METHOD AND SYSTEM FOR MONITORING AUTONOMOUS AGRICULTURAL PRODUCTION MACHINES

A method for monitoring autonomous agricultural production machines is disclosed. The autonomous agricultural production machine autonomously performs an agricultural job. When an anomaly occurs, the autonomous agricultural production machine performs a response routine, interrupting the performance of the agricultural job. The autonomous agricultural production machine senses anomaly data during and/or after the response routine and transmits the anomaly data to a remote monitoring center in a reporting routine that a user may access in the remote monitoring center. The remote monitoring center generates, based on the anomaly data, a control instruction and transmits the control instruction to the autonomous agricultural production machine to execute in order to further respond to the anomaly and thereafter continue to perform the agricultural job.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to German Patent Application No. DE 10 2022 110 213.0 filed Apr. 27, 2022, the entire disclosure of which is hereby incorporated by reference herein. This application incorporates by reference herein the following US applications in their entirety: U.S. application Ser. No. ______ entitled “AUTONOMOUS AGRICULTURAL PRODUCTION MACHINE” (attorney docket no. 15191-23004A (P05575/8)); U.S. application Ser. No. ______ entitled “SWARM ASSISTANCE SYSTEM AND METHOD FOR AUTONOMOUS AGRICULTURAL UNIVERSAL PRODUCTION MACHINES” (attorney docket no. 15191-23005A (P05576/8)); U.S. application Ser. No. ______ entitled “METHOD AND SYSTEM FOR MONITORING OPERATION OF AN AUTONOMOUS AGRICULTURAL PRODUCTION MACHINE” (attorney docket no. 15191-23007A (P05580/8)); and U.S. application Ser. No. ______ entitled “SYSTEM AND METHOD FOR DEPLOYMENT PLANNING AND COORDINATION OF A VEHICLE FLEET” (attorney docket no. 15191-23008A (P05585/8)).

TECHNICAL FIELD

The present application relates to a method for monitoring autonomous agricultural production machines, to an autonomous agricultural production machine, and to a use of an autonomous agricultural production machine.

BACKGROUND

Autonomous agricultural production machines, such as autonomous combine harvesters, autonomous forage harvesters, autonomous tractors, and autonomous agricultural universal production machines, may perform various agricultural tasks automatically.

DETAILED DESCRIPTION

As discussed in the background, autonomous agricultural production machines, such as any one, any combination, or all of autonomous combine harvesters, autonomous forage harvesters, autonomous tractors, and autonomous agricultural universal production machines, may perform various actions automatically. Thus, in one or some embodiments, any discussion herein regarding autonomous may comprise automatic operation without any human intervention. However, the autonomous agricultural production machines may face common challenges and perform agricultural tasks largely on their own. Problematically, the autonomous agricultural production machines may not be able to continue their work in all unexpected situations. Also, in some expected situations, the only available or viable option may be to automatically stop the autonomous agricultural production machine. This may result in a user having to monitor the autonomous agricultural production machine for automatic stoppage. In this regard, one of the main advantages of an autonomous agricultural production machine, in particular its autonomy and its completely automatic operational nature, may not then be fully realized.

Thus, in principle, approaches for monitoring or remotely controlling autonomous agricultural production machines are known, but suffer from problems that still leave much potential open in terms of results. It is therefore a challenge to optimize autonomous agricultural production machines in terms of their autonomy.

One consideration is that an autonomous agricultural production machine usually has enough sensors to allow a remote monitoring center to decide how to respond to an anomaly based only on the sensor data. It may therefore be sufficient for an autonomous agricultural production machine to automatically initiate a response routine when an anomaly is present, and then outsource the decision on how to proceed further to a user or a more powerful artificial intelligence (AI) that does not need to be on site. Therefore, a remote monitoring center is disclosed to which the autonomous agricultural production machine transmits sensor data on an anomaly, and from which a control instruction is generated on how the autonomous agricultural production machine should proceed further.

