system

Abstract

The system according to this embodiment aims to efficiently monitor the growth status of plants and to diagnose abnormalities and diseases, manage them, harvest them, and deliver them. [Solution] The system according to the embodiment comprises a detection unit, a diagnostic unit, a management unit, a harvesting unit, and a delivery unit. The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. The diagnostic unit analyzes the abnormalities and disease symptoms detected by the detection unit and performs diagnosis and prediction in real time. The management unit performs growth management based on the diagnostic results obtained by the diagnostic unit. The harvesting unit harvests the plants managed by the management unit. The delivery unit delivers the agricultural products harvested by the harvesting unit based on supply and demand.

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, including 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 conventional technology, the processes of monitoring the growth state of plants, detecting, diagnosing, managing, harvesting, and delivering abnormalities are not efficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently monitor the growth state of plants and perform diagnosis, management, harvesting, and delivery of abnormalities and diseases.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, a diagnostic unit, a management unit, a harvesting unit, and a delivery unit. The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. The diagnostic unit analyzes the abnormalities and disease symptoms detected by the detection unit and performs diagnosis and prediction in real time. The management unit performs growth management based on the diagnostic results obtained by the diagnostic unit. The harvesting unit harvests the plants managed by the management unit. The delivery unit delivers the agricultural products harvested by the harvesting unit based on supply and demand. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently monitor the growth status of plants, diagnose abnormalities and diseases, manage the plants, harvest them, and deliver them. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 to say, "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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) The agricultural support system according to an embodiment of the present invention is a system that uses image recognition technology and a multimodal large-scale language model (LLM) to detect abnormalities and disease symptoms during agricultural growth in real time, and to diagnose and predict them. The agricultural support system monitors the growth status of plants and detects abnormalities and disease symptoms. Next, it uses a multimodal LLM to analyze the detected abnormalities and disease symptoms and to diagnose and predict them in real time. This makes it possible to propose effective countermeasures to agricultural producers. Furthermore, weed management and growth management (water and fertilizer) are also managed by AI. Specifically, sensors are used to monitor soil moisture and nutrient status, and the AI ​​controls the optimal supply of water and fertilizer. This optimizes plant growth and improves quality and yield. Automated agricultural robots are used for harvesting. The agricultural robots are controlled by AI to optimize the harvesting time and method. This solves the problem of labor shortages and makes harvesting more efficient. Finally, a system is built to deliver agricultural products to the appropriate locations to meet supply and demand. AI simulates the supply and demand of agricultural products and provides a marketplace that supplies produce to the optimal time and region. This allows food suppliers to distribute produce efficiently, and consumers to obtain safe and high-quality produce. For example, the agricultural support system monitors plant growth and detects abnormalities and disease symptoms. Next, a multimodal LLM is used to analyze the detected abnormalities and disease symptoms and perform real-time diagnoses and predictions. This allows for the suggestion of effective measures for agricultural producers. Furthermore, weed management and growth management (water and fertilizer) are also managed by AI. Specifically, sensors are used to monitor soil moisture and nutrient status, and the AI ​​controls the optimal supply of water and fertilizer. This optimizes plant growth and improves quality and yield. Automated agricultural robots are used for harvesting. These agricultural robots are controlled by AI and optimize the harvesting time and method. This solves the problem of labor shortages and improves the efficiency of harvesting. Finally, a system is built to deliver agricultural products to the right places to meet supply and demand.AI simulates the supply and demand of agricultural products and provides a marketplace that supplies agricultural products at the optimal time and in the optimal region. This allows food suppliers to distribute agricultural products efficiently, and consumers to obtain safe and high-quality produce. As a result, the agricultural support system enables farmers to easily monitor growth conditions and detect diseases early, improving yield and quality. Food suppliers can also distribute agricultural products efficiently while balancing supply and demand, and consumers can obtain safe and high-quality produce. Ultimately, this contributes to solving food problems and aiming to realize sustainable agriculture.

[0029] The agricultural support system according to this embodiment comprises a detection unit, a diagnostic unit, a management unit, a harvesting unit, and a delivery unit. The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. For example, the detection unit uses image recognition technology to analyze the color and shape of plant leaves and detect signs of abnormalities and disease. The detection unit can also use sensors to monitor soil moisture and nutrient status and detect abnormalities. Furthermore, the detection unit can use drones to monitor a wide area of ​​a field and detect abnormalities. For example, the detection unit uses image recognition technology to analyze the color and shape of plant leaves and detect signs of abnormalities and disease. The detection unit can also use sensors to monitor soil moisture and nutrient status and detect abnormalities. Furthermore, the detection unit can use drones to monitor a wide area of ​​a field and detect abnormalities. The diagnostic unit analyzes the abnormalities and disease symptoms detected by the detection unit and performs diagnosis and prediction in real time. For example, the diagnostic unit uses a multimodal LLM to analyze the abnormalities and disease symptoms and output the diagnosis results. The diagnostic unit can also improve the accuracy of its diagnoses by referring to past diagnostic data. Furthermore, the diagnostic unit can apply different diagnostic algorithms depending on the stage of the abnormality or disease progression. For example, the diagnostic unit uses multimodal LLM to analyze the symptoms of abnormalities and diseases and output diagnostic results. The diagnostic unit can also improve the accuracy of its diagnoses by referring to past diagnostic data. Furthermore, the diagnostic unit can apply different diagnostic algorithms depending on the stage of the abnormality or disease progression. The management unit performs growth management based on the diagnostic results obtained by the diagnostic unit. The management unit, for example, uses sensors to monitor soil moisture and nutrient status and controls the supply of optimal water and fertilizer. Furthermore, the management unit can apply different management methods depending on the stage of plant growth. Furthermore, the management unit can optimize its management algorithms by referring to past management data. For example, the management unit uses sensors to monitor soil moisture and nutrient status and controls the supply of optimal water and fertilizer. Furthermore, the management unit can apply different management methods depending on the stage of plant growth. Furthermore, the management unit can optimize its management algorithms by referring to past management data. The harvesting unit harvests the plants managed by the management unit.The harvesting unit, for example, uses AI-controlled agricultural robots to perform harvesting tasks. The harvesting unit can also optimize the harvesting time and method. Furthermore, the harvesting unit can optimize the harvesting algorithm by referring to past harvesting data. The distribution unit distributes the agricultural products harvested by the harvesting unit based on supply and demand. The distribution unit, for example, uses AI to simulate the supply and demand of agricultural products and supplies them to the optimal time and region. The distribution unit can also monitor the storage condition of agricultural products in real time and select the optimal distribution method. Furthermore, the distribution unit can optimize the distribution algorithm by referring to past distribution data. As a result, the agricultural support system according to this embodiment can monitor the growth status of plants, detect abnormalities and disease symptoms, perform diagnosis and prediction, and efficiently manage, harvest, and deliver the plants.

