system

Abstract

The system according to this embodiment aims to monitor the conditions of farmland and optimally control the operation of harvesting robots and drones. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, and a control unit. The monitoring unit monitors the conditions of the farmland. The analysis unit analyzes the data collected by the monitoring unit. The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the situation of farmland has not been efficiently monitored, and the operations of harvesting robots and drones have not been optimally controlled, leaving room for improvement.

[0005] The system according to the embodiment aims to monitor the situation of farmland and optimally control the operations of harvesting robots and drones.

Means for Solving the Problems

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, and a control unit. The monitoring unit monitors the situation of farmland. The analysis unit analyzes the data collected by the monitoring unit. The control unit controls the operations of the harvesting robot and the drone based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the conditions of the farmland and optimally control the operation of harvesting robots and drones. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides an agricultural work efficiency system that integrates multiple harvesting robots and drones to perform agricultural work efficiently. In this agricultural work efficiency system, each robot has an individual role, and an AI agent automates the optimal work planning and execution. For example, the agricultural work efficiency system uses an AI agent to monitor the condition of the farmland in real time and control the movements of the harvesting robots and drones. For example, the harvesting robots harvest fruits and vegetables, while the drones monitor the entire farmland and collect data. This allows each robot to perform its work efficiently. Next, the agricultural work efficiency system analyzes the data collected by the AI ​​agent and creates an optimal work plan. For example, the timing of harvesting can be adjusted according to weather and seasonal fluctuations. This improves the efficiency of agricultural work and maximizes the yield. Furthermore, the agricultural work efficiency system uses an AI agent to control the movements of the harvesting robots and drones in real time and monitor the progress of the work. For example, when a harvesting robot is harvesting fruit, the drone can monitor the situation and issue instructions as needed. This improves the accuracy of the work and reduces unnecessary movements. This mechanism can solve problems faced by farmers and farmland managers, such as labor shortages and the difficulty of creating efficient work plans. Furthermore, by utilizing generative AI, AI agent technology with personality, memory, planning, and behavioral capabilities can be realized, further advancing the automation and efficiency of agricultural work. For example, when an AI agent controls the operation of a harvesting robot to harvest fruit, a drone can monitor the entire farmland and grasp the progress of harvesting in real time. This allows for efficient harvesting and maximizes yields. In addition, by adjusting the timing of harvesting according to weather and seasonal changes, the quality of crops can be improved and yields can be maximized. For example, harvesting before bad weather occurs can maintain the quality of crops. Thus, a system in which an AI agent integrates harvesting robots and drones to perform agricultural work efficiently is extremely useful for farmers and farm managers, enabling increased efficiency and automation in agriculture.

[0029] The agricultural work efficiency system according to this embodiment comprises a monitoring unit, an analysis unit, and a control unit. The monitoring unit monitors the conditions of the farmland. The conditions of the farmland include, but are not limited to, the soil condition, crop growth status, and pest occurrence status. The monitoring unit monitors the soil moisture content using, for example, a soil sensor. The monitoring unit can also monitor the crop growth status using a camera. Furthermore, the monitoring unit can monitor the pest occurrence status using a pest sensor. For example, the monitoring unit monitors the soil moisture content in real time using a soil sensor and collects data. It periodically photographs the crop growth status using a camera and collects image data. It detects the occurrence status of pests using a pest sensor and collects data. The analysis unit analyzes the data collected by the monitoring unit. For example, machine learning algorithms and statistical analysis methods are used for the analysis, but are not limited to these. For example, the analysis unit analyzes soil moisture content data using a machine learning algorithm and estimates the optimal irrigation timing. The analysis unit can also analyze the crop growth status using image analysis technology and detect growth abnormalities. Furthermore, the analysis unit can analyze pest outbreak data and identify pest outbreak patterns. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. It uses image analysis technology to analyze crop growth and detect growth abnormalities. It analyzes pest outbreak data and identifies pest outbreak patterns. The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. Control includes, but is not limited to, controlling the operation of the harvesting robot and the flight of the drone. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. The control unit can also control the flight of the drone to monitor the entire farmland and collect data. Furthermore, the control unit can control the coordinated operation of the harvesting robot and drone to achieve efficient work. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. It controls the flight of the drone to monitor the entire farmland and collect data. It controls the coordinated operation of the harvesting robot and drone to achieve efficient work.As a result, the agricultural work efficiency system according to this embodiment improves the efficiency of agricultural work by monitoring and analyzing the conditions of the farmland and controlling the operation of harvesting robots and drones.

[0030] The monitoring unit monitors the conditions of the farmland. These conditions include, but are not limited to, soil conditions, crop growth, and pest outbreaks. For example, the unit can monitor soil moisture using soil sensors. These sensors are embedded in the soil and can collect data such as soil moisture, temperature, and pH in real time. This allows for constant monitoring of the soil conditions and determination of appropriate irrigation and fertilization timing. The monitoring unit can also monitor crop growth using cameras. These cameras are installed to cover the entire farmland and periodically photograph crop growth. This allows for monitoring of crop growth rate and health, enabling early intervention if abnormalities occur. Furthermore, the monitoring unit can monitor pest outbreaks using pest sensors. These sensors detect the pheromones and movements of specific pests, allowing for real-time monitoring of pest outbreaks. For example, the monitoring unit can monitor soil moisture in real time using soil sensors and collect data. It can also periodically photograph crop growth using cameras and collect image data. The system uses pest sensors to detect pest outbreaks and collect data. This allows the monitoring unit to comprehensively monitor the condition of the farmland and collect necessary data in real time. Furthermore, the monitoring unit transmits the collected data to a cloud server, making it accessible to the analysis and control units. This enables the monitoring unit to efficiently monitor the condition of the farmland and contribute to the efficiency of agricultural work.

[0031] The analysis unit analyzes data collected by the monitoring unit. Analysis may utilize, but is not limited to, machine learning algorithms and statistical analysis methods. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. The machine learning algorithm learns the relationship between soil moisture and crop growth based on past data and predicts future irrigation timing. The analysis unit can also use image analysis technology to analyze crop growth and detect growth abnormalities. Image analysis technology analyzes the color, shape, and growth rate of crop leaves to detect abnormalities such as diseases and nutrient deficiencies early. Furthermore, the analysis unit can analyze pest outbreak data and identify pest outbreak patterns. Identifying pest outbreak patterns allows for the implementation of effective control measures. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. It uses image analysis technology to analyze crop growth and detect growth abnormalities. It analyzes pest outbreak data and identifies pest outbreak patterns. This allows the analysis unit to quickly and accurately analyze collected data, contributing to increased efficiency in agricultural work. Furthermore, the analysis unit can utilize historical data and statistical information to formulate long-term agricultural work plans. For example, it can predict optimal sowing and harvesting times based on historical weather data and crop growth data, enabling the creation of agricultural work plans. Thus, the analysis unit can contribute not only to real-time data analysis but also to the formulation of long-term agricultural work plans.

[0032] The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. This control includes, but is not limited to, controlling the operation of the harvesting robot and the flight of the drone. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. Based on the data provided by the analysis unit, the harvesting robot can understand the location and growth status of the crops and perform harvesting work efficiently. The control unit can also control the flight of the drone to monitor the entire farmland and collect data. The drone flies over the farmland and monitors the farmland conditions in real time using cameras and sensors. This allows for an understanding of the overall farmland conditions and the collection of necessary data. Furthermore, the control unit can control the coordinated operation of the harvesting robot and drone to achieve efficient work. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. It controls the flight of the drone to monitor the entire farmland and collect data. It controls the coordinated operation of the harvesting robot and drone to achieve efficient work. In this way, the control unit can efficiently control the operation of the harvesting robot and drone, contributing to the efficiency of agricultural work. Furthermore, the control unit can monitor the operation of the harvesting robots and drones in real time and correct their movements as needed. For example, if a harvesting robot comes into contact with an obstacle or if the drone's battery runs low, the control unit can respond immediately to maintain safe and efficient work. This allows the control unit to optimize the operation of the harvesting robots and drones, improving the efficiency and safety of agricultural work.