Specifically, in one or some embodiments, the autonomous agricultural production machine is configured to sense an anomaly (e.g., sense anomaly data indicative of the anomaly) during and/or after the response routine, and is configured to transmit the anomaly data to a remote monitoring center in a reporting routine. In response to the transmission, the remote monitoring center is configured to generate, based on the anomaly data, a control instruction and transmit the control instruction to the autonomous agricultural production machine in order for the autonomous agricultural production machine to address the anomaly. In response to receiving the control instruction, the autonomous agricultural production machine is configured to execute the control instruction, and then continue to perform the agricultural job.

In one or some embodiments, the monitoring may be activated as a service using the remote monitoring center. In this way, this may be a simple way to offer or make use of the remote monitoring as needed.

In one or some embodiments, the autonomous agricultural production machine is configured to perform an emergency stop in the response routine, and/or that an anomaly is indicative of an obstacle (to which the autonomous agricultural production machine responds with performing an emergency stop). Obstacles may be the most common and dangerous anomalies that may occur when performing an agricultural job using an autonomous agricultural production machine. Given the monitoring by the remote monitoring center, an emergency stop may be an acceptable emergency solution in almost every case since the remote monitoring center may decide then how or whether to continue the agricultural job. It is therefore unproblematic when, if necessary, an emergency stop is performed more frequently than may be necessary.

One embodiment concerns two options for how the remote monitoring center may proceed in interaction with the autonomous agricultural production machine. On the one hand, it is contemplated for a routine (such as the response routine) that is to be executed to already be saved in the autonomous agricultural production machine, and it only has to be selected and accessed from memory, whereby little data need be transmitted; on the other hand, it is also contemplated that the autonomous agricultural production machine is remotely controlled using the remote monitoring center. In this regard, the routine (such as the response routine) need not be resident within the autonomous agricultural production machine in order for the routine to control the autonomous agricultural production machine.

In one or some embodiments, the remote monitoring center is configured to monitor a plurality of autonomous agricultural production machines. In this way, simple and efficient monitoring of many autonomous agricultural production machines may be achieved.

In one or some embodiments, an AI model, through which the autonomous agricultural production machines may be controlled, may be re-trained by linking the anomaly data and the control instructions of the remote monitoring center in response to the anomalies. In this way, the AI model (and in turn the autonomous agricultural production machines controlled by the AI model) may be successively improved based on real data.

In one or some embodiments, a distinction may be made between two concepts of autonomous agricultural machines. On the one hand, autonomous agricultural machines may be specialized, such as an autonomous combine harvester or even an autonomous wheat combine harvester, or the autonomous agricultural machines may be generalized. Thus, in one embodiment, generalized autonomous agricultural production machines comprise autonomous agricultural universal production machines. These autonomous universal agricultural production machines may be distinguished by the fact that they may be used for a variety of different agricultural jobs by changing configurations such as changing work assemblies and changing software modules.

In particular, such autonomous universal agricultural production machines may have decisive advantages in terms of their capacity and purchase costs, but may have the disadvantage that they are technically more demanding, especially in terms of software. An AI model that is trained to always harvest only wheat with the same technical equipment may be technically easier to realize or to train than an AI model that may perform any agricultural job with any equipment. Therefore, the amount of anomalies in the sense of states or measured values that were unexpected may also be disproportionately greater in an autonomous agricultural universal production machine than in an autonomous production machine that may be precisely or specifically adapted to its particular activity. Therefore, the need for remote monitoring may also be greater for autonomous agricultural universal production machines, which may make the disclosed solution particularly advantageous in this case.

In one or some embodiments, various types of the anomaly data are contemplated. In one or some embodiments, provision may be made for the user in the remote monitoring center to access additional data from a database beyond or separate from the anomaly data. This database may include general background information such as field information data or weather data. In particular, the database may include data that are not accessible to the autonomous agricultural production machine.