[0030] The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. Specifically, it uses image recognition technology to analyze the color and shape of plant leaves and detect signs of abnormalities or disease. For example, if plant leaves are turning yellow, this may be a sign of nutrient deficiency or an early symptom of disease. Image recognition technology can detect these subtle changes with high accuracy, enabling early detection of abnormalities. The detection unit can also monitor soil moisture and nutrient status using sensors and detect abnormalities. Soil sensors measure soil moisture content, pH value, and nutrient concentration in real time and transmit this data to a central database. This allows for the maintenance of optimal environmental conditions required by plants. Furthermore, the detection unit can also use drones to monitor wide-area fields and detect abnormalities. Drones equipped with high-resolution cameras and multispectral sensors can scan wide-area fields in a short time and identify abnormal areas. For example, drones analyze the color and shape of plant leaves to detect abnormalities and disease symptoms. Drones can also monitor soil moisture and nutrient status using sensors and detect abnormalities. This allows the detection unit to efficiently monitor a wide area of ​​the field and detect abnormalities early.

[0031] The diagnostic unit analyzes the symptoms of abnormalities and diseases detected by the detection unit and performs diagnoses and predictions in real time. For example, the diagnostic unit uses a multimodal LLM to analyze the symptoms of abnormalities and diseases and output diagnostic results. The multimodal LLM comprehensively analyzes image data and sensor data to identify the cause and progression of abnormalities. For example, it analyzes discoloration and changes in shape of plant leaves to diagnose signs of specific diseases or nutritional deficiencies. The diagnostic unit can also improve the accuracy of diagnoses by referring to past diagnostic data. The past database records the diagnostic results and countermeasures when similar symptoms or abnormalities occurred, and by referring to this, more accurate diagnoses are possible. Furthermore, the diagnostic unit can apply different diagnostic algorithms depending on the progression of the abnormality or disease. For example, it proposes early detection and preventive measures for diseases in the early stages, and therapeutic measures for diseases that have progressed. In this way, the diagnostic unit can provide optimal countermeasures according to the progression of the abnormality or disease.

[0032] The management department manages plant growth based on diagnostic results obtained by the diagnostic department. For example, the management department monitors soil moisture and nutrient status using sensors and controls the supply of optimal water and fertilizer. Specifically, based on data obtained from soil sensors, it automatically adjusts irrigation and fertilizer supply systems to maintain the optimal environment required by the plants. The management department can also apply different management methods depending on the stage of plant growth. For example, it can supply specific nutrients to promote root development in the early stages of growth and nutrients to promote fruit maturation in the later stages of growth. Furthermore, the management department can optimize management algorithms by referring to past management data. The past database records optimal management methods and results under specific conditions, and by adjusting the management algorithm based on this, more effective growth management becomes possible. In this way, the management department can optimally support plant growth and improve yield and quality.

[0033] The harvesting unit harvests plants managed by the management unit. The harvesting unit uses, for example, AI-controlled agricultural robots to perform harvesting tasks. These agricultural robots use sensors to detect the growth stage and harvest time of plants and harvest at the optimal time. For example, they measure the color and hardness of the fruit with sensors to determine the best time to harvest. The harvesting unit can also optimize the harvesting time and method. For example, certain fruits may be best picked by hand, while other crops may be better suited to mechanical harvesting. This allows the harvesting unit to select the optimal harvesting method for each crop, achieving efficient harvesting while maintaining quality. Furthermore, the harvesting unit can optimize its harvesting algorithm by referring to past harvesting data. The past database contains information on harvesting time and method, and by adjusting the harvesting algorithm based on this data, more effective harvesting becomes possible. This allows the harvesting unit to streamline harvesting operations and improve yield and quality.

[0034] The distribution department distributes agricultural products harvested by the harvesting department based on supply and demand. For example, the distribution department uses AI to simulate the supply and demand of agricultural products and supply them to the optimal time and region. The AI ​​analyzes past supply and demand data and market trends to predict when and where demand will be high. This ensures that agricultural products are supplied to the market at the time when they will be consumed most effectively. The distribution department can also monitor the storage conditions of agricultural products in real time and select the optimal distribution method. For example, it monitors temperature and humidity to ensure that agricultural products reach consumers in a fresh state. Furthermore, the distribution department can optimize its distribution algorithms by referring to past distribution data. The historical database contains information on distribution routes and storage conditions, and by adjusting the distribution algorithm based on this information, more efficient distribution becomes possible. As a result, the distribution department can deliver agricultural products to consumers quickly and efficiently, optimizing the supply and demand balance while maintaining quality.

[0035] The management unit includes a monitoring unit that monitors soil moisture and nutrient status. The monitoring unit, for example, measures soil moisture using sensors and collects data. The monitoring unit can also analyze soil samples to measure soil nutrient status. Furthermore, the monitoring unit can collect data in real time and provide it to the management unit. For example, the monitoring unit measures soil moisture using sensors and collects data. The monitoring unit can also analyze soil samples to measure soil nutrient status. Furthermore, the monitoring unit can collect data in real time and provide it to the management unit. This allows for the optimization of plant growth by monitoring soil moisture and nutrient status. 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 soil moisture data acquired by sensors into a generating AI and have the generating AI perform data analysis.

[0036] The management unit includes a supply unit that controls the optimal supply of water and fertilizer. The supply unit, for example, measures soil moisture using sensors and determines the optimal amount of water to supply. The supply unit can also measure the soil's nutrient status and determine the optimal amount of fertilizer to supply. Furthermore, the supply unit can collect data in real time and provide it to the management unit. This allows for the optimization of plant growth by controlling the optimal supply of water and fertilizer. Some or all of the above processes in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input soil moisture data acquired by sensors into a generating AI and have the generating AI perform data analysis.

