acids, phenylcarbamates, phenylpyrazoles, phenylpyrazolines, phenylpyridazines, phosphinic acids, phosphoroamidates, phosphorodithioates, phthalamates, pyrazoles, pyridazinones, pyridines, pyridinecarboxylic acids, pyridinecarboxamides, pyrimidinediones, pyrimidinyl(thio)benzoates, quinolinecarboxylic acids, semicarbazones, sulfonylaminocarbonyltriazolinones, sulfonylureas, tetrazolinones, thiadiazoles, thiocarbamates, triazines, triazinones, triazoles, triazolinones, triazolocarboxamides, triazolopyrimidines, triketones, uracils, ureas. Further, a herbicide may be, but are not limited thereto, lipid biosynthesis inhibitors, acetolactate synthase inhibitors (ALS inhibitors), photosynthesis inhibitors, protoporphyrinogen-IX oxidase inhibitors, bleacher herbicides, enolpyruvyl shikimate 3-phosphate synthase inhibitors (EPSP inhibitors), glutamine synthetase inhibitors, 7,8-dihydropteroate synthase inhibitors (DHP inhibitors), mitosis inhibitors, inhibitors of the synthesis of very long chain fatty acids (VLCFA inhibitors), cellulose biosynthesis inhibitors, decoupler herbicides, auxinic herbicides, auxin transport inhibitors, and/or other herbicides selected from the group consisting of bromobutide, chlorflurenol, chlorflurenol-methyl, cinmethylin, cumyluron, dalapon, dazomet, difenzoquat, difenzoquat-metilsulfate, dimethipin, DSMA, dymron, endothal and its salts, etobenzanid, flamprop, flamprop-isopropyl, flamprop-methyl, flamprop-M-isopropyl, flamprop-M-methyl, flurenol, flurenol-butyl, flurprimidol, fosamine, fosamine-ammonium, indanofan, indaziflam, maleic hydrazide, mefluidide, metam, methiozolin, methyl azide, methyl bromide, methyl-dymron, methyl iodide, MSMA, oleic acid, oxaziclomefone, pelargonic acid, pyributicarb, quinoclamine, tetflupyrolimet, triaziflam, tridiphane, and their agriculturally acceptable salts, amides, Isoxaflutole, Flufenacet, S-Metolachlor, Pendimethalin, Acetochlor, Pyroxasulfone, Cloransulam-methyl, Imazamethayr, Dimethenamid-P, Metamitrion, Ethofumesate, Quimerac, Prosulfocarb, Chlortoluron, Cinmethylin, Pendimethalin, esters or thioesters.
The term “weed distribution” as used herein is to be understood broadly in the present case and presents any data/information defining or indicating the existence, distribution and/or appearance of weed plants on the agricultural field. Weed plants are unwanted plants which should be controlled by using herbicides. The weed distribution data may be depicted as 2-dimensonal for one season or a plurality of seasons. The weed distributing data may be historical data indicating/depicting areas of high appearance/high density, i.e. hot-spots, of weeds. The weed distribution data may be provided by scouting, camera or sensor based mapping analysis methods.
The term “historical treatment” and/or “historic treatment” as used herein is to be understood broadly in the present case and presents any data/information providing, defining, describing or indicating historical treatments of the agricultural field. Specifically, the historical treatment data may comprise information about treatments performed in previous seasons on the agricultural field. The historical treatment data may be provided as 2-dimensional maps of the agricultural field depicting either treatment information for one specific previous season/sum of a plurality of specific previous seasons, e.g. depending on weather influences, or a sum for all previous seasons. The historical treatment data are provided by a database and/or a data system.
The term “agricultural field” as used herein is to be understood broadly in the present case and presents any area, i.e. surface and subsurface, of a soil to be treated by e.g. seeding, planting and/or fertilizing. The agricultural field may be any plant or crop cultivation area, such as a farming field, a greenhouse, or the like. A plant may be a crop, a weed, a volunteer plant, a crop from a previous growing season, a beneficial plant or any other plant present on the agricultural field. The agricultural field may be identified through its geographical location or geo-referenced location data. A reference coordinate, a size and/or a shape may be used to further specify the agricultural field.
The term “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.
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 agricultural machine and/or the treatment component. The control data may be provided by a control unit and may be configured to control one or more technical means of the agricultural machine and/or treatment component.