[0033] The harvesting control unit controls the operation of the harvesting robot. For example, the harvesting control unit optimizes the movement path of the harvesting robot. For example, the harvesting control unit optimizes the movement path of the harvesting robot to achieve efficient harvesting. The harvesting control unit can also optimize the harvesting order of the harvesting robot. For example, the harvesting control unit optimizes the harvesting order of the harvesting robot to improve the efficiency of the harvesting operation. Furthermore, the harvesting control unit can also optimize the operation pattern of the harvesting robot. For example, the harvesting control unit optimizes the operation pattern of the harvesting robot to improve the accuracy of the harvesting operation. In this way, the harvesting control unit improves the efficiency of the harvesting operation by controlling the operation of the harvesting robot. Some or all of the above processing in the harvesting control unit may be performed using AI, for example, or without using AI. For example, the harvesting control unit can input operation data of the harvesting robot into a generating AI and have the generating AI execute operation control of the harvesting robot.

[0034] The drone control unit controls the drone's movements. For example, the drone control unit optimizes the drone's flight pattern. For example, the drone control unit optimizes the drone's flight pattern to achieve efficient monitoring and data collection. The drone control unit can also optimize the drone's data collection method. For example, the drone control unit optimizes the drone's data collection method to improve the accuracy of the collected data. Furthermore, the drone control unit can optimize the drone's monitoring range. For example, the drone control unit optimizes the drone's monitoring range to achieve efficient monitoring. As a result, the drone control unit makes monitoring and data collection of the entire farmland more efficient by controlling the drone's movements. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input drone flight data into a generating AI and have the generating AI perform drone flight control.

[0035] The adjustment unit adjusts the timing of harvest according to weather or seasonal fluctuations. The adjustment unit optimizes the timing of harvest using, for example, weather forecast data. For example, the adjustment unit optimizes the timing of harvest using weather forecast data to improve the quality of crops. The adjustment unit can also adjust the timing of harvest considering seasonal crop growth patterns. For example, the adjustment unit adjusts the timing of harvest considering seasonal crop growth patterns to maximize the yield. Furthermore, the adjustment unit can also adjust the timing of harvest according to changes in temperature and precipitation. For example, the adjustment unit adjusts the timing of harvest according to changes in temperature and precipitation to maintain the quality of crops. In this way, the adjustment unit improves the quality of crops and maximizes the yield by adjusting the timing of harvest according to weather and seasonal fluctuations. Some or all of the above processes in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input weather forecast data into a generating AI and have the generating AI perform the adjustment of the timing of harvest.

[0036] The monitoring unit monitors the operation of the harvesting robot and drone in real time. For example, the monitoring unit monitors the operation of the harvesting robot in real time to understand the progress of the work. For example, the monitoring unit monitors the operation of the harvesting robot in real time to understand the progress of the work. The monitoring unit can also monitor the flight of the drone in real time to understand the progress of monitoring the entire farmland and data collection. For example, the monitoring unit monitors the flight of the drone in real time to understand the progress of monitoring the entire farmland and data collection. Furthermore, the monitoring unit can monitor the coordinated operation of the harvesting robot and drone in real time to achieve efficient work. For example, the monitoring unit monitors the coordinated operation of the harvesting robot and drone in real time to achieve efficient work. As a result, by monitoring the operation of the harvesting robot and drone in real time, the accuracy of the work is improved and unnecessary movements are reduced. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input operation data of the harvesting robot and drone into a generating AI and have the generating AI perform real-time monitoring.

[0037] The monitoring unit collects detailed data by focusing on specific areas of farmland. For example, the monitoring unit can collect detailed data by focusing on areas with low moisture content in the farmland. The monitoring unit can also collect detailed data by focusing on areas where pest or disease outbreaks are suspected. Furthermore, the monitoring unit can collect detailed data by focusing on areas nearing harvest time. This allows the monitoring unit to detect and address problems early by collecting detailed data by focusing on specific areas of farmland. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data from a specific area into a generating AI and have the generating AI perform detailed data collection.

[0038] The monitoring unit immediately reports abnormal farmland conditions using an anomaly detection algorithm. For example, if the monitoring unit detects an abnormal temperature change, it will immediately report it. The monitoring unit can also immediately report if it detects an abnormal fluctuation in moisture content. Furthermore, the monitoring unit can immediately report if it detects an abnormal crop growth pattern. This allows the monitoring unit to respond quickly by immediately reporting abnormal farmland conditions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the anomaly detection algorithm into a generating AI and have the generating AI execute the reporting of abnormal farmland conditions.

[0039] The monitoring unit dynamically changes its monitoring range based on the geographical characteristics of the farmland. For example, the monitoring unit may change its monitoring range considering the elevation differences of the farmland. The monitoring unit may also change its monitoring range considering the location of water sources in the farmland. Furthermore, the monitoring unit may also change its monitoring range considering the density of vegetation in the farmland. This enables efficient monitoring by allowing the monitoring unit to dynamically change its monitoring range based on the geographical characteristics of the farmland. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical characteristic data of the farmland into a generating AI and have the generating AI perform the dynamic change of the monitoring range.

[0040] The monitoring unit predicts anomalies by referring to historical data of farmland. For example, the monitoring unit predicts abnormal yields by referring to past harvest data. The monitoring unit can also predict abnormal weather patterns by referring to past weather data. Furthermore, the monitoring unit can predict abnormal pest and disease outbreaks by referring to past pest and disease outbreak data. This allows the monitoring unit to take preventative measures by predicting anomalies by referring to historical data of farmland. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input historical data of farmland into a generating AI and have the generating AI perform anomaly predictions.

[0041] The analysis unit performs data cleaning to improve the accuracy of the collected data. For example, the analysis unit detects and removes outliers. The analysis unit can also impute missing data. Furthermore, the analysis unit can verify and correct data consistency. As a result, the accuracy of the analysis results is improved by the data cleaning performed by the analysis unit. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform data cleaning.

[0042] The analysis unit performs multifaceted analysis by combining different analysis algorithms. For example, the analysis unit can combine machine learning algorithms with statistical analysis. Furthermore, the analysis unit can combine image analysis and text analysis. Additionally, the analysis unit can combine time series analysis and spatial analysis. This allows the analysis unit to perform more multifaceted analysis by combining different analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms into a generating AI and have the generating AI perform multifaceted analysis.

[0043] The analysis unit divides the analysis results by region, taking into account the geographical information of the farmland. The analysis unit can also divide the analysis results by considering the topography of the farmland. Furthermore, the analysis unit can also divide the analysis results by considering the climatic conditions of the farmland. In this way, by dividing the analysis results by region, taking into account the geographical information of the farmland, the analysis unit can perform analysis tailored to the characteristics of each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical information of the farmland into a generating AI and have the generating AI perform the regional division of the analysis results.

[0044] The analysis unit grasps trends by comparing them with past analysis results. For example, the analysis unit grasps harvest yield trends by comparing them with past harvest yield data. The analysis unit can also grasp weather pattern trends by comparing them with past weather data. Furthermore, the analysis unit can also grasp pest and disease outbreak trends by comparing them with past pest and disease outbreak data. In this way, the analysis unit can grasp trends by comparing them with past analysis results and make future predictions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform trend identification.

[0045] The control unit performs simulations to optimize the movements of the harvesting robot and the drone. For example, the control unit simulates the movement patterns of the harvesting robot. The control unit can also simulate the flight paths of the drone. Furthermore, the control unit can simulate the coordinated movements of the harvesting robot and the drone. By performing simulations to optimize the movements of the harvesting robot and the drone, the control unit enables efficient operation. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input movement data of the harvesting robot and the drone into a generating AI and have the generating AI perform a simulation to optimize the movements.

[0046] The control unit incorporates emergency response procedures in case of an anomaly. For example, the control unit incorporates emergency response procedures in case of a malfunction of a harvesting robot. The control unit can also incorporate emergency response procedures in case of a drone crash. Furthermore, the control unit can incorporate emergency response procedures in case of extreme weather. This allows the control unit to respond quickly by incorporating emergency response procedures in case of an anomaly. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data on the occurrence of an anomaly into a generating AI and have the generating AI execute the emergency response procedures.

[0047] The control unit modifies its operation pattern considering the geographical conditions of the farmland. For example, the control unit modifies its operation pattern considering the elevation differences of the farmland. The control unit can also modify its operation pattern considering the location of the water source in the farmland. Furthermore, the control unit can also modify its operation pattern considering the vegetation density in the farmland. In this way, the control unit can operate efficiently by modifying its operation pattern considering the geographical conditions of the farmland. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input geographical condition data of the farmland into a generating AI and have the generating AI perform the modification of the operation pattern.