[0037] The harvesting unit includes an optimization unit that optimizes the harvesting time and method. The optimization unit, for example, uses AI to analyze the growth state of plants and determine the optimal harvesting time. The optimization unit can also control the operation of the harvesting machine to optimize the harvesting method. Furthermore, the optimization unit can optimize the harvesting algorithm by referring to past harvesting data. By optimizing the harvesting time and method, the efficiency of the harvesting work is improved. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input plant growth state data into a generating AI and have the generating AI determine the optimal harvesting time.

[0038] The distribution department simulates the supply and demand of agricultural products and provides a marketplace that supplies agricultural products at the optimal time and in the optimal region. For example, the distribution department uses AI to analyze market data and simulate the balance of supply and demand. The distribution department can also calculate the optimal distribution route and supply agricultural products efficiently. Furthermore, the distribution department can optimize the distribution algorithm by referring to past distribution data. This allows for the balancing of supply and demand by simulating the supply and demand of agricultural products and supplying them at the optimal time and in the optimal region. Some or all of the above processes in the distribution department may be performed using AI, for example, or without AI. For example, the distribution department can input market data into a generating AI and have the generating AI perform a supply and demand simulation.

[0039] The detection unit applies different monitoring algorithms depending on the plant's growth stage. For example, during the germination stage, the detection unit focuses on monitoring soil moisture and temperature. During the growth stage, it can also monitor changes in leaf color and shape. Furthermore, during the flowering stage, it can monitor the flowering status and signs of disease. This allows for the early detection of abnormalities and diseases by applying monitoring algorithms appropriate to the plant's growth stage. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input plant growth stage data into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0040] The detection unit sets specific parameters to detect different abnormalities and disease symptoms for each type of plant. For example, the detection unit sets parameters to detect leaf spots and fruit discoloration for tomatoes. It can also set parameters to detect fruit rot and mold growth for strawberries. Furthermore, it can set parameters to detect leaf wilting and insect damage for lettuce. By setting specific parameters for each type of plant, the accuracy of detecting abnormalities and diseases is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data for each type of plant into a generating AI and have the generating AI set specific parameters.

[0041] The detection unit monitors the plant's growing environment in real time and issues an alert if an abnormality occurs. For example, the detection unit can issue an alert if the temperature exceeds a set range. It can also issue an alert if the humidity decreases. Furthermore, it can also issue an alert if the amount of light is insufficient. This allows for real-time monitoring of the plant's growing environment and prompt response by issuing alerts when abnormalities occur. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input temperature data into a generating AI and have the generating AI perform abnormality detection and alert generation.

[0042] The detection unit uses a drone to monitor a wide area when monitoring the growth status of plants. For example, the detection unit periodically photographs a wide area of ​​a field using a drone and detects anomalies using image recognition technology. The detection unit can also measure temperature and humidity with sensors mounted on the drone and detect anomalies. Furthermore, the detection unit can acquire high-resolution images using a drone and perform detailed anomaly detection. For example, the detection unit periodically photographs a wide area of ​​a field using a drone and detects anomalies using image recognition technology. The detection unit can also measure temperature and humidity with sensors mounted on the drone and detect anomalies. Furthermore, the detection unit can acquire high-resolution images using a drone and perform detailed anomaly detection. This enables efficient anomaly detection by monitoring a wide area using a drone. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image data acquired by the drone into a generating AI and have the generating AI perform anomaly detection.

[0043] The diagnostic unit applies different diagnostic algorithms depending on the abnormality or the stage of disease progression. For example, the diagnostic unit applies an algorithm that suggests preventive measures for diseases in their early stages. It can also apply an algorithm that suggests treatment methods for diseases in progress. Furthermore, the diagnostic unit can apply an algorithm that suggests emergency measures for serious diseases. By applying a diagnostic algorithm according to the abnormality or the stage of disease progression, appropriate measures can be suggested. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input disease progression data into a generating AI and have the generating AI execute the application of the diagnostic algorithm.

[0044] The diagnostic unit improves the accuracy of the diagnosis by referring to past diagnostic data. For example, the diagnostic unit identifies diseases with similar symptoms based on past diagnostic data. The diagnostic unit can also analyze past diagnostic data and optimize the diagnostic algorithm. Furthermore, the diagnostic unit can improve the reliability of the diagnostic results by referring to past diagnostic data. For example, the diagnostic unit identifies diseases with similar symptoms based on past diagnostic data. The diagnostic unit can also analyze past diagnostic data and optimize the diagnostic algorithm. Furthermore, the diagnostic unit can improve the reliability of the diagnostic results by referring to past diagnostic data. As a result, the accuracy of the diagnosis is improved by referring to past diagnostic data. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input past diagnostic data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0045] The diagnostic unit uses other modal data, such as audio data and temperature data, in conjunction with the diagnostic unit when analyzing abnormalities and disease symptoms. For example, the diagnostic unit can analyze audio data to detect abnormal sounds in plants. It can also analyze temperature data to detect abnormal temperature changes. Furthermore, the diagnostic unit can integrate multiple modal data to perform a more accurate diagnosis. This improves the accuracy of the diagnosis by using other modal data in combination. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input audio data and temperature data into a generating AI and have the generating AI perform abnormality detection.

[0046] The diagnostic unit considers the plant's genetic information when analyzing abnormalities and disease symptoms. For example, the diagnostic unit evaluates susceptibility to specific diseases based on the plant's genetic information. The diagnostic unit can also analyze genetic information and predict the risk of disease progression. Furthermore, the diagnostic unit can propose the optimal treatment method considering the genetic information. This allows for more accurate diagnoses by considering the plant's genetic information. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the plant's genetic information into a generating AI and have the generating AI perform the diagnosis.

[0047] The management department applies different management methods depending on the type of abnormality or disease. For example, the management department applies preventive management methods in the early stages of a disease. The management department can also apply therapeutic management methods to diseases that are progressing. Furthermore, the management department can also apply emergency measures to serious diseases. This allows appropriate measures to be taken by applying management methods according to the type of abnormality or disease. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input disease type data into a generating AI and have the generating AI execute the application of management methods.

[0048] The management department optimizes the management algorithm by referring to past management data. For example, the management department selects the optimal management method based on past management data. The management department can also analyze past management data and optimize the management algorithm. Furthermore, the management department can improve the reliability of management results by referring to past management data. For example, the management department selects the optimal management method based on past management data. The management department can also analyze past management data and optimize the management algorithm. Furthermore, the management department can improve the reliability of management results by referring to past management data. As a result, the accuracy of the management algorithm is improved by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management data into a generating AI and have the generating AI perform the optimization of the management algorithm.