The term “harmful organism” is understood to be any organism which has a negative impact to the growth or to the health of the agricultural crop plant. Preferably, the harmful organism is selected from the group consisting of weeds, fungi, viruses, bacteria, insects, arachnids, nematodes, mollusks, birds, and rodents, more preferably, the harmful organism selected from the group consisting of weeds, fungi, and insects, most preferably, the harmful organism is weed.
Any disclosure and embodiments described herein relate to the methods, the apparatuses, the devices, the machines and the computer program 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 means to perform the respective action.
The methods, systems and computer means disclosed herein provide an efficient, sustainable and robust way for treating an agricultural field. By considering the monitoring and treatment status from the first agricultural machine, a second agricultural machine can be operated to treat a specific section of the agricultural field based on the monitoring and treatment status obtained by the first agricultural machine that specific section. Overall, this provides more tailored and more sustainable operation, since the second agricultural machine can treat based on the field data already acquired by the first agricultural machine. Multiple agricultural machines can hence be operated in an efficient, quasi on-demand manner.
The present disclosure can provide an efficient, sustainable and robust way of treating an agricultural field. These and other objects, which become apparent upon the following description, are solved by the subject matter of the independent claims. The dependent claims refer to preferred embodiments of the invention.
Exemplary embodiments will be described in the following with reference to the following drawings:
The disclosure is based on the finding that agricultural fields comprise heterogeneous characteristics (e.g. plant, weed, soil, etc.) distributed over the entire agricultural field. These characteristics are not permanent and therefore not completely known before the agricultural machines treat the agricultural field. By monitoring with sensor devices of an agricultural machine during a treatment process of an agricultural field, these specific characteristics of the agricultural field are at least partly revealed. The collected information about these specific characteristics serves to beneficially improve the treatment strategy of the one or more further agricultural machines. By doing so, it is possible to (re-)act on changing conditions in the agricultural field on demand. In other words, the method collects field data via means of the first and/or further agricultural machines passing through the agricultural field and provides data based on the field data, also for the second and/or further agricultural machines. This enables a demand driven treatment of the agricultural field with a plurality of agricultural machines and advantageously increases the treatment efficiency.
The following embodiments are mere examples for implementing the methods, the systems or the computer elements disclosed herein and shall not be considered limiting
Additionally or alternatively, the division of the agricultural field 11 may depend on the crops cultivated and/or at least one characteristic of an agricultural machine, for example a track width, a velocity or a working width. The agricultural field 11 may be treated by use of at least one of a treatment product, e.g. an herbicide, pesticide, insecticide, fungicide, nutrient.
The agricultural machine 10 crosses the agricultural field 11 at a velocity v. The agricultural machine 10 may cross the agricultural field 11 using a drive lane, in particular, the agricultural machine 10 may cross the agricultural field 11 at a substantially constant velocity and drive lanes may be arranged at a substantially equal spacing across the agricultural field 11. During the crossing of the agricultural field 11 the at least one sensor device may acquire location-specific sensor data in real-time from a specific location at or nearby the current location of the agricultural machine 10 in real-time. Preferably, the location-specific sensor data may be acquired repeatedly at a substantially constant frequency and/or at substantially uniform distances across the agricultural field.
The location-specific sensor data may be further processed by a computing means to derive control data, for example in form of a control file, to control the agricultural machine 10, a treatment device, the treatment component(s) and/or other components of the agricultural machine.
Based on the location-specific sensor data, the treatment component can be activated, blocked and/or adjusted to treat the agricultural field 11.
In particular, the treatment can be performed in substantially uniform subareas, which can be for example defined by the treatment area covered by a treatment component during a substantially fixed amount of time, for example by a single or a multiple of the location-specific sensor data acquisition time and/or dependent on the velocity of the agricultural machine 10.
Further, the agricultural field 11, may be any plant or crop cultivation field, such as a field, a greenhouse, or the like, at a geo-referenced location. As indicated in
The system 12 may comprise or form a distributed computing environment. It may comprise one or more of an agricultural machine 10, a first computing resource or means 14, a second computing resource or means 16, and a third computing resource or means 18. The agricultural machine 10 and/or the first, second and third computing means 14, 16, 18, may at least partly be remote to each other. At least some of the agricultural machine 10 and the first, the second and the third computing means 14, 16, 18 may comprise one or more of a data processing unit, a memory, a data interface, a communication interface, etc. Within the system 12, the agricultural machine 10 and the first, the second and the third computing means 14, 16, 18 may be configured to communicate with each other via communication means, such as a communications network, as indicated in
The first computing means 14 may be a data management system configured to send data to the agricultural machine 10 and/or to receive data from agricultural machine 10. For example, the data received from the agricultural machine 10 may comprise one or maps, such as a growth distribution map, a weed distribution map, or the like, which may be generated and/or provided based on data recorded during operation of the agricultural machine 10 and/or application of the treatment product at or on the agricultural field 11.