[0048] The control unit adjusts its operation considering coordination with other agricultural machinery. For example, the control unit adjusts the coordinated operation of a harvesting robot and a tractor. The control unit can also adjust the coordinated operation of a drone and an irrigation system. Furthermore, the control unit can adjust the coordinated operation of a harvesting robot and a fertilizer spreader. In this way, by adjusting its operation considering coordination with other agricultural machinery, the control unit enables efficient farm work. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input coordination data with other agricultural machinery into a generating AI and have the generating AI perform the adjustment of the operation.

[0049] The harvest control unit evaluates the ripeness of fruits and vegetables in real time and performs harvesting. For example, the harvest control unit can evaluate the color of fruits to determine their ripeness. The harvest control unit can also evaluate the size of vegetables to determine their ripeness. Furthermore, the harvest control unit can evaluate the hardness of fruits and vegetables to determine their ripeness. This allows the harvest control unit to evaluate the ripeness of fruits and vegetables in real time and perform harvesting at the optimal time. Some or all of the above processes in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input fruit and vegetable ripeness data into a generating AI and have the generating AI perform real-time evaluation and harvesting.

[0050] The harvesting control unit performs path planning to optimize the operation of the harvesting robot. For example, the harvesting control unit optimizes the movement path of the harvesting robot. The harvesting control unit can also optimize the harvesting order of the harvesting robot. Furthermore, the harvesting control unit can optimize the operation pattern of the harvesting robot. By performing path planning to optimize the operation of the harvesting robot, the harvesting control unit enables efficient harvesting. Some or all of the above processing in the harvesting control unit may be performed using AI, for example, or without AI. For example, the harvesting control unit can input the operation data of the harvesting robot into a generating AI and have the generating AI perform path planning.

[0051] The harvest control unit modifies the harvesting pattern considering the geographical conditions of the farmland. For example, the harvest control unit modifies the harvesting pattern considering the elevation differences of the farmland. The harvest control unit can also modify the harvesting pattern considering the location of the water source in the farmland. For example, the harvest control unit modifies the harvesting pattern considering the location of the water source in the farmland. Furthermore, the harvest control unit can also modify the harvesting pattern considering the vegetation density in the farmland. For example, the harvest control unit modifies the harvesting pattern considering the vegetation density in the farmland. As a result, by modifying the harvesting pattern considering the geographical conditions of the farmland, the harvest control unit enables efficient harvesting. Some or all of the above processing in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input geographical condition data of the farmland into a generating AI and have the generating AI perform the modification of the harvesting pattern.

[0052] The harvest control unit evaluates and sorts the harvested produce based on its quality. For example, the harvest control unit can evaluate the color of fruits to determine their quality. The harvest control unit can also evaluate the size of vegetables to determine their quality. Furthermore, the harvest control unit can evaluate the hardness of fruits and vegetables to determine their quality. In this way, the harvest control unit can select high-quality produce by evaluating and sorting the harvested produce. Some or all of the above processes in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input harvested produce quality data into a generating AI and have the generating AI perform quality evaluation and sorting.

[0053] The drone control unit collects detailed topographic data of farmland and uses it for analysis. For example, the drone control unit can collect detailed elevation differences of farmland while the drone is in flight. The drone control unit can also collect detailed locations of water sources in farmland while the drone is in flight. Furthermore, the drone control unit can also collect detailed vegetation density of farmland while the drone is in flight. This allows the drone control unit to collect detailed topographic data of farmland and use it for analysis, enabling more accurate analysis. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input the topographic data of farmland into a generating AI and use it for analysis.

[0054] The drone control unit immediately reports abnormal farmland conditions using an anomaly detection algorithm. For example, if the drone detects an abnormal temperature change while the drone is in flight, it will immediately report it. The drone control unit can also immediately report if it detects an abnormal fluctuation in moisture content while the drone is in flight. Furthermore, the drone control unit can immediately report if it detects an abnormal crop growth pattern while the drone is in flight. This allows the drone control unit to respond quickly by immediately reporting abnormal farmland conditions. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input the anomaly detection algorithm into a generating AI and have the generating AI perform the reporting of abnormal farmland conditions.

[0055] The drone control unit dynamically changes the flight range considering the geographical features of the farmland. For example, the drone control unit can change the flight range considering the elevation differences of the farmland. The drone control unit can also change the flight range considering the location of water sources in the farmland. For example, the drone control unit can change the flight range considering the location of water sources in the farmland. Furthermore, the drone control unit can also change the flight range considering the density of vegetation in the farmland. For example, the drone control unit can change the flight range considering the density of vegetation in the farmland. This enables efficient flight by dynamically changing the flight range considering the geographical features of the farmland. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input geographical feature data of the farmland into a generating AI and have the generating AI perform the dynamic change of the flight range.

[0056] The drone control unit predicts anomalies by referring to historical data of farmland. For example, the drone control unit predicts abnormal yields by referring to past harvest data. The drone control unit can also predict abnormal weather patterns by referring to past weather data. Furthermore, the drone control unit can predict abnormal pest and disease outbreaks by referring to past pest and disease outbreak data. This allows the drone control unit to take preventative measures by predicting anomalies by referring to historical data of farmland. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input historical data of farmland into a generating AI and have the generating AI perform anomaly predictions.

[0057] The adjustment unit optimizes the harvest plan using weather forecast data. For example, the adjustment unit optimizes the timing of the harvest based on the weather forecast data. The adjustment unit can also optimize the order of the harvest based on the weather forecast data. Furthermore, the adjustment unit can also optimize the harvest method based on the weather forecast data. In this way, the adjustment unit enables efficient harvesting by optimizing the harvest plan using weather forecast data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input weather forecast data into a generating AI and have the generating AI perform the optimization of the harvest plan.

[0058] The adjustment unit evaluates crop growth data in real time to determine the harvest timing. The adjustment unit can, for example, evaluate crop growth data in real time to determine the harvest timing. The adjustment unit can also evaluate crop growth data in real time to determine the harvest order. The adjustment unit can also evaluate crop growth data in real time to determine the harvest order. Furthermore, the adjustment unit can evaluate crop growth data in real time to determine the harvest method. By doing so, the adjustment unit can evaluate crop growth data in real time to determine the harvest timing, enabling harvesting at the optimal time. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input crop growth data into a generating AI and have the generating AI determine the harvest timing.

[0059] The adjustment unit modifies the harvest plan considering the geographical conditions of the farmland. For example, the adjustment unit modifies the harvest plan considering the elevation differences of the farmland. The adjustment unit can also modify the harvest plan considering the location of the water source in the farmland. For example, the adjustment unit modifies the harvest plan considering the location of the water source in the farmland. Furthermore, the adjustment unit can also modify the harvest plan considering the density of the vegetation in the farmland. For example, the adjustment unit modifies the harvest plan considering the density of the vegetation in the farmland. In this way, by modifying the harvest plan considering the geographical conditions of the farmland, the adjustment unit enables efficient harvesting. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input geographical condition data of the farmland into a generating AI and have the generating AI execute the modification of the harvest plan.

[0060] The adjustment unit adjusts the harvest timing considering coordination with other agricultural machinery. For example, the adjustment unit adjusts the coordinated operation of a harvesting robot and a tractor. The adjustment unit can also adjust the coordinated operation of a drone and an irrigation system. Furthermore, the adjustment unit can adjust the coordinated operation of a harvesting robot and a fertilizer spreader. In this way, the adjustment unit enables efficient harvesting by adjusting the harvest timing considering coordination with other agricultural machinery. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input coordination data with other agricultural machinery into a generating AI and have the generating AI perform the adjustment of the harvest timing.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The agricultural efficiency system can also be equipped with a prediction unit. This unit predicts future farmland conditions based on collected data. For example, it can predict future precipitation and temperature fluctuations based on weather data. It can also predict harvest times based on crop growth data. Furthermore, it can predict pest outbreak risks based on pest outbreak data. This allows the prediction unit to anticipate future farmland conditions and take preventative measures in advance.

[0063] The agricultural work efficiency system can also be equipped with a notification unit. This unit notifies the user of collected data and analysis results. For example, the notification unit can notify the user of the operating status of harvesting robots. It can also notify the user of drone flight status. Furthermore, the notification unit can notify the user of weather changes and pest outbreaks. This allows the notification unit to provide users with real-time information, enabling quick responses.

[0064] The agricultural work efficiency system can also be equipped with a learning unit. The learning unit learns to optimize the system's operation based on collected data. For example, the learning unit can learn the optimal operation pattern based on the operation data of a harvesting robot. It can also learn the optimal flight route based on drone flight data. Furthermore, the learning unit can learn the optimal harvesting timing based on weather data and crop growth data. This allows the learning unit to continuously optimize the system's operation.