In one or some embodiments, the autonomous agricultural production machine is configured to continuously send data to the remote monitoring center. The term “continuous” or “continuously” may generally refer to processes which occur repeatedly over time independent of an external trigger to instigate subsequent repetitions. In some instances, continual processes may repeat in real time, having minimal periods of inactivity between repetitions. In some instances, periods of inactivity may be inherent in the continual process. Therefore, in the event of an anomaly, the user in the remote monitoring center may directly access a large amount of current and historical data. Furthermore, monitoring of the autonomous agricultural production machine is also made possible on an ad hoc basis, for example on a random or regular basis. Alternatively, it may be provided that the autonomous agricultural production machine only sends data to the remote monitoring center after it has triggered the response routine. In this way, data traffic may be kept to a minimum.

In one or some embodiments, unless the anomaly may be resolved remotely, a service technician may be dispatched.

In one or some embodiments, in the event of an anomaly, at least one further agricultural production machine of a network of agricultural production machines in which the autonomous agricultural production machine is operating is configured to transmit environment sensor data to the remote monitoring center that depicts or characterizes the autonomous agricultural production machine and/or the immediate environment. This may allow the user in the remote monitoring center to get a wider view of an obstacle, for example.

In one or some embodiments, the remote monitoring center may monitor broader activities besides the agricultural job, and therefore may generally monitor the use of the autonomous agricultural production machine and, in turn, ensure that the autonomous agricultural production machine is performing its work.

In one or some embodiments, an autonomous agricultural production machine is claimed to be configured for use in the disclosed method. Reference is made to all statements regarding the disclosed method.

In one or some embodiments, a use of an autonomous agricultural production machine in the disclosed method. Reference is made to the disclosed method, and the disclosed autonomous agricultural production machine.

In one or some embodiments, an exemplary application is a harvesting process. This harvesting process may comprise, for example, the process chain of one or both of the agricultural jobs “harvesting a crop” and “salvaging the crop”.

As a rule, this process chain may be executed in such a way that one or more agricultural production machines designed as combine harvesters1first harvest the crop grown on a cultivated area (seeFIG.1). As an example, the part of the harvested material formed by the fruit may be temporarily stored in a grain tank on the combine harvester1while the remaining part of the harvested material (e.g., the straw) may be deposited in windrows on the cultivated area. When the straw deposited in windrows has reached a moisture content that allows the straw to be stored, a baler pulled by a tractor may compress the straw into bales of the harvested material that are first deposited on the cultivated area.

In another step of the process chain, the harvested material bales may be loaded by so-called lift trucks onto platform trailers towed by tractors, for example, and transported away for storage. Similarly, the fruit temporarily stored in the grain tank may be taken by tractor-drawn transport trailers and sent to storage or further processing.

In the present case, one or more of these activities may now be performed by autonomous agricultural production machines3, such as autonomous agricultural universal production machines4in various configurations.FIG.1shows, for example, cooperation between four autonomous agricultural universal production machines4and two autonomous combine harvesters1during harvesting.

Alternatively, it is also contemplated that the autonomous agricultural universal production machines4are used as a forage harvester6via configuration changes (e.g., by equipping them with corresponding work assemblies5). It is contemplated, for example, that a rudimentary forage harvester7may be operated as a work assembly5with little electronics and no traction drive by means of one or more autonomous agricultural universal production machines4, in that the autonomous agricultural universal production machines4may serve as a traction drive and control and may be docked to the rudimentary forage harvester7(seeFIG.3). The rudimentary forage harvester7may have computing functionality16, which may include at least one processor14, at least one memory15, a user interface17(e.g., a touchscreen), and a communication interface18. Communication interface18may be configured to communicate (e.g., wired and/or wirelessly) with one or more other external electronic devices, such as remote monitoring center9. Further, the rudimentary forage harvester7may include one or more sensors19in which to sense various aspects of its operation and/or of its environment (e.g., to sense one or more obstacles), as discussed herein.