The second computing means 16 may be a field management system configured to generate and/or provide a control parameter set, which may comprise one or more of control data for operating the agricultural machine 10, a control protocol, an activation code, a set of threshold adjustments or a basic threshold, a decision logic to the agricultural machine 10, and/or to receive data from the agricultural machine 10. Such data may also be provided and/or received through the first computing means 14. The third computing means 18 may be a client computer configured to receive client data from and/or to provide data to at least the second computing means 16 and/or the agricultural machine 10. Such client data may, for example, comprise an application schedule for the treatment product to be applied on a specific agricultural area by operating the agricultural machine 10.
Additionally or alternatively, the client data may comprise field analysis data to provide insights into the health state, weed information, plant or crop information, geo-location data, or the like, of a specific agricultural area.
Further, when data is monitored, collected and/or recorded by the agricultural machine 10, such data may be distributed to one or more of, or even to every, computing means 14, 16, 18 of the distributed computing environments.
The treatment device 17 shown in
The spray nozzles 21 may be fixed or may be attached movable along the boom in regular or irregular intervals. Each spray nozzle 21 may arranged together with one or more, preferably separately, controllable valves 38 regulate fluid release from the spray nozzles 21 to the agricultural field 11.
One or more tank(s) 23, 24, 25 are in fluid communication with the nozzles 21 through one or more fluidic lines 26, which distribute the one or more treatment products as released from the tanks 23, 24, 25 to the spray nozzles 21. This may include chemically active or inactive ingredients like a treatment product or mixture, individual ingredients of a treatment product or mixture, a selective treatment product for specific weeds, a fungicide, a fungicide or mixture, ingredients of a fungicide mixture, ingredients of a plant growth regulator or mixture, a plant growth regulator, water, oil, or any other treatment product. Each tank 23, 24, 25 may further comprise a controllable valve (not shown) to regulate fluid release from the tank 23, 24, 25 to fluid lines 26. Such arrangement allows to control the treatment product or mixture released to the agricultural field 11 in a targeted manner depending on the conditions determined for the agricultural field 11.
For monitoring and/or detecting, the agricultural machine 10 (as shown in
In at least some embodiments, the sensor devices 31 may be arranged perpendicular to the movement direction of the treatment device 17 and in front of the nozzles 21 (seen from drive direction). In the embodiment shown in
The sensor devices 31, the tank valves and/or the nozzle valves 38 are communicatively coupled to a control unit 32. In the embodiment shown in
In an exemplary embodiment, the sensor device may comprise a fluid sensor operatively coupled with the fluidic lines 26. Additionally or alternatively, the sensor device may alternatively or additionally comprise a tank sensor for a tank of the agricultural machine.
The computing means may be completely or partly a part of the agricultural machine 10, or a distributed computing system and may comprise mobile devices communicatively coupled to the computing means.
In a second step 420, the location-specific sensor data may be analyzed with respect to at least one treatment indicator by the computing means.
In a third step 430, location-specific control data for the at least one treatment component are generated based on the analyzed location-specific sensor data.
In a fourth step 440, a treatment savings parameter for the treatment is provided. The treatment savings parameter characterizes the amount of treatment based on the location-specific sensor data in relation to an amount of treatment based on a reference treatment.
In one example, the treatment savings parameter is a comparison between a determined amount of treatment and a flat rate treatment, wherein the same amount of treatment is applied on the agricultural field. Additionally or alternatively, the reference treatment can also comprise historic data, in particular data from historic treatments.
In one exemplary embodiment, a comparison between the location-specific sensor data and a threshold is used to determine if the treatment component should be activated, for example switched between an on- and off-state. Alternatively or additionally, the strength of the activation and therefore the locally applied dosage can be controlled.
As an example, the number or density of weed present in the specific location of the agricultural field can be used. Alternatively or additionally, the number or density of cultivated crops present in the specific location of the agricultural field can be used. The threshold may be determined before the treatment based on previous available data and it may be adapted during the treatment.
The treatment savings parameter for a treatment can be defined in several ways, depending on the available data. The treatment savings parameter is a relation between data derived from in real-time acquired location-specific sensor data and reference treatment data.