[0065] The agricultural work efficiency system can also be equipped with a diagnostic unit. The diagnostic unit diagnoses the health of the farmland based on collected data. For example, the diagnostic unit can diagnose soil health based on soil moisture data. It can also diagnose crop health based on crop growth data. Furthermore, the diagnostic unit can diagnose the risk of pest damage to the farmland based on pest occurrence data. This allows the diagnostic unit to take appropriate measures by diagnosing the health of the farmland.

[0066] The agricultural work efficiency system can also be equipped with a reporting unit. This unit creates reports based on collected data and analysis results. For example, the reporting unit can compile reports on the operation status of harvesting robots. It can also compile reports on drone flight status. Furthermore, the reporting unit can compile reports on weather changes and pest outbreaks. This allows the reporting unit to provide users with detailed information, which can then be used to improve agricultural work.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The monitoring unit monitors the conditions of the farmland. These conditions include soil conditions, crop growth, and pest infestations. The monitoring unit uses soil sensors to monitor soil moisture in real time and collects data. It also uses cameras to periodically photograph crop growth and collects image data. Pest sensors are used to detect pest infestations and collect data. Step 2: The analysis unit analyzes the data collected by the monitoring unit. Machine learning algorithms and statistical analysis methods are used for the analysis. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. It uses image analysis technology to analyze crop growth status and detect growth abnormalities. It analyzes pest outbreak data and identifies pest outbreak patterns. Step 3: The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. This control includes controlling the operation of the harvesting robot and the flight of the drone. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. It controls the flight of the drone to monitor the entire farmland and collect data. It controls the coordinated operation of the harvesting robot and drone to achieve efficient work.

[0069] (Example of form 2) An embodiment of the present invention provides an agricultural work efficiency system that integrates multiple harvesting robots and drones to perform agricultural work efficiently. In this agricultural work efficiency system, each robot has an individual role, and an AI agent automates the optimal work planning and execution. For example, the agricultural work efficiency system uses an AI agent to monitor the condition of the farmland in real time and control the movements of the harvesting robots and drones. For example, the harvesting robots harvest fruits and vegetables, while the drones monitor the entire farmland and collect data. This allows each robot to perform its work efficiently. Next, the agricultural work efficiency system analyzes the data collected by the AI ​​agent and creates an optimal work plan. For example, the timing of harvesting can be adjusted according to weather and seasonal fluctuations. This improves the efficiency of agricultural work and maximizes the yield. Furthermore, the agricultural work efficiency system uses an AI agent to control the movements of the harvesting robots and drones in real time and monitor the progress of the work. For example, when a harvesting robot is harvesting fruit, the drone can monitor the situation and issue instructions as needed. This improves the accuracy of the work and reduces unnecessary movements. This mechanism can solve problems faced by farmers and farmland managers, such as labor shortages and the difficulty of creating efficient work plans. Furthermore, by utilizing generative AI, AI agent technology with personality, memory, planning, and behavioral capabilities can be realized, further advancing the automation and efficiency of agricultural work. For example, when an AI agent controls the operation of a harvesting robot to harvest fruit, a drone can monitor the entire farmland and grasp the progress of harvesting in real time. This allows for efficient harvesting and maximizes yields. In addition, by adjusting the timing of harvesting according to weather and seasonal changes, the quality of crops can be improved and yields can be maximized. For example, harvesting before bad weather occurs can maintain the quality of crops. Thus, a system in which an AI agent integrates harvesting robots and drones to perform agricultural work efficiently is extremely useful for farmers and farm managers, enabling increased efficiency and automation in agriculture.

[0070] The agricultural work efficiency system according to this embodiment comprises a monitoring unit, an analysis unit, and a control unit. The monitoring unit monitors the conditions of the farmland. The conditions of the farmland include, but are not limited to, the soil condition, crop growth status, and pest occurrence status. The monitoring unit monitors the soil moisture content using, for example, a soil sensor. The monitoring unit can also monitor the crop growth status using a camera. Furthermore, the monitoring unit can monitor the pest occurrence status using a pest sensor. For example, the monitoring unit monitors the soil moisture content in real time using a soil sensor and collects data. It periodically photographs the crop growth status using a camera and collects image data. It detects the occurrence status of pests using a pest sensor and collects data. The analysis unit analyzes the data collected by the monitoring unit. For example, machine learning algorithms and statistical analysis methods are used for the analysis, but are not limited to these. For example, the analysis unit analyzes soil moisture content data using a machine learning algorithm and estimates the optimal irrigation timing. The analysis unit can also analyze the crop growth status using image analysis technology and detect growth abnormalities. Furthermore, the analysis unit can analyze pest outbreak data and identify pest outbreak patterns. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. It uses image analysis technology to analyze crop growth and detect growth abnormalities. It analyzes pest outbreak data and identifies pest outbreak patterns. The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. Control includes, but is not limited to, controlling the operation of the harvesting robot and the flight of the drone. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. The control unit can also control the flight of the drone to monitor the entire farmland and collect data. Furthermore, the control unit can control the coordinated operation of the harvesting robot and drone to achieve efficient work. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. It controls the flight of the drone to monitor the entire farmland and collect data. It controls the coordinated operation of the harvesting robot and drone to achieve efficient work.As a result, the agricultural work efficiency system according to this embodiment improves the efficiency of agricultural work by monitoring and analyzing the conditions of the farmland and controlling the operation of harvesting robots and drones.

[0071] The monitoring unit monitors the conditions of the farmland. These conditions include, but are not limited to, soil conditions, crop growth, and pest outbreaks. For example, the unit can monitor soil moisture using soil sensors. These sensors are embedded in the soil and can collect data such as soil moisture, temperature, and pH in real time. This allows for constant monitoring of the soil conditions and determination of appropriate irrigation and fertilization timing. The monitoring unit can also monitor crop growth using cameras. These cameras are installed to cover the entire farmland and periodically photograph crop growth. This allows for monitoring of crop growth rate and health, enabling early intervention if abnormalities occur. Furthermore, the monitoring unit can monitor pest outbreaks using pest sensors. These sensors detect the pheromones and movements of specific pests, allowing for real-time monitoring of pest outbreaks. For example, the monitoring unit can monitor soil moisture in real time using soil sensors and collect data. It can also periodically photograph crop growth using cameras and collect image data. The system uses pest sensors to detect pest outbreaks and collect data. This allows the monitoring unit to comprehensively monitor the condition of the farmland and collect necessary data in real time. Furthermore, the monitoring unit transmits the collected data to a cloud server, making it accessible to the analysis and control units. This enables the monitoring unit to efficiently monitor the condition of the farmland and contribute to the efficiency of agricultural work.

[0072] The analysis unit analyzes data collected by the monitoring unit. Analysis may utilize, but is not limited to, machine learning algorithms and statistical analysis methods. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. The machine learning algorithm learns the relationship between soil moisture and crop growth based on past data and predicts future irrigation timing. The analysis unit can also use image analysis technology to analyze crop growth and detect growth abnormalities. Image analysis technology analyzes the color, shape, and growth rate of crop leaves to detect abnormalities such as diseases and nutrient deficiencies early. Furthermore, the analysis unit can analyze pest outbreak data and identify pest outbreak patterns. Identifying pest outbreak patterns allows for the implementation of effective control measures. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. It uses image analysis technology to analyze crop growth and detect growth abnormalities. It analyzes pest outbreak data and identifies pest outbreak patterns. This allows the analysis unit to quickly and accurately analyze collected data, contributing to increased efficiency in agricultural work. Furthermore, the analysis unit can utilize historical data and statistical information to formulate long-term agricultural work plans. For example, it can predict optimal sowing and harvesting times based on historical weather data and crop growth data, enabling the creation of agricultural work plans. Thus, the analysis unit can contribute not only to real-time data analysis but also to the formulation of long-term agricultural work plans.