A method for monitoring autonomous agricultural production machines3is disclosed, wherein the autonomous agricultural production machine3may autonomously or automatically perform an agricultural job, wherein when an anomaly occurs, the autonomous agricultural production machine3is configured to perform a response routine, and wherein the autonomous agricultural production machine3is configured to respond to the anomaly in the response routine and to interrupt the performance of the agricultural job.

As will be explained further below, the response routine may include an emergency stop of the autonomous agricultural universal production machine4and may additionally or alternatively include changing work assemblies5to a safe state. For example, if an autonomous agricultural universal production machine4towing a transport trailer2encounters an obstacle8, it may simply automatically stop. However, the problem is that it may regularly lack the capabilities to safely maneuver relatively unknown transport trailers2around the obstacle8or even to assess whether such a maneuver is safe and/or appropriate.

In one or some embodiments, it may be essential that the autonomous agricultural production machine3is configured to sense anomaly data during and/or after the response routine (e.g., during and/or after execution of the response routine) and to transmit the anomaly data to a remote monitoring center9in a reporting routine, so that any one, any combination, or all of the following is performed: a user10may access the anomaly data in the remote monitoring center9; the remote monitoring center9generates a control instruction for the autonomous agricultural production machine3(either fully automatically without input from the user10or based on user input from the user10) to further respond to the anomaly; and the remote monitoring center9transmits the control instruction to the autonomous agricultural production machine3; and that the autonomous agricultural production machine3automatically executes the control instruction and then automatically continues to perform the agricultural job.

In one or some embodiments, the remote monitoring center9comprises at least one computing device, such as a server sitting on the Internet. The remote monitoring center9may comprise at least one processor14and at least one memory15that stores information and/or software, with the processor configured to execute the software stored in the memory. Further, the remote monitoring center9may include a user interface17(e.g., a touchscreen) and a communication interface18, which may be configured to communicate with one or more external electronic devices (e.g., autonomous agricultural production machine3; autonomous agricultural universal production machine4; etc.) wired and/or wirelessly. Thus, in one or some embodiments, the remote monitoring center9may comprise any type of computing functionality, such as the at least one processor14(which may comprise a microprocessor, controller, PLA, or the like) and the at least one memory15. The memory15may comprise any type of storage device (e.g., any type of memory). Though the processor14and the memory15are depicted as separate elements, they may be part of a single machine, which includes a microprocessor (or other type of controller) and a memory. Alternatively, the processor14may rely on memory15for all of its memory needs.

The processor14and memory15are merely one example of a computational configuration. Other types of computational configurations are contemplated. For example, all or parts of the implementations may be circuitry that includes a type of controller, including an instruction processor, such as a Central Processing Unit (CPU), microcontroller, or a microprocessor; or as an Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or Field Programmable Gate Array (FPGA); or as circuitry that includes discrete logic or other circuit components, including analog circuit components, digital circuit components or both; or any combination thereof. The circuitry may include discrete interconnected hardware components or may be combined on a single integrated circuit die, distributed among multiple integrated circuit dies, or implemented in a Multiple Chip Module (MCM) of multiple integrated circuit dies in a common package, as examples. The above discussion regarding the at least one processor14and the at least one memory15may be applied to other devices, such as computing functionality that may be resident in any one, any combination, or all of: combine harvester1, transport trailer2; autonomous agricultural production machine3; autonomous agricultural universal production machine4; work assembly5; forage harvester6; rudimentary forage harvester7; or AI model11.

In principle, it is contemplated that a user10in the remote monitoring center9may access the anomaly data. Additionally or alternatively, artificial intelligence (AI), which may be manifested in AI model11, may access the anomaly data and generate the control instruction (e.g., without any input from the user10so that the remote monitoring center9generates the control instruction fully automatically). The AI model11may be configured to perform one or both of the following: classifying; or controlling. In one or some embodiments, classifying may comprise identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. In one or some embodiments, controlling may comprise determining, based on a classification, one or more actions to perform. Merely by way of example, responsive to identifying an obstacle (based on sensor input) and/or identifying a specific type of obstacle, the AI model11may be configured to perform a certain action (e.g., which may be manifested by the control instruction), thereby performing both classifying and controlling. Thus, the AI model may be based on a trained neural network (e.g., supervised and/or unsupervised learning) as the machine learning method.