[0049] The management unit adjusts the plant's growing environment in real time when performing growth management. For example, if the temperature exceeds the set range, the management unit will activate a cooling device. The management unit can also activate a humidifier if the humidity drops. Furthermore, the management unit can activate a lighting device if the light level is insufficient. By adjusting the plant's growing environment in real time, the optimal growing environment can be maintained. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input temperature data into a generating AI and have the generating AI perform the adjustment of the growing environment.

[0050] The management department selects the optimal management method when managing plant growth, taking into account the plant's genetic information. For example, the management department can prioritize the supply of specific nutrients based on the plant's genetic information. The management department can also analyze the genetic information to determine the optimal amount of water and fertilizer to supply. Furthermore, the management department can implement disease prevention measures while taking the genetic information into consideration. For example, the management department can prioritize the supply of specific nutrients based on the plant's genetic information. The management department can also analyze the genetic information to determine the optimal amount of water and fertilizer to supply. Furthermore, the management department can implement disease prevention measures while taking the genetic information into consideration. This allows for more accurate management by considering the plant's genetic information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the plant's genetic information into a generating AI and have the generating AI select the management method.

[0051] The harvesting unit applies different harvesting algorithms depending on the plant's growth stage. For example, the harvesting unit recommends manual harvesting in the early stages of growth. It can also apply a semi-automated harvesting method in the mid-stages of growth. Furthermore, it can apply a fully automated harvesting method in the late stages of growth. This allows for appropriate harvesting by applying harvesting algorithms tailored to the plant's growth stage. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without AI. For example, the harvesting unit can input plant growth stage data into a generating AI and have the generating AI execute the application of the harvesting algorithm.

[0052] The harvesting unit optimizes the harvesting algorithm by referring to past harvesting data. For example, the harvesting unit determines the optimal harvesting time based on past harvesting data. The harvesting unit can also analyze past harvesting data and optimize the harvesting algorithm. Furthermore, the harvesting unit can improve the reliability of the harvesting results by referring to past harvesting data. For example, the harvesting unit determines the optimal harvesting time based on past harvesting data. The harvesting unit can also analyze past harvesting data and optimize the harvesting algorithm. Furthermore, the harvesting unit can improve the reliability of the harvesting results by referring to past harvesting data. This improves the accuracy of the harvesting algorithm by referring to past harvesting data. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without AI. For example, the harvesting unit can input past harvesting data into a generating AI and have the generating AI perform the optimization of the harvesting algorithm.

[0053] The harvesting unit can improve harvesting efficiency by using drones during harvesting operations. For example, the harvesting unit can use drones to monitor a wide area of ​​the field and identify the target crops. The harvesting unit can also check the condition of the target crops using sensors mounted on the drones. Furthermore, the harvesting unit can automate the harvesting process using drones to improve efficiency. For example, the harvesting unit can use drones to monitor a wide area of ​​the field and identify the target crops. The harvesting unit can also check the condition of the target crops using sensors mounted on the drones. Furthermore, the harvesting unit can automate the harvesting process using drones to improve efficiency. This makes efficient harvesting possible by using drones for harvesting operations. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or not. For example, the harvesting unit can input data acquired by drones into a generating AI and have the generating AI perform tasks to improve the efficiency of the harvesting process.

[0054] The harvesting unit selects the optimal harvesting method when performing harvesting operations, taking into account the genetic information of the plants. For example, the harvesting unit selects a specific harvesting method based on the genetic information of the plants. The harvesting unit can also analyze the genetic information to determine the optimal harvesting time. Furthermore, the harvesting unit can optimize the harvesting method by taking the genetic information into consideration. For example, the harvesting unit selects a specific harvesting method based on the genetic information of the plants. The harvesting unit can also analyze the genetic information to determine the optimal harvesting time. Furthermore, the harvesting unit can optimize the harvesting method by taking the genetic information into consideration. This makes it possible to harvest with higher accuracy by taking the genetic information of the plants into consideration. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without using AI. For example, the harvesting unit can input the genetic information of the plants into a generating AI and have the generating AI select the harvesting method.

[0055] The delivery department applies different delivery algorithms depending on the type of agricultural product. For example, the delivery department applies a rapid delivery algorithm to fresh produce. The delivery department can also apply a standard delivery algorithm to non-perishable foods. Furthermore, the delivery department can apply a temperature-controlled delivery algorithm to foods that require specific temperature control. This enables appropriate delivery by applying a delivery algorithm appropriate to the type of agricultural product. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input agricultural product type data into a generating AI and have the generating AI execute the application of the delivery algorithm.

[0056] The delivery unit optimizes the delivery algorithm by referring to past delivery data. For example, the delivery unit determines the optimal delivery route based on past delivery data. The delivery unit can also analyze past delivery data and optimize the delivery algorithm. Furthermore, the delivery unit can improve the reliability of delivery results by referring to past delivery data. For example, the delivery unit determines the optimal delivery route based on past delivery data. The delivery unit can also analyze past delivery data and optimize the delivery algorithm. Furthermore, the delivery unit can improve the reliability of delivery results by referring to past delivery data. This improves the accuracy of the delivery algorithm by referring to past delivery data. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input past delivery data into a generating AI and have the generating AI perform the optimization of the delivery algorithm.

[0057] The delivery department can improve the efficiency of deliveries by using drones. For example, the delivery department can use drones to quickly deliver over a wide area. The delivery department can also use sensors mounted on the drones to check the condition of the items to be delivered. Furthermore, the delivery department can use drones to automate deliveries and improve efficiency. For example, the delivery department can use drones to quickly deliver over a wide area. The delivery department can also use sensors mounted on the drones to check the condition of the items to be delivered. Furthermore, the delivery department can use drones to automate deliveries and improve efficiency. This makes efficient deliveries possible by using drones for deliveries. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input data acquired by drones into a generating AI and have the generating AI perform tasks to improve the efficiency of deliveries.