[0073] The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. This control includes, but is not limited to, controlling the operation of the harvesting robot and the flight of the drone. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. Based on the data provided by the analysis unit, the harvesting robot can understand the location and growth status of the crops and perform harvesting work efficiently. The control unit can also control the flight of the drone to monitor the entire farmland and collect data. The drone flies over the farmland and monitors the farmland conditions in real time using cameras and sensors. This allows for an understanding of the overall farmland conditions and the collection of necessary data. Furthermore, the control unit can control the coordinated operation of the harvesting robot and drone to achieve efficient work. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. It controls the flight of the drone to monitor the entire farmland and collect data. It controls the coordinated operation of the harvesting robot and drone to achieve efficient work. In this way, the control unit can efficiently control the operation of the harvesting robot and drone, contributing to the efficiency of agricultural work. Furthermore, the control unit can monitor the operation of the harvesting robots and drones in real time and correct their movements as needed. For example, if a harvesting robot comes into contact with an obstacle or if the drone's battery runs low, the control unit can respond immediately to maintain safe and efficient work. This allows the control unit to optimize the operation of the harvesting robots and drones, improving the efficiency and safety of agricultural work.

[0074] The harvesting control unit controls the operation of the harvesting robot. For example, the harvesting control unit optimizes the movement path of the harvesting robot. For example, the harvesting control unit optimizes the movement path of the harvesting robot to achieve efficient harvesting. The harvesting control unit can also optimize the harvesting order of the harvesting robot. For example, the harvesting control unit optimizes the harvesting order of the harvesting robot to improve the efficiency of the harvesting operation. Furthermore, the harvesting control unit can also optimize the operation pattern of the harvesting robot. For example, the harvesting control unit optimizes the operation pattern of the harvesting robot to improve the accuracy of the harvesting operation. In this way, the harvesting control unit improves the efficiency of the harvesting operation by controlling the operation of the harvesting robot. Some or all of the above processing in the harvesting control unit may be performed using AI, for example, or without using AI. For example, the harvesting control unit can input operation data of the harvesting robot into a generating AI and have the generating AI execute operation control of the harvesting robot.

[0075] The drone control unit controls the drone's movements. For example, the drone control unit optimizes the drone's flight pattern. For example, the drone control unit optimizes the drone's flight pattern to achieve efficient monitoring and data collection. The drone control unit can also optimize the drone's data collection method. For example, the drone control unit optimizes the drone's data collection method to improve the accuracy of the collected data. Furthermore, the drone control unit can optimize the drone's monitoring range. For example, the drone control unit optimizes the drone's monitoring range to achieve efficient monitoring. As a result, the drone control unit makes monitoring and data collection of the entire farmland more efficient by controlling the drone's movements. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input drone flight data into a generating AI and have the generating AI perform drone flight control.

[0076] The adjustment unit adjusts the timing of harvest according to weather or seasonal fluctuations. The adjustment unit optimizes the timing of harvest using, for example, weather forecast data. For example, the adjustment unit optimizes the timing of harvest using weather forecast data to improve the quality of crops. The adjustment unit can also adjust the timing of harvest considering seasonal crop growth patterns. For example, the adjustment unit adjusts the timing of harvest considering seasonal crop growth patterns to maximize the yield. Furthermore, the adjustment unit can also adjust the timing of harvest according to changes in temperature and precipitation. For example, the adjustment unit adjusts the timing of harvest according to changes in temperature and precipitation to maintain the quality of crops. In this way, the adjustment unit improves the quality of crops and maximizes the yield by adjusting the timing of harvest according to weather and seasonal fluctuations. Some or all of the above processes in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input weather forecast data into a generating AI and have the generating AI perform the adjustment of the timing of harvest.

[0077] The monitoring unit monitors the operation of the harvesting robot and drone in real time. For example, the monitoring unit monitors the operation of the harvesting robot in real time to understand the progress of the work. For example, the monitoring unit monitors the operation of the harvesting robot in real time to understand the progress of the work. The monitoring unit can also monitor the flight of the drone in real time to understand the progress of monitoring the entire farmland and data collection. For example, the monitoring unit monitors the flight of the drone in real time to understand the progress of monitoring the entire farmland and data collection. Furthermore, the monitoring unit can monitor the coordinated operation of the harvesting robot and drone in real time to achieve efficient work. For example, the monitoring unit monitors the coordinated operation of the harvesting robot and drone in real time to achieve efficient work. As a result, by monitoring the operation of the harvesting robot and drone in real time, the accuracy of the work is improved and unnecessary movements are reduced. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input operation data of the harvesting robot and drone into a generating AI and have the generating AI perform real-time monitoring.

[0078] The monitoring unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit increases the monitoring frequency to provide a sense of security. The monitoring unit can also reduce the system load by decreasing the monitoring frequency when the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can prioritize increasing the monitoring frequency of important areas. In this way, the monitoring unit can reduce the system load while providing a sense of security to the user by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI adjust the monitoring frequency.

[0079] The monitoring unit collects detailed data by focusing on specific areas of farmland. For example, the monitoring unit can collect detailed data by focusing on areas with low moisture content in the farmland. The monitoring unit can also collect detailed data by focusing on areas where pest or disease outbreaks are suspected. Furthermore, the monitoring unit can collect detailed data by focusing on areas nearing harvest time. This allows the monitoring unit to detect and address problems early by collecting detailed data by focusing on specific areas of farmland. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data from a specific area into a generating AI and have the generating AI perform detailed data collection.

[0080] The monitoring unit immediately reports abnormal farmland conditions using an anomaly detection algorithm. For example, if the monitoring unit detects an abnormal temperature change, it will immediately report it. The monitoring unit can also immediately report if it detects an abnormal fluctuation in moisture content. Furthermore, the monitoring unit can immediately report if it detects an abnormal crop growth pattern. This allows the monitoring unit to respond quickly by immediately reporting abnormal farmland conditions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the anomaly detection algorithm into a generating AI and have the generating AI execute the reporting of abnormal farmland conditions.

[0081] The monitoring unit estimates the user's emotions and prioritizes monitoring data based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit will prioritize monitoring important data. The monitoring unit can also prioritize normal monitoring data if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring data of high urgency. In this way, the monitoring unit can prioritize monitoring important data by prioritizing monitoring data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI determine the priority of the monitoring data.

[0082] The monitoring unit dynamically changes its monitoring range based on the geographical characteristics of the farmland. For example, the monitoring unit may change its monitoring range considering the elevation differences of the farmland. The monitoring unit may also change its monitoring range considering the location of water sources in the farmland. Furthermore, the monitoring unit may also change its monitoring range considering the density of vegetation in the farmland. This enables efficient monitoring by allowing the monitoring unit to dynamically change its monitoring range based on the geographical characteristics of the farmland. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical characteristic data of the farmland into a generating AI and have the generating AI perform the dynamic change of the monitoring range.

[0083] The monitoring unit predicts anomalies by referring to historical data of farmland. For example, the monitoring unit predicts abnormal yields by referring to past harvest data. The monitoring unit can also predict abnormal weather patterns by referring to past weather data. Furthermore, the monitoring unit can predict abnormal pest and disease outbreaks by referring to past pest and disease outbreak data. This allows the monitoring unit to take preventative measures by predicting anomalies by referring to historical data of farmland. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input historical data of farmland into a generating AI and have the generating AI perform anomaly predictions.

[0084] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple display method. For example, if the user is feeling stressed, the analysis unit provides a simple display method. The analysis unit can also display detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit displays detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, if the user is in a hurry, the analysis unit provides a concise display method. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions, making it possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0085] The analysis unit performs data cleaning to improve the accuracy of the collected data. For example, the analysis unit detects and removes outliers. The analysis unit can also impute missing data. Furthermore, the analysis unit can verify and correct data consistency. As a result, the accuracy of the analysis results is improved by the data cleaning performed by the analysis unit. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform data cleaning.

[0086] The analysis unit performs multifaceted analysis by combining different analysis algorithms. For example, the analysis unit can combine machine learning algorithms with statistical analysis. Furthermore, the analysis unit can combine image analysis and text analysis. Additionally, the analysis unit can combine time series analysis and spatial analysis. This allows the analysis unit to perform more multifaceted analysis by combining different analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different analysis algorithms into a generating AI and have the generating AI perform multifaceted analysis.

[0087] The analysis unit estimates the user's emotions and determines the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize important analyses. For example, if the user is feeling anxious, the analysis unit will prioritize important analyses. The analysis unit can also prioritize normal analyses if the user is relaxed. For example, if the user is in a hurry, the analysis unit will prioritize normal analyses. Furthermore, if the user is in a hurry, the analysis unit will prioritize urgent analyses. For example, if the user is in a hurry, the analysis unit will prioritize urgent analyses. In this way, the analysis unit can prioritize important analyses by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI determine the priority of the analysis.