The AI may transmit the control instruction directly to the autonomous agricultural production machine3or to the user10who may confirm or modify it. For example, at least one processor associated with AI model may access the anomaly data in a memory and generate/transmit the control instruction to the autonomous agricultural production machine3. It is also contemplated that one set of anomalies may be handled by the user10(e.g., the user reviews the anomaly and determines the control instruction to send) and another set of anomalies may be handled by the AI. In this case, the AI may automatically decide, for example, using its processor and on the basis of security, whether the anomaly should be presented to a user10. This may also make it possible to provide a higher performance AI in the remote monitoring center9than in the autonomous agricultural production machine3.

This may allow the user10to decide how to respond to the anomaly. Continuing to perform the agricultural job is, of course, not envisaged in every case, but should be the goal usually sought. The remote monitoring center9may make it possible for the autonomous agricultural production machine3to perform the agricultural job without local monitoring, without its owner having to regularly check up on it or go out himself in case of anomalies, and without its owner finding an unfinished agricultural job in the evening.

Furthermore, in one or some embodiments, the monitoring using the remote monitoring center9may be enabled as a service (e.g., based on user input (e.g., via a touchscreen) indicative of a request by a user) using the autonomous agricultural production machine3, such as via a terminal on or of the autonomous agricultural production machine3. The autonomous agricultural production machine3may therefore be integrated as needed into the remote monitoring system as required without any hardware changes.

In one or some embodiments, the autonomous agricultural production machine3may perform an automatic emergency stop in the response routine. For example, the autonomous agricultural production machine3may, using its processor in executing the response routine, may determine to perform the automatic emergency stop and to control itself accordingly (e.g., control the drive resident on the autonomous agricultural production machine3to stop and/or control the work assembly5connected to the autonomous agricultural production machine3to stop). An emergency stop may comprise a stop of a travel movement and/or of a work assembly5.

One example anomaly comprises an obstacle8. Specifically, the autonomous agricultural production machine3may respond with the response routine to the obstacle8. Since an autonomous agricultural production machine3should, in case of doubt, preferably detect a non-existing obstacle8than not detect an existing obstacle8(e.g., err on the side of false positive of detecting an obstacle8), real and unreal obstacles8may occur regularly. At the same time, in any case previously unknown obstacles8in a field are more or less by definition unexpected, whereby it is to be expected that the agricultural production machine may occasionally be unable to respond to the obstacle8. The remote monitoring center9may provide a simple remedy for this. In one or some embodiments, the control instruction is an instruction for automatically starting a predefined routine stored in the autonomous agricultural production machine3, and/or that the user10and/or the AI remotely controls the autonomous agricultural production machine3using one or a plurality of control instructions, such as for avoiding the obstacle8.

In one or some embodiments, the remote monitoring center9and the autonomous agricultural production machine3are configured such that the user10and/or the AI may use a predefined routine or remotely control the autonomous agricultural production machine3after the anomaly is present, such as depending on whether one of the predefined routines is adequate to respond to the anomaly according to the user's assessment.

In one or some embodiments, the remote monitoring center9is configured to automatically monitor a plurality of autonomous agricultural production machines3while the plurality of autonomous agricultural production machines3are performing a plurality of agricultural jobs, the plurality of autonomous agricultural production machines3automatically performs response routines and automatic reporting routines when anomalies occur, and the remote monitoring center9generates control instructions for the particular autonomous agricultural production machines3based on anomaly data from the plurality of autonomous agricultural production machines3.

Therefore, for efficiency reasons, in one or some embodiments, the remote monitoring center9is configured to automatically monitor many autonomous agricultural production machines3of many farms and/or owners.