In one exemplary embodiment, the treatment savings parameter can relate to a derived amount of treatment, which is not to be applied, and the reference data can relate to the amount of treatment used during a flat rate treatment.
In another exemplary embodiment, the treatment savings parameter can relate to the not applied amount of treatment and the reference data can relate to the amount of treatment used during a flat rate treatment.
In
In the embodiment depicted in
Therefore, treatment is performed in this example on the subareas Da, Db, Cc, Bd, Cd and Ce. No treatment is performed on the subareas Aa-Ca, Ab-Cb, Ac, Bc, Dc, Ad, Dd, Ae, Be and De. From the historic treatment data it is derived that treatment was done on all subareas, a flat treatment.
The treatment savings parameter can then be determined based on the number of non-treated subareas divided by the number of treated subareas derived from the historic treatment data in real-time. To illustrate the method, treatment is performed from rows D to A and over all columns a to e at the same time. So the treatment savings parameter could start to amount from 3/5=60% after treating row D, to 5/10=50% after treating rows D and C, to 9/15=60% after treating rows D to B and to 14/20=70% after treating rows D to A. Alternatively or additionally to an accumulated treatment savings parameter, a treatment savings parameter for each row can be determined in real-time. The treatment savings parameter for row D would be 3/5=60%, for row C 2/5=40%, for row B 4/5=80% and for row A 5/5=100%.
In the embodiment depicted in
The treatment savings parameter can then be determined for example based on the number of treatment component activations and the number of possible operations or the number of subareas, in particular from a log file generated by the agricultural machine and updated when activating, not activating and/or blocking the one or more treatment components of the agricultural machine. In this example the treatment savings parameter for row D′ could be derived in real time to 1-3/5=40%, for row C′ 1-3/5=40%, for row B′ 1-2/5=60% and for row A′ 1-5/5=100%. An accumulated treatment savings parameter for a treatment starting from row D′ to row A′ could the be derived to be 1-3/5=40% after treating row D′, 1-6/10=40% after treating rows D′ and C′, 1-8/15=46.67% after treating rows D′ to B′ and 1-13/20=70% after treating rows D′ to A′.
In the embodiment depicted in
In this example the treatment savings parameter for row D″ could be derived in real time to 1−(75%+75%+25%+50%+50%)/5=45%, for row C″ 1−(25%+25%+75%+100%+75%)/5=40%, for B″ 1−(0%+0%+50%+75%+0%)/5=75% and for A″ 1−(0%+50%+25%+50%+0%)/5=75%. An accumulated treatment savings parameter for a treatment starting from row D″ to row A″ could then be derived to be 45% after treating row D″, 42.5% after treating rows D″ and C″, 53.3% after treating rows D″ to B″ and 58.75% after treating rows D″ to A″.
In general, treatment savings parameter can alternatively or additionally be based on data from at least one of the following:
Additionally or alternatively, historic data can be used to compare the real-time treatment with a historic treatment or data from a treatment of another agricultural field can be used to compare with the actual treatment.
Data used for the treatment savings parameter can come from the processed real-time location-specific sensor data, such as from a determined location-specific amount of treatment or from control data, but also for example from machine data, external sources, such as external databases, or from other sensor devices, for example a tank sensor.
In a further embodiment the treatment savings parameter is stored on a storage device. This way applied maps can be recorded by storing the time, treatment savings parameter corresponding to such time, the position corresponding to such time and optionally the activation signal information coming from control data corresponding to such time. As a result, the data collected during operation can be stored and used after operation for further analysis. Here the real or determined treatment may be recorded optionally together with the activation signal including the information on which treatment component was triggered when with which activation signal. In this way the treatment savings parameter can be provided for further use, for example for another treatment, another type of treatment or another agricultural machine as input for a treatment.
The displayed information may be updated in real-time or at a specific frequency, for example 1 Hz. Exemplarily, the treatment savings parameter 68 can be updated based on the provided treatment savings parameter or the information about the agricultural field 62 based on location data.
Additionally, the display output may be modified based on external input, especially on input from a human-machine interface, for example from an operator of the agricultural machine. For example, a treatment indicator or other performance parameter may be modified based on external input.
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 computer-executable instructions or data structures can be transferred automatically from transmission media to storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computing system RAM and/or to less volatile storage media at a computing system. Thus, it should be understood that storage media can be included in computing components that also (or even primarily) utilize transmission media.
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
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.
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.
| Number | Date | Country | Kind |
|---|---|---|---|
| 21204882.1 | Oct 2021 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/079856 | 10/26/2022 | WO |