[0088] The analysis unit divides the analysis results by region, taking into account the geographical information of the farmland. The analysis unit can also divide the analysis results by considering the topography of the farmland. Furthermore, the analysis unit can also divide the analysis results by considering the climatic conditions of the farmland. In this way, by dividing the analysis results by region, taking into account the geographical information of the farmland, the analysis unit can perform analysis tailored to the characteristics of each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical information of the farmland into a generating AI and have the generating AI perform the regional division of the analysis results.

[0089] The analysis unit grasps trends by comparing them with past analysis results. For example, the analysis unit grasps harvest yield trends by comparing them with past harvest yield data. The analysis unit can also grasp weather pattern trends by comparing them with past weather data. Furthermore, the analysis unit can also grasp pest and disease outbreak trends by comparing them with past pest and disease outbreak data. In this way, the analysis unit can grasp trends by comparing them with past analysis results and make future predictions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform trend identification.

[0090] The control unit estimates the user's emotions and adjusts the timing of control based on the estimated emotions. For example, if the user is stressed, the control unit may speed up the timing of control. The control unit may also delay the timing of control if the user is relaxed. Furthermore, if the user is in a hurry, the control unit may optimize the timing of control. This allows the control unit to provide control that meets the user's needs by adjusting the timing of control according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit may input user emotion data into a generative AI and have the generative AI adjust the timing of control.

[0091] The control unit performs simulations to optimize the movements of the harvesting robot and the drone. For example, the control unit simulates the movement patterns of the harvesting robot. The control unit can also simulate the flight paths of the drone. Furthermore, the control unit can simulate the coordinated movements of the harvesting robot and the drone. By performing simulations to optimize the movements of the harvesting robot and the drone, the control unit enables efficient operation. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input movement data of the harvesting robot and the drone into a generating AI and have the generating AI perform a simulation to optimize the movements.

[0092] The control unit incorporates emergency response procedures in case of an anomaly. For example, the control unit incorporates emergency response procedures in case of a malfunction of a harvesting robot. The control unit can also incorporate emergency response procedures in case of a drone crash. Furthermore, the control unit can incorporate emergency response procedures in case of extreme weather. This allows the control unit to respond quickly by incorporating emergency response procedures in case of an anomaly. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input data on the occurrence of an anomaly into a generating AI and have the generating AI execute the emergency response procedures.

[0093] The control unit estimates the user's emotions and determines the priority of controls based on the estimated emotions. For example, if the user is feeling anxious, the control unit will prioritize important controls. The control unit can also prioritize normal controls if the user is relaxed. Furthermore, if the user is in a hurry, the control unit can prioritize urgent controls. In this way, the control unit can prioritize important controls by determining the priority of controls according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI or not using AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI determine the priority of controls.

[0094] The control unit modifies its operation pattern considering the geographical conditions of the farmland. For example, the control unit modifies its operation pattern considering the elevation differences of the farmland. The control unit can also modify its operation pattern considering the location of the water source in the farmland. Furthermore, the control unit can also modify its operation pattern considering the vegetation density in the farmland. In this way, the control unit can operate efficiently by modifying its operation pattern considering the geographical conditions of the farmland. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input geographical condition data of the farmland into a generating AI and have the generating AI perform the modification of the operation pattern.

[0095] The control unit adjusts its operation considering coordination with other agricultural machinery. For example, the control unit adjusts the coordinated operation of a harvesting robot and a tractor. The control unit can also adjust the coordinated operation of a drone and an irrigation system. Furthermore, the control unit can adjust the coordinated operation of a harvesting robot and a fertilizer spreader. In this way, by adjusting its operation considering coordination with other agricultural machinery, the control unit enables efficient farm work. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input coordination data with other agricultural machinery into a generating AI and have the generating AI perform the adjustment of the operation.

[0096] The harvest control unit estimates the user's emotions and adjusts the harvesting timing based on the estimated emotions. For example, if the user is stressed, the harvest control unit will accelerate the harvesting timing. The harvest control unit can also delay the harvesting timing if the user is relaxed. Furthermore, if the user is in a hurry, the harvest control unit can optimize the harvesting timing. This allows the harvest control unit to adjust the harvesting timing according to the user's emotions, enabling harvesting that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input user emotion data into the generating AI and have the generating AI adjust the timing of the harvest.

[0097] The harvest control unit evaluates the ripeness of fruits and vegetables in real time and performs harvesting. For example, the harvest control unit can evaluate the color of fruits to determine their ripeness. The harvest control unit can also evaluate the size of vegetables to determine their ripeness. Furthermore, the harvest control unit can evaluate the hardness of fruits and vegetables to determine their ripeness. This allows the harvest control unit to evaluate the ripeness of fruits and vegetables in real time and perform harvesting at the optimal time. Some or all of the above processes in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input fruit and vegetable ripeness data into a generating AI and have the generating AI perform real-time evaluation and harvesting.

[0098] The harvesting control unit performs path planning to optimize the operation of the harvesting robot. For example, the harvesting control unit optimizes the movement path of the harvesting robot. The harvesting control unit can also optimize the harvesting order of the harvesting robot. Furthermore, the harvesting control unit can optimize the operation pattern of the harvesting robot. By performing path planning to optimize the operation of the harvesting robot, the harvesting control unit enables efficient harvesting. Some or all of the above processing in the harvesting control unit may be performed using AI, for example, or without AI. For example, the harvesting control unit can input the operation data of the harvesting robot into a generating AI and have the generating AI perform path planning.

[0099] The harvest control unit estimates the user's emotions and determines harvest priorities based on the estimated emotions. For example, if the user is feeling anxious, the harvest control unit will prioritize harvesting important crops. The harvest control unit can also prioritize normal harvesting if the user is relaxed. Furthermore, if the user is in a hurry, the harvest control unit can prioritize harvesting high-priority crops. In this way, the harvest control unit can prioritize harvesting important crops by determining harvest priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input user emotion data into the generating AI and have the generating AI determine the harvesting priority.

[0100] The harvest control unit modifies the harvesting pattern considering the geographical conditions of the farmland. For example, the harvest control unit modifies the harvesting pattern considering the elevation differences of the farmland. The harvest control unit can also modify the harvesting pattern considering the location of the water source in the farmland. For example, the harvest control unit modifies the harvesting pattern considering the location of the water source in the farmland. Furthermore, the harvest control unit can also modify the harvesting pattern considering the vegetation density in the farmland. For example, the harvest control unit modifies the harvesting pattern considering the vegetation density in the farmland. As a result, by modifying the harvesting pattern considering the geographical conditions of the farmland, the harvest control unit enables efficient harvesting. Some or all of the above processing in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input geographical condition data of the farmland into a generating AI and have the generating AI perform the modification of the harvesting pattern.

[0101] The harvest control unit evaluates and sorts the harvested produce based on its quality. For example, the harvest control unit can evaluate the color of fruits to determine their quality. The harvest control unit can also evaluate the size of vegetables to determine their quality. Furthermore, the harvest control unit can evaluate the hardness of fruits and vegetables to determine their quality. In this way, the harvest control unit can select high-quality produce by evaluating and sorting the harvested produce. Some or all of the above processes in the harvest control unit may be performed using AI, for example, or without AI. For example, the harvest control unit can input harvested produce quality data into a generating AI and have the generating AI perform quality evaluation and sorting.

[0102] The drone control unit estimates the user's emotions and adjusts the drone's flight pattern based on the estimated emotions. For example, if the user is stressed, the drone control unit simplifies the drone's flight pattern. For example, if the user is stressed, the drone control unit simplifies the drone's flight pattern. The drone control unit can also complicate the drone's flight pattern if the user is relaxed. For example, if the user is in a hurry, the drone control unit optimizes the drone's flight pattern. For example, if the user is in a hurry, the drone control unit optimizes the drone's flight pattern. In this way, the drone control unit can adjust the drone's flight pattern according to the user's emotions, enabling flight that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input user emotion data into a generating AI and have the AI ​​adjust the flight pattern.

[0103] The drone control unit collects detailed topographic data of farmland and uses it for analysis. For example, the drone control unit can collect detailed elevation differences of farmland while the drone is in flight. The drone control unit can also collect detailed locations of water sources in farmland while the drone is in flight. Furthermore, the drone control unit can also collect detailed vegetation density of farmland while the drone is in flight. This allows the drone control unit to collect detailed topographic data of farmland and use it for analysis, enabling more accurate analysis. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input the topographic data of farmland into a generating AI and use it for analysis.