Further, in one or some embodiments, the autonomous agricultural production machines3perform the agricultural jobs automatically controlled by means of an AI model11. In order to improve this AI model11over time, the anomaly data and the control instructions of the remote monitoring center9may be linked to form training data12, and that the AI model11may be re-trained based on the linked training data12. Additionally or alternatively, the AI in the remote monitoring center9may be re-trained in this way.

As a result, the autonomous agricultural production machine3therefore may automatically learn (e.g., indirectly learn), for example via software updates, from past reactions of the user10to anomalies, such as from a plurality of reactions when there are a plurality of anomalies that have occurred in a plurality of autonomous agricultural production machines3.

This variant may become more interesting the greater the number of autonomous agricultural production machines3are monitored by the remote monitoring center9.

In one or some embodiments, the autonomous agricultural production machine3is an autonomous agricultural universal production machine4, or that the plurality of autonomous agricultural production machines3are autonomous agricultural universal production machines4.

Each autonomous agricultural universal production machine4may be configurable to perform a plurality of different agricultural jobs by being equipped with alternate work assemblies5.

In one or some embodiments, an autonomous agricultural universal production machine4is a production machine that may autonomously perform an agricultural job (e.g., without close user monitoring and automatically based on its own actions). In this case, the autonomous agricultural universal production machine4is an unmanned production machine. It may work by its itself or in a network.

Furthermore, the autonomous agricultural universal production machine4may be configured to perform a variety of different agricultural jobs, such as by replacing or attaching work assemblies5.

In one or some embodiments, when the work assembly5is changed for an autonomous agricultural universal production machine4, control assemblies concerning the work assembly5may additionally be changed, or control assemblies may be added. It is also contemplated that two (or more than two) autonomous agricultural universal production machines4jointly automatically operate a work assembly5(seeFIG.3), so that, for example, two autonomous agricultural universal production machines4together with a larger supporting structure with various work assemblies5function as a forage harvester6, combine harvester1, or something else.

In one or some embodiments, the autonomous agricultural universal production machine4or autonomous agricultural universal production machines4are individualized for their particular agricultural job by means of process knowledge. Such process knowledge may include any one, any combination, or all of: optimized settings of machine parameters; parameterizations of software modules; weightings by neural networks; a route plan; or an optimization strategy or the like. The specific process data may comprise, for example, field information data such as any one, any combination, or all of: a fruit type of a crop; a soil type; a soil slope; field-independent process data such as existing operating resources; or preceding and/or subsequent work steps. General process data may include non-specific process data such as optimized settings of the autonomous agricultural universal production machine4for a harvesting operation. Environmental data may be such data that do not directly affect the field but generally affects a larger environment, for example weather data, temperature data and the like. The process knowledge may be automatically used by the autonomous agricultural universal production machine4to perform the agricultural job; in particular, the autonomous agricultural universal production machine4may automatically use the process knowledge to set machine parameters.

In one or some embodiments, the process knowledge comprises work-assembly-specific work assembly knowledge5at least for some, or for all, work assemblies5with which the autonomous agricultural universal production machine4may be equipped. In this case, the autonomous agricultural universal production machine4need not have work assembly-specific work assembly knowledge5for some or all of the work assemblies5with which it may be equipped, at least in a basic or factory configuration. Alternatively, the autonomous agricultural universal production machine4may have work assembly type-specific work assembly knowledge5for some or all of the work assemblies5with which it may be equipped, at least in the basic or factory configuration. For example, the autonomous agricultural universal production machine4may have a basic set of plow-specific work assembly knowledge5, but may be equipped by an external source for the individual agricultural job with work assembly knowledge5concerning the exact type of plow that allows more efficient use of the plow.

In one or some embodiments, therefore, the autonomous agricultural universal production machine4may be designed in such a way that it cannot use the particular work assembly5without the work-assembly-specific work assembly knowledge5, or may use it only on the basis of the work-assembly-type-specific work-assembly knowledge5.