[0058] The delivery department monitors the storage condition of agricultural products in real time when performing delivery operations and selects the optimal delivery method. For example, the delivery department monitors the storage condition of agricultural products in real time and selects the optimal delivery route. The delivery department can also select a delivery method that requires temperature control depending on the storage condition. Furthermore, the delivery department can also select a rapid delivery method considering the storage condition. For example, the delivery department monitors the storage condition of agricultural products in real time and selects the optimal delivery route. The delivery department can also select a delivery method that requires temperature control depending on the storage condition. Furthermore, the delivery department can also select a rapid delivery method considering the storage condition. This allows the optimal delivery method to be selected by monitoring the storage condition of agricultural products in real time. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input agricultural product storage condition data into a generating AI and have the generating AI select the optimal delivery method.

[0059] The distribution department simulates the supply and demand of agricultural products when carrying out distribution operations, and supplies agricultural products to the optimal time and region. For example, the distribution department conducts supply and demand simulations to determine the optimal distribution time. The distribution department can also select the optimal distribution area based on the supply and demand simulations. Furthermore, the distribution department can use supply and demand simulations to formulate efficient distribution plans. For example, the distribution department conducts supply and demand simulations to determine the optimal distribution time. The distribution department can also select the optimal distribution area based on the supply and demand simulations. Furthermore, the distribution department can use supply and demand simulations to formulate efficient distribution plans. In this way, by simulating the supply and demand of agricultural products, they can be supplied to the optimal time and region. Some or all of the above processes in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input supply and demand data into a generating AI and have the generating AI execute supply and demand simulations.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The detection unit can also use drones to monitor a wide area when monitoring the growth status of plants. For example, it can periodically photograph a wide field using a drone and detect anomalies using image recognition technology. It can also measure temperature and humidity with sensors mounted on the drone and detect anomalies. Furthermore, it can acquire high-resolution images using a drone and perform detailed anomaly detection. This enables efficient anomaly detection by monitoring a wide area using a drone. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image data acquired by a drone into a generating AI and have the generating AI perform anomaly detection.

[0062] The diagnostic unit can also apply different diagnostic algorithms depending on the abnormality or the stage of disease progression. For example, an algorithm that suggests preventive measures can be applied to diseases in their early stages. An algorithm that suggests treatment methods can be applied to diseases in progress. Furthermore, an algorithm that suggests emergency measures can be applied to serious diseases. In this way, appropriate measures can be suggested by applying a diagnostic algorithm that is appropriate to the abnormality or the stage of disease progression. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input disease progression data into a generating AI and have the generating AI execute the application of the diagnostic algorithm.

[0063] The delivery department can also monitor the storage condition of agricultural products in real time and select the optimal delivery method. For example, it can monitor the storage condition of agricultural products in real time and select the optimal delivery route. It can also select a delivery method that requires temperature control depending on the storage condition. Furthermore, it can select a rapid delivery method considering the storage condition. In this way, the optimal delivery method can be selected by monitoring the storage condition of agricultural products in real time. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input agricultural product storage condition data into a generating AI and have the generating AI select the optimal delivery method.

[0064] The management department can also optimize management algorithms by referring to past management data. For example, it can select the optimal management method based on past management data. It can also analyze past management data and optimize management algorithms. Furthermore, it can improve the reliability of management results by referring to past management data. In this way, the accuracy of the management algorithm is improved by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past management data into a generating AI and have the generating AI perform the optimization of the management algorithm.

[0065] The harvesting unit can also optimize its harvesting algorithm by referring to past harvesting data. For example, it can determine the optimal harvesting time based on past harvesting data. It can also analyze past harvesting data and optimize the harvesting algorithm. Furthermore, it can improve the reliability of harvesting results by referring to past harvesting data. This improves the accuracy of the harvesting algorithm by referring to past harvesting data. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without AI. For example, the harvesting unit can input past harvesting data into a generating AI and have the generating AI perform the optimization of the harvesting algorithm.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. For example, it uses image recognition technology to analyze the color and shape of plant leaves to detect abnormalities and signs of disease. It can also use sensors to monitor soil moisture and nutrient status and detect abnormalities. Furthermore, it can use drones to monitor wide areas of fields and detect abnormalities. Step 2: The diagnostic unit analyzes the abnormalities and disease symptoms detected by the detection unit and performs diagnosis and prediction in real time. For example, it uses a multimodal LLM to analyze the abnormalities and disease symptoms and outputs a diagnostic result. It can also improve the accuracy of the diagnosis by referring to past diagnostic data. Furthermore, different diagnostic algorithms can be applied depending on the progression of the abnormality or disease. Step 3: The management unit performs growth management based on the diagnostic results obtained by the diagnostic unit. For example, it monitors soil moisture and nutrient status using sensors and controls the supply of optimal water and fertilizer. It can also apply different management methods depending on the stage of plant growth. Furthermore, it can optimize the management algorithm by referring to past management data. Step 4: The harvesting unit harvests the plants managed by the management unit. For example, harvesting is performed using AI-controlled agricultural robots. It is also possible to optimize the harvesting time and method. Furthermore, the harvesting algorithm can be optimized by referring to past harvesting data. Step 5: The distribution department distributes the agricultural products harvested by the harvesting department based on supply and demand. For example, AI simulates the supply and demand of agricultural products and supplies them to the optimal time and region. It can also monitor the storage condition of agricultural products in real time and select the optimal distribution method. Furthermore, it can optimize the distribution algorithm by referring to past distribution data.