[0104] The drone control unit immediately reports abnormal farmland conditions using an anomaly detection algorithm. For example, if the drone detects an abnormal temperature change while the drone is in flight, it will immediately report it. The drone control unit can also immediately report if it detects an abnormal fluctuation in moisture content while the drone is in flight. Furthermore, the drone control unit can immediately report if it detects an abnormal crop growth pattern while the drone is in flight. This allows the drone control unit to respond quickly by immediately reporting abnormal farmland conditions. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input the anomaly detection algorithm into a generating AI and have the generating AI perform the reporting of abnormal farmland conditions.

[0105] The drone control unit estimates the user's emotions and determines the drone's flight priorities based on the estimated emotions. For example, if the user is feeling anxious, the drone control unit will prioritize flying over important areas. The drone control unit can also prioritize normal flight if the user is relaxed. Furthermore, if the user is in a hurry, the drone control unit can prioritize flying over high-urgency areas. In this way, the drone control unit can prioritize flying over important areas by determining the drone's flight priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input user emotion data into a generating AI and have the AI ​​determine flight priorities.

[0106] The drone control unit dynamically changes the flight range considering the geographical features of the farmland. For example, the drone control unit can change the flight range considering the elevation differences of the farmland. The drone control unit can also change the flight range considering the location of water sources in the farmland. For example, the drone control unit can change the flight range considering the location of water sources in the farmland. Furthermore, the drone control unit can also change the flight range considering the density of vegetation in the farmland. For example, the drone control unit can change the flight range considering the density of vegetation in the farmland. This enables efficient flight by dynamically changing the flight range considering the geographical features of the farmland. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input geographical feature data of the farmland into a generating AI and have the generating AI perform the dynamic change of the flight range.

[0107] The drone control unit predicts anomalies by referring to historical data of farmland. For example, the drone control unit predicts abnormal yields by referring to past harvest data. The drone control unit can also predict abnormal weather patterns by referring to past weather data. Furthermore, the drone control unit can predict abnormal pest and disease outbreaks by referring to past pest and disease outbreak data. This allows the drone control unit to take preventative measures by predicting anomalies by referring to historical data of farmland. Some or all of the above processing in the drone control unit may be performed using AI, for example, or without AI. For example, the drone control unit can input historical data of farmland into a generating AI and have the generating AI perform anomaly predictions.

[0108] The adjustment unit estimates the user's emotions and adjusts the harvesting timing based on the estimated emotions. For example, if the user is stressed, the adjustment unit may accelerate the harvesting timing. The adjustment unit may also delay the harvesting timing if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit may optimize the harvesting timing. This allows the adjustment unit to adjust the harvesting timing according to the user's emotions, enabling harvesting that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into the generating AI and have the generating AI adjust the timing of harvesting.

[0109] The adjustment unit optimizes the harvest plan using weather forecast data. For example, the adjustment unit optimizes the timing of the harvest based on the weather forecast data. The adjustment unit can also optimize the order of the harvest based on the weather forecast data. Furthermore, the adjustment unit can also optimize the harvest method based on the weather forecast data. In this way, the adjustment unit enables efficient harvesting by optimizing the harvest plan using weather forecast data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input weather forecast data into a generating AI and have the generating AI perform the optimization of the harvest plan.

[0110] The adjustment unit evaluates crop growth data in real time to determine the harvest timing. The adjustment unit can, for example, evaluate crop growth data in real time to determine the harvest timing. The adjustment unit can also evaluate crop growth data in real time to determine the harvest order. The adjustment unit can also evaluate crop growth data in real time to determine the harvest order. Furthermore, the adjustment unit can evaluate crop growth data in real time to determine the harvest method. By doing so, the adjustment unit can evaluate crop growth data in real time to determine the harvest timing, enabling harvesting at the optimal time. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input crop growth data into a generating AI and have the generating AI determine the harvest timing.

[0111] The adjustment unit estimates the user's emotions and determines harvest priorities based on the estimated emotions. For example, if the user is feeling anxious, the adjustment unit will prioritize harvesting important crops. The adjustment unit can also prioritize normal harvesting if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can prioritize harvesting high-priority crops. In this way, the adjustment unit can prioritize harvesting important crops by determining harvest priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into the generating AI and have the generating AI determine the harvesting priority.

[0112] The adjustment unit modifies the harvest plan considering the geographical conditions of the farmland. For example, the adjustment unit modifies the harvest plan considering the elevation differences of the farmland. The adjustment unit can also modify the harvest plan considering the location of the water source in the farmland. For example, the adjustment unit modifies the harvest plan considering the location of the water source in the farmland. Furthermore, the adjustment unit can also modify the harvest plan considering the density of the vegetation in the farmland. For example, the adjustment unit modifies the harvest plan considering the density of the vegetation in the farmland. In this way, by modifying the harvest plan considering the geographical conditions of the farmland, the adjustment unit enables efficient harvesting. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input geographical condition data of the farmland into a generating AI and have the generating AI execute the modification of the harvest plan.

[0113] The adjustment unit adjusts the harvest timing considering coordination with other agricultural machinery. For example, the adjustment unit adjusts the coordinated operation of a harvesting robot and a tractor. The adjustment unit can also adjust the coordinated operation of a drone and an irrigation system. Furthermore, the adjustment unit can adjust the coordinated operation of a harvesting robot and a fertilizer spreader. In this way, the adjustment unit enables efficient harvesting by adjusting the harvest timing considering coordination with other agricultural machinery. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input coordination data with other agricultural machinery into a generating AI and have the generating AI perform the adjustment of the harvest timing.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The agricultural efficiency system can also be equipped with a prediction unit. This unit predicts future farmland conditions based on collected data. For example, it can predict future precipitation and temperature fluctuations based on weather data. It can also predict harvest times based on crop growth data. Furthermore, it can predict pest outbreak risks based on pest outbreak data. This allows the prediction unit to anticipate future farmland conditions and take preventative measures in advance.

[0116] The agricultural work efficiency system can also be equipped with a notification unit. This unit notifies the user of collected data and analysis results. For example, the notification unit can notify the user of the operating status of harvesting robots. It can also notify the user of drone flight status. Furthermore, the notification unit can notify the user of weather changes and pest outbreaks. This allows the notification unit to provide users with real-time information, enabling quick responses.

[0117] The agricultural work efficiency system can also be equipped with a learning unit. The learning unit learns to optimize the system's operation based on collected data. For example, the learning unit can learn the optimal operation pattern based on the operation data of a harvesting robot. It can also learn the optimal flight route based on drone flight data. Furthermore, the learning unit can learn the optimal harvesting timing based on weather data and crop growth data. This allows the learning unit to continuously optimize the system's operation.

[0118] The agricultural work efficiency system can also be equipped with a diagnostic unit. The diagnostic unit diagnoses the health of the farmland based on collected data. For example, the diagnostic unit can diagnose soil health based on soil moisture data. It can also diagnose crop health based on crop growth data. Furthermore, the diagnostic unit can diagnose the risk of pest damage to the farmland based on pest occurrence data. This allows the diagnostic unit to take appropriate measures by diagnosing the health of the farmland.

[0119] The agricultural work efficiency system can also be equipped with a reporting unit. This unit creates reports based on collected data and analysis results. For example, the reporting unit can compile reports on the operation status of harvesting robots. It can also compile reports on drone flight status. Furthermore, the reporting unit can compile reports on weather changes and pest outbreaks. This allows the reporting unit to provide users with detailed information, which can then be used to improve agricultural work.

[0120] The agricultural work efficiency system can also be equipped with an emotion estimation unit. This unit estimates the user's emotions and adjusts the system's operation based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can simplify the system's operation. Conversely, if the user is relaxed, the emotion estimation unit can complicate the system's operation. Furthermore, if the user is in a hurry, the emotion estimation unit can optimize the system's operation. In this way, the emotion estimation unit can adjust the system's operation according to the user's emotions, enabling operation that meets the user's needs.

[0121] The agricultural work efficiency system can also be equipped with an emotional feedback unit. This unit estimates the user's emotions and provides feedback based on those emotions. For example, if the user is feeling stressed, the emotional feedback unit can offer advice on how to relax. If the user is relaxed, it can also provide positive feedback praising their work progress. Furthermore, if the user is in a hurry, the emotional feedback unit can suggest more efficient work methods. In this way, the emotional feedback unit can improve the user's work efficiency by providing appropriate feedback according to their emotions.