In one or some embodiments, the machine parameters may be machine parameters in the narrow sense, such as the engine speed and/or a position of a choke valve. Also included may be settings of a rear power lift or the like. The machine parameters may also comprise instructions for setting automatic setting devices or other control systems of the autonomous agricultural universal production machine4from which machine parameters in the narrow sense are then generated.

In one or some embodiments, the user10and/or the AI in the remote monitoring center9has access to the process knowledge or a portion of the process knowledge.

In one or some embodiments, the anomaly data may comprise any one, any combination, or all of: environment data; machine data; a driving route; work assembly data of the autonomous agricultural production machine3; or GPS data of the autonomous agricultural production machine3.

Additionally or alternatively, the user10and/or the AI in the remote monitoring center9may access further data relating to any one, any combination, or all of: the agricultural job; the autonomous agricultural production machine3; or the environment of the autonomous agricultural production machine3which may be stored in a database13of the remote monitoring center9. Thus, in one or some embodiments, the further data may comprise environmental data (e.g., weather data) and/or field information data.

In one or some embodiments, the weather data and/or field information data may be provided to the remote monitoring center9by a farm management information system or the like.

Further, in one or some embodiments, the autonomous agricultural production machine3may continuously send data to the remote monitoring center9outside of the response routine while performing the agricultural job and/or may store data in a cloud, such as in the farm management information system, or that the autonomous agricultural production machine3only sends data to the remote monitoring center9responsive to the autonomous agricultural production machine3triggering the response routine.

In one or some embodiments, the user10and/or the AI in the remote monitoring center9may dispatch a local service technician when the anomaly is identified as a malfunction, and/or for the user10and/or the AI in the remote monitoring center9to dispatch a local service technician when a data connection with the autonomous agricultural production machine3is lost for at least a defined period of time.

In one or some embodiments, the remote monitoring center9may have a listing of service technicians for this purpose and, if necessary, know their workload. In one or some embodiments, the service technician closest to the field is typically involved. Prioritization (such as automatic prioritization by the remote monitoring center9), for example, may be automatically performed according to the urgency or economic damage of the anomaly.

In one or some embodiments, the agricultural job is performed by a network of agricultural production machines, that at least one other agricultural production machine transmits environment sensor data to the remote monitoring center9after the autonomous agricultural production machine3has triggered the response routine, that the environment sensor data depict the autonomous agricultural production machine3and/or its immediate environment.

In one or some embodiments, a network may be understood to be a group of agricultural production machines cooperating and communicating with each other. If the sensor data of the autonomous agricultural production machine3is not sufficient to understand the anomaly, the user10and/or the AI in the remote monitoring center9may actively query environmental sensors from other agricultural production machines in the vicinity of the autonomous agricultural production machine3(e.g., the AI in the remote monitoring center9may automatically actively query environmental sensors from other agricultural production machines in the vicinity of the autonomous agricultural production). Alternatively, the environment sensor data may be automatically transmitted to the remote monitoring center9, such as triggered by a communication automatically sent from the autonomous agricultural production machine3in the response routine to the network.

In one or some embodiments, the remote monitoring center9automatically monitors any one, any combination, or all of: the agricultural job; a preparation of the agricultural job; the approach of the agricultural job; or follow-up after the agricultural job was automatically performed.

In one or some embodiments, an autonomous agricultural production machine3may be configured for use in the disclosed method. Reference may be made to all statements regarding the proposed method.

In one or some embodiments, an autonomous agricultural production machine3may be used in the disclosed method. Reference may be made to all statements regarding the disclosed method.

Further, it is intended that the foregoing detailed description be understood as an illustration of selected forms that the invention may take and not as a definition of the invention. It is only the following claims, including all equivalents, that are intended to define the scope of the claimed invention. Further, it should be noted that any aspect of any of the preferred embodiments described herein may be used alone or in combination with one another. Finally, persons skilled in the art will readily recognize that in preferred implementation, some, or all of the steps in the disclosed method are performed using a computer so that the methodology is computer implemented. In such cases, the resulting physical properties model may be downloaded or saved to computer storage.

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