[0068] (Example of form 2) The agricultural support system according to an embodiment of the present invention is a system that uses image recognition technology and a multimodal large-scale language model (LLM) to detect abnormalities and disease symptoms during agricultural growth in real time, and to diagnose and predict them. The agricultural support system monitors the growth status of plants and detects abnormalities and disease symptoms. Next, it uses a multimodal LLM to analyze the detected abnormalities and disease symptoms and to diagnose and predict them in real time. This makes it possible to propose effective countermeasures to agricultural producers. Furthermore, weed management and growth management (water and fertilizer) are also managed by AI. Specifically, sensors are used to monitor soil moisture and nutrient status, and the AI ​​controls the optimal supply of water and fertilizer. This optimizes plant growth and improves quality and yield. Automated agricultural robots are used for harvesting. The agricultural robots are controlled by AI to optimize the harvesting time and method. This solves the problem of labor shortages and makes harvesting more efficient. Finally, a system is built to deliver agricultural products to the appropriate locations to meet supply and demand. AI simulates the supply and demand of agricultural products and provides a marketplace that supplies produce to the optimal time and region. This allows food suppliers to distribute produce efficiently, and consumers to obtain safe and high-quality produce. For example, the agricultural support system monitors plant growth and detects abnormalities and disease symptoms. Next, a multimodal LLM is used to analyze the detected abnormalities and disease symptoms and perform real-time diagnoses and predictions. This allows for the suggestion of effective measures for agricultural producers. Furthermore, weed management and growth management (water and fertilizer) are also managed by AI. Specifically, sensors are used to monitor soil moisture and nutrient status, and the AI ​​controls the optimal supply of water and fertilizer. This optimizes plant growth and improves quality and yield. Automated agricultural robots are used for harvesting. These agricultural robots are controlled by AI and optimize the harvesting time and method. This solves the problem of labor shortages and improves the efficiency of harvesting. Finally, a system is built to deliver agricultural products to the right places to meet supply and demand.AI simulates the supply and demand of agricultural products and provides a marketplace that supplies agricultural products at the optimal time and in the optimal region. This allows food suppliers to distribute agricultural products efficiently, and consumers to obtain safe and high-quality produce. As a result, the agricultural support system enables farmers to easily monitor growth conditions and detect diseases early, improving yield and quality. Food suppliers can also distribute agricultural products efficiently while balancing supply and demand, and consumers can obtain safe and high-quality produce. Ultimately, this contributes to solving food problems and aiming to realize sustainable agriculture.

[0069] The agricultural support system according to this embodiment comprises a detection unit, a diagnostic unit, a management unit, a harvesting unit, and a delivery unit. The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. For example, the detection unit uses image recognition technology to analyze the color and shape of plant leaves and detect signs of abnormalities and disease. The detection unit can also use sensors to monitor soil moisture and nutrient status and detect abnormalities. Furthermore, the detection unit can use drones to monitor a wide area of ​​a field and detect abnormalities. For example, the detection unit uses image recognition technology to analyze the color and shape of plant leaves and detect signs of abnormalities and disease. The detection unit can also use sensors to monitor soil moisture and nutrient status and detect abnormalities. Furthermore, the detection unit can use drones to monitor a wide area of ​​a field and detect abnormalities. The diagnostic unit analyzes the abnormalities and disease symptoms detected by the detection unit and performs diagnosis and prediction in real time. For example, the diagnostic unit uses a multimodal LLM to analyze the abnormalities and disease symptoms and output the diagnosis results. The diagnostic unit can also improve the accuracy of its diagnoses by referring to past diagnostic data. Furthermore, the diagnostic unit can apply different diagnostic algorithms depending on the stage of the abnormality or disease progression. For example, the diagnostic unit uses multimodal LLM to analyze the symptoms of abnormalities and diseases and output diagnostic results. The diagnostic unit can also improve the accuracy of its diagnoses by referring to past diagnostic data. Furthermore, the diagnostic unit can apply different diagnostic algorithms depending on the stage of the abnormality or disease progression. The management unit performs growth management based on the diagnostic results obtained by the diagnostic unit. The management unit, for example, uses sensors to monitor soil moisture and nutrient status and controls the supply of optimal water and fertilizer. Furthermore, the management unit can apply different management methods depending on the stage of plant growth. Furthermore, the management unit can optimize its management algorithms by referring to past management data. For example, the management unit uses sensors to monitor soil moisture and nutrient status and controls the supply of optimal water and fertilizer. Furthermore, the management unit can apply different management methods depending on the stage of plant growth. Furthermore, the management unit can optimize its management algorithms by referring to past management data. The harvesting unit harvests the plants managed by the management unit.The harvesting unit, for example, uses AI-controlled agricultural robots to perform harvesting tasks. The harvesting unit can also optimize the harvesting time and method. Furthermore, the harvesting unit can optimize the harvesting algorithm by referring to past harvesting data. The distribution unit distributes the agricultural products harvested by the harvesting unit based on supply and demand. The distribution unit, for example, uses AI to simulate the supply and demand of agricultural products and supplies them to the optimal time and region. The distribution unit can also monitor the storage condition of agricultural products in real time and select the optimal distribution method. Furthermore, the distribution unit can optimize the distribution algorithm by referring to past distribution data. As a result, the agricultural support system according to this embodiment can monitor the growth status of plants, detect abnormalities and disease symptoms, perform diagnosis and prediction, and efficiently manage, harvest, and deliver the plants.

[0070] The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. Specifically, it uses image recognition technology to analyze the color and shape of plant leaves and detect signs of abnormalities or disease. For example, if plant leaves are turning yellow, this may be a sign of nutrient deficiency or an early symptom of disease. Image recognition technology can detect these subtle changes with high accuracy, enabling early detection of abnormalities. The detection unit can also monitor soil moisture and nutrient status using sensors and detect abnormalities. Soil sensors measure soil moisture content, pH value, and nutrient concentration in real time and transmit this data to a central database. This allows for the maintenance of optimal environmental conditions required by plants. Furthermore, the detection unit can also use drones to monitor wide-area fields and detect abnormalities. Drones equipped with high-resolution cameras and multispectral sensors can scan wide-area fields in a short time and identify abnormal areas. For example, drones analyze the color and shape of plant leaves to detect abnormalities and disease symptoms. Drones can also monitor soil moisture and nutrient status using sensors and detect abnormalities. This allows the detection unit to efficiently monitor a wide area of ​​the field and detect abnormalities early.

[0071] The diagnostic unit analyzes the symptoms of abnormalities and diseases detected by the detection unit and performs diagnoses and predictions in real time. For example, the diagnostic unit uses a multimodal LLM to analyze the symptoms of abnormalities and diseases and output diagnostic results. The multimodal LLM comprehensively analyzes image data and sensor data to identify the cause and progression of abnormalities. For example, it analyzes discoloration and changes in shape of plant leaves to diagnose signs of specific diseases or nutritional deficiencies. The diagnostic unit can also improve the accuracy of diagnoses by referring to past diagnostic data. The past database records the diagnostic results and countermeasures when similar symptoms or abnormalities occurred, and by referring to this, more accurate diagnoses are possible. Furthermore, the diagnostic unit can apply different diagnostic algorithms depending on the progression of the abnormality or disease. For example, it proposes early detection and preventive measures for diseases in the early stages, and therapeutic measures for diseases that have progressed. In this way, the diagnostic unit can provide optimal countermeasures according to the progression of the abnormality or disease.