[0122] The agricultural work efficiency system can also be equipped with an emotion monitoring unit. This unit monitors the user's emotions in real time and adjusts the system's operation accordingly. For example, if the user is stressed, the emotion monitoring unit can simplify the system's operation. Conversely, if the user is relaxed, it can complicate the system's operation. Furthermore, if the user is in a hurry, the emotion monitoring unit can optimize the system's operation. In this way, the emotion monitoring unit adjusts the system's operation according to the user's emotions, enabling operation that meets the user's needs.

[0123] The agricultural work efficiency system can also be equipped with an emotion alert unit. The emotion alert unit estimates the user's emotions and issues alerts based on those estimates. For example, if the emotion alert unit is feeling stressed, it can issue an alert prompting the user to take a break. It can also issue an alert to check the progress of the work if the user is relaxed. Furthermore, if the user is in a hurry, it can issue an alert suggesting more efficient work methods. In this way, the emotion alert unit can improve the user's work efficiency by issuing appropriate alerts according to the user's emotions.

[0124] The agricultural work efficiency system can also be equipped with an emotional support unit. This unit estimates the user's emotions and provides support based on those emotions. For example, if the user is feeling stressed, the emotional support unit can provide relaxing music. If the user is relaxed, it can also provide support by praising their work progress. Furthermore, if the user is in a hurry, it can offer support by suggesting more efficient work methods. In this way, the emotional support unit can improve the user's work efficiency by providing appropriate support according to their emotions.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The monitoring unit monitors the conditions of the farmland. These conditions include soil conditions, crop growth, and pest infestations. The monitoring unit uses soil sensors to monitor soil moisture in real time and collects data. It also uses cameras to periodically photograph crop growth and collects image data. Pest sensors are used to detect pest infestations and collect data. Step 2: The analysis unit analyzes the data collected by the monitoring unit. Machine learning algorithms and statistical analysis methods are used for the analysis. For example, the analysis unit uses machine learning algorithms to analyze soil moisture data and estimate the optimal irrigation timing. It uses image analysis technology to analyze crop growth status and detect growth abnormalities. It analyzes pest outbreak data and identifies pest outbreak patterns. Step 3: The control unit controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. This control includes controlling the operation of the harvesting robot and the flight of the drone. For example, the control unit controls the operation of the harvesting robot to harvest crops at the optimal timing. It controls the flight of the drone to monitor the entire farmland and collect data. It controls the coordinated operation of the harvesting robot and drone to achieve efficient work.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] For example, each of the multiple elements, including the monitoring unit, analysis unit, and control unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the condition of the farmland using the camera 42 and soil sensors of the smart device 14, and collects data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to detect optimal irrigation timing and growth abnormalities. The control unit controls the operation of the harvesting robot and drone using the specific processing unit 290 of the data processing unit 12 to achieve efficient work. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, each of the multiple elements, including the monitoring unit, analysis unit, and control unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the condition of the farmland using the camera 42 and soil sensors of the smart glasses 214 and collects data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to detect optimal irrigation timing and growth abnormalities. The control unit controls the operation of the harvesting robot and drone using the identification processing unit 290 of the data processing unit 12 to achieve efficient work. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, each of the multiple elements, including the monitoring unit, analysis unit, and control unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the condition of the farmland using the camera 42 and soil sensors of the headset terminal 314 and collects data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to detect optimal irrigation timing and growth abnormalities. The control unit controls the operation of the harvesting robot and drone using the specific processing unit 290 of the data processing unit 12 to achieve efficient work. The correspondence between each unit and the devices and control unit is not limited to the example described above and can be modified in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] For example, each of the multiple elements, including the monitoring unit, analysis unit, and control unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the condition of the farmland using the camera 42 and soil sensors of the robot 414 and collects data using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to detect optimal irrigation timing and growth anomalies. The control unit controls the operation of the harvesting robot and drone using the specific processing unit 290 of the data processing unit 12 to achieve efficient work. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) The monitoring department, which monitors the condition of farmland, An analysis unit analyzes the data collected by the aforementioned monitoring unit, The system includes a control unit that controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) It includes a harvesting control unit that controls the operation of the harvesting robot. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a drone control unit that controls the drone's movements. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with an adjustment unit that adjusts the timing of harvesting according to weather or seasonal variations. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a monitoring unit that monitors the operation of harvesting robots and drones in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, Collect detailed data by focusing on specific areas of farmland. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, Anomaly detection algorithms are used to immediately report abnormal farmland conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It estimates user sentiment and prioritizes monitoring data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, Dynamically change the monitoring range based on the geographical characteristics of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, We predict anomalies by referring to historical data on agricultural land. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Perform data cleaning to improve the accuracy of collected data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Perform a multifaceted analysis by combining different analytical algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The analysis results are divided by region, taking into account the geographical information of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Compare with past analysis results to understand the trend. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, It estimates the user's emotions and adjusts the timing of control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, We will conduct simulations to optimize the operation of harvesting robots and drones. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, Incorporate emergency response procedures in case of an anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, It estimates the user's emotions and determines the priority of control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, The operation pattern is changed to take into account the geographical conditions of the farmland. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, The operation is adjusted to take into account coordination with other agricultural machinery. The system described in Appendix 1, characterized by the features described herein. (Note 24) The harvest control unit, It estimates the user's emotions and adjusts the harvest timing based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The harvest control unit, The ripeness of fruits and vegetables is evaluated in real time for harvesting. The system described in Appendix 2, characterized by the features described herein. (Note 26) The harvest control unit, Perform path planning to optimize the operation of harvesting robots. The system described in Appendix 2, characterized by the features described herein. (Note 27) The harvest control unit, It estimates user sentiment and determines harvest priorities based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 28) The harvest control unit, Change harvesting patterns to take into account the geographical conditions of the farmland. The system described in Appendix 2, characterized by the features described herein. (Note 29) The harvest control unit, Evaluate and sort the harvested produce based on its quality. The system described in Appendix 2, characterized by the features described herein. (Note 30) The drone control unit is The system estimates the user's emotions and adjusts the drone's flight pattern based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The drone control unit is Collect and analyze detailed topographic data of agricultural land. The system described in Appendix 3, characterized by the features described herein. (Note 32) The drone control unit is Anomaly detection algorithms are used to immediately report abnormal farmland conditions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The drone control unit is The system estimates the user's emotions and determines the drone's flight priority based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The drone control unit is The flight range is dynamically adjusted to take into account the geographical characteristics of the farmland. The system described in Appendix 3, characterized by the features described herein. (Note 35) The drone control unit is We predict anomalies by referring to historical data on agricultural land. The system described in Appendix 3, characterized by the features described herein. (Note 36) The adjustment unit is, It estimates the user's emotions and adjusts the harvest timing based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The adjustment unit is, Optimizing harvest plans using weather forecast data The system described in Appendix 4, characterized by the features described herein. (Note 38) The adjustment unit is, Real-time evaluation of crop growth data determines the optimal harvest time. The system described in Appendix 4, characterized by the features described herein. (Note 39) The adjustment unit is, It estimates user sentiment and determines harvest priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 40) The adjustment unit is, The harvest plan will be modified to take into account the geographical conditions of the farmland. The system described in Appendix 4, characterized by the features described herein. (Note 41) The adjustment unit is, Adjusting harvest timing to coordinate with other agricultural machinery. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department, which monitors the condition of farmland, An analysis unit analyzes the data collected by the aforementioned monitoring unit, The system includes a control unit that controls the operation of the harvesting robot and drone based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. It includes a harvesting control unit that controls the operation of the harvesting robot. The system according to feature 1.

3. It includes a drone control unit that controls the drone's movements. The system according to feature 1.

4. It is equipped with an adjustment unit that adjusts the timing of harvesting according to weather or seasonal variations. The system according to feature 1.

5. It includes a monitoring unit that monitors the operation of harvesting robots and drones in real time. The system according to feature 1.

6. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

7. The aforementioned monitoring unit, Collect detailed data by focusing on specific areas of farmland. The system according to feature 1.

8. The aforementioned monitoring unit, Anomaly detection algorithms are used to immediately report abnormal farmland conditions. The system according to feature 1.

9. The aforementioned monitoring unit, It estimates user sentiment and prioritizes monitoring data based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned monitoring unit, Dynamically change the monitoring range based on the geographical characteristics of the farmland. The system according to feature 1.

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