[0072] The management department manages plant growth based on diagnostic results obtained by the diagnostic department. For example, the management department monitors soil moisture and nutrient status using sensors and controls the supply of optimal water and fertilizer. Specifically, based on data obtained from soil sensors, it automatically adjusts irrigation and fertilizer supply systems to maintain the optimal environment required by the plants. The management department can also apply different management methods depending on the stage of plant growth. For example, it can supply specific nutrients to promote root development in the early stages of growth and nutrients to promote fruit maturation in the later stages of growth. Furthermore, the management department can optimize management algorithms by referring to past management data. The past database records optimal management methods and results under specific conditions, and by adjusting the management algorithm based on this, more effective growth management becomes possible. In this way, the management department can optimally support plant growth and improve yield and quality.

[0073] The harvesting unit harvests plants managed by the management unit. The harvesting unit uses, for example, AI-controlled agricultural robots to perform harvesting tasks. These agricultural robots use sensors to detect the growth stage and harvest time of plants and harvest at the optimal time. For example, they measure the color and hardness of the fruit with sensors to determine the best time to harvest. The harvesting unit can also optimize the harvesting time and method. For example, certain fruits may be best picked by hand, while other crops may be better suited to mechanical harvesting. This allows the harvesting unit to select the optimal harvesting method for each crop, achieving efficient harvesting while maintaining quality. Furthermore, the harvesting unit can optimize its harvesting algorithm by referring to past harvesting data. The past database contains information on harvesting time and method, and by adjusting the harvesting algorithm based on this data, more effective harvesting becomes possible. This allows the harvesting unit to streamline harvesting operations and improve yield and quality.

[0074] The distribution department distributes agricultural products harvested by the harvesting department based on supply and demand. For example, the distribution department uses AI to simulate the supply and demand of agricultural products and supply them to the optimal time and region. The AI ​​analyzes past supply and demand data and market trends to predict when and where demand will be high. This ensures that agricultural products are supplied to the market at the time when they will be consumed most effectively. The distribution department can also monitor the storage conditions of agricultural products in real time and select the optimal distribution method. For example, it monitors temperature and humidity to ensure that agricultural products reach consumers in a fresh state. Furthermore, the distribution department can optimize its distribution algorithms by referring to past distribution data. The historical database contains information on distribution routes and storage conditions, and by adjusting the distribution algorithm based on this information, more efficient distribution becomes possible. As a result, the distribution department can deliver agricultural products to consumers quickly and efficiently, optimizing the supply and demand balance while maintaining quality.

[0075] The management unit includes a monitoring unit that monitors soil moisture and nutrient status. The monitoring unit, for example, measures soil moisture using sensors and collects data. The monitoring unit can also analyze soil samples to measure soil nutrient status. Furthermore, the monitoring unit can collect data in real time and provide it to the management unit. For example, the monitoring unit measures soil moisture using sensors and collects data. The monitoring unit can also analyze soil samples to measure soil nutrient status. Furthermore, the monitoring unit can collect data in real time and provide it to the management unit. This allows for the optimization of plant growth by monitoring soil moisture and nutrient status. 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 soil moisture data acquired by sensors into a generating AI and have the generating AI perform data analysis.

[0076] The management unit includes a supply unit that controls the optimal supply of water and fertilizer. The supply unit, for example, measures soil moisture using sensors and determines the optimal amount of water to supply. The supply unit can also measure the soil's nutrient status and determine the optimal amount of fertilizer to supply. Furthermore, the supply unit can collect data in real time and provide it to the management unit. This allows for the optimization of plant growth by controlling the optimal supply of water and fertilizer. Some or all of the above processes in the supply unit may be performed using AI, for example, or without AI. For example, the supply unit can input soil moisture data acquired by sensors into a generating AI and have the generating AI perform data analysis.

[0077] The harvesting unit includes an optimization unit that optimizes the harvesting time and method. The optimization unit, for example, uses AI to analyze the growth state of plants and determine the optimal harvesting time. The optimization unit can also control the operation of the harvesting machine to optimize the harvesting method. Furthermore, the optimization unit can optimize the harvesting algorithm by referring to past harvesting data. By optimizing the harvesting time and method, the efficiency of the harvesting work is improved. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input plant growth state data into a generating AI and have the generating AI determine the optimal harvesting time.

[0078] The distribution department simulates the supply and demand of agricultural products and provides a marketplace that supplies agricultural products at the optimal time and in the optimal region. For example, the distribution department uses AI to analyze market data and simulate the balance of supply and demand. The distribution department can also calculate the optimal distribution route and supply agricultural products efficiently. Furthermore, the distribution department can optimize the distribution algorithm by referring to past distribution data. This allows for the balancing of supply and demand by simulating the supply and demand of agricultural products and supplying them at the optimal time and in the optimal region. Some or all of the above processes in the distribution department may be performed using AI, for example, or without AI. For example, the distribution department can input market data into a generating AI and have the generating AI perform a supply and demand simulation.

[0079] The detection unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The detection unit estimates the user's emotions using, for example, facial recognition technology. The detection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the detection unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). This reduces the burden on the user by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using, 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 detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's facial expression data into a generating AI, allowing the generating AI to perform emotion estimation.

[0080] The detection unit applies different monitoring algorithms depending on the plant's growth stage. For example, during the germination stage, the detection unit focuses on monitoring soil moisture and temperature. During the growth stage, it can also monitor changes in leaf color and shape. Furthermore, during the flowering stage, it can monitor the flowering status and signs of disease. This allows for the early detection of abnormalities and diseases by applying monitoring algorithms appropriate to the plant's growth stage. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input plant growth stage data into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0081] The detection unit sets specific parameters to detect different abnormalities and disease symptoms for each type of plant. For example, the detection unit sets parameters to detect leaf spots and fruit discoloration for tomatoes. It can also set parameters to detect fruit rot and mold growth for strawberries. Furthermore, it can set parameters to detect leaf wilting and insect damage for lettuce. By setting specific parameters for each type of plant, the accuracy of detecting abnormalities and diseases is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data for each type of plant into a generating AI and have the generating AI set specific parameters.

[0082] The detection unit estimates the user's emotions and determines the priority of abnormalities and illnesses based on the estimated user emotions. The detection unit estimates the user's emotions using, for example, facial recognition technology. The detection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the detection unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). For example, the detection unit estimates the user's emotions using facial recognition technology. The detection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the detection unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for prioritizing abnormalities and illnesses according to the user's emotions, enabling priority treatment of important abnormalities and illnesses. Emotion estimation is achieved using, 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-described processing in the detection unit may be performed using, for example, AI, or without AI. For example, the detection unit can input the user's facial expression data into a generating AI, allowing the generating AI to perform emotion estimation.

[0083] The detection unit monitors the plant's growing environment in real time and issues an alert if an abnormality occurs. For example, the detection unit can issue an alert if the temperature exceeds a set range. It can also issue an alert if the humidity decreases. Furthermore, it can also issue an alert if the amount of light is insufficient. This allows for real-time monitoring of the plant's growing environment and prompt response by issuing alerts when abnormalities occur. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input temperature data into a generating AI and have the generating AI perform abnormality detection and alert generation.

[0084] The detection unit uses a drone to monitor a wide area when monitoring the growth status of plants. For example, the detection unit periodically photographs a wide area of ​​a field using a drone and detects anomalies using image recognition technology. The detection unit can also measure temperature and humidity with sensors mounted on the drone and detect anomalies. Furthermore, the detection unit can acquire high-resolution images using a drone and perform detailed anomaly detection. For example, the detection unit periodically photographs a wide area of ​​a field using a drone and detects anomalies using image recognition technology. The detection unit can also measure temperature and humidity with sensors mounted on the drone and detect anomalies. Furthermore, the detection unit can acquire high-resolution images using a drone and perform detailed anomaly detection. This enables efficient anomaly detection by monitoring a wide area using a drone. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image data acquired by the drone into a generating AI and have the generating AI perform anomaly detection.

[0085] The diagnostic unit estimates the user's emotions and adjusts the display method of the diagnostic results based on the estimated user emotions. The diagnostic unit estimates the user's emotions using, for example, facial expression recognition technology. The diagnostic unit can also estimate the user's emotions using voice analysis technology. Furthermore, the diagnostic unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). For example, the diagnostic unit estimates the user's emotions using facial expression recognition technology. The diagnostic unit can also estimate the user's emotions using voice analysis technology. Furthermore, the diagnostic unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for a display that is easy for the user to understand by adjusting the display method of the diagnostic results according to the user's emotions. Emotion estimation is achieved using, 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 diagnostic unit may be performed using, for example, AI, or not using AI. For example, the diagnostic unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0086] The diagnostic unit applies different diagnostic algorithms depending on the abnormality or the stage of disease progression. For example, the diagnostic unit applies an algorithm that suggests preventive measures for diseases in their early stages. It can also apply an algorithm that suggests treatment methods for diseases in progress. Furthermore, the diagnostic unit can apply an algorithm that suggests emergency measures for serious diseases. By applying a diagnostic algorithm according to the abnormality or the stage of disease progression, appropriate measures can be suggested. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input disease progression data into a generating AI and have the generating AI execute the application of the diagnostic algorithm.

[0087] The diagnostic unit improves the accuracy of the diagnosis by referring to past diagnostic data. For example, the diagnostic unit identifies diseases with similar symptoms based on past diagnostic data. The diagnostic unit can also analyze past diagnostic data and optimize the diagnostic algorithm. Furthermore, the diagnostic unit can improve the reliability of the diagnostic results by referring to past diagnostic data. For example, the diagnostic unit identifies diseases with similar symptoms based on past diagnostic data. The diagnostic unit can also analyze past diagnostic data and optimize the diagnostic algorithm. Furthermore, the diagnostic unit can improve the reliability of the diagnostic results by referring to past diagnostic data. As a result, the accuracy of the diagnosis is improved by referring to past diagnostic data. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input past diagnostic data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0088] The diagnostic unit estimates the user's emotions and determines the priority of diagnostic results based on the estimated user emotions. The diagnostic unit estimates the user's emotions using, for example, facial expression recognition technology. The diagnostic unit can also estimate the user's emotions using voice analysis technology. Furthermore, the diagnostic unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows important diagnostic results to be displayed preferentially by determining the priority of diagnostic results according to the user's emotions. Emotion estimation is achieved using, 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 diagnostic unit may be performed using, for example, AI, or not using AI. For example, the diagnostic unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0089] The diagnostic unit uses other modal data, such as audio data and temperature data, in conjunction with the diagnostic unit when analyzing abnormalities and disease symptoms. For example, the diagnostic unit can analyze audio data to detect abnormal sounds in plants. It can also analyze temperature data to detect abnormal temperature changes. Furthermore, the diagnostic unit can integrate multiple modal data to perform a more accurate diagnosis. This improves the accuracy of the diagnosis by using other modal data in combination. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input audio data and temperature data into a generating AI and have the generating AI perform abnormality detection.

[0090] The diagnostic unit considers the plant's genetic information when analyzing abnormalities and disease symptoms. For example, the diagnostic unit evaluates susceptibility to specific diseases based on the plant's genetic information. The diagnostic unit can also analyze genetic information and predict the risk of disease progression. Furthermore, the diagnostic unit can propose the optimal treatment method considering the genetic information. This allows for more accurate diagnoses by considering the plant's genetic information. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the plant's genetic information into a generating AI and have the generating AI perform the diagnosis.

[0091] The management department estimates the user's emotions and adjusts the management method based on the estimated user emotions. The management department may, for example, use facial recognition technology to estimate the user's emotions. The management department may also use voice analysis technology to estimate the user's emotions. Furthermore, the management department may also use biometric data (heart rate and skin electrical activity) to estimate the user's emotions. For example, the management department may estimate the user's emotions using facial recognition technology. The management department may also use voice analysis technology to estimate the user's emotions. Furthermore, the management department may also use biometric data (heart rate and skin electrical activity) to estimate the user's emotions. This allows for optimal management for the user by adjusting the management method 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 management department may be performed using AI, for example, or without AI. For example, the management department can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0092] The management department applies different management methods depending on the type of abnormality or disease. For example, the management department applies preventive management methods in the early stages of a disease. The management department can also apply therapeutic management methods to diseases that are progressing. Furthermore, the management department can also apply emergency measures to serious diseases. This allows appropriate measures to be taken by applying management methods according to the type of abnormality or disease. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input disease type data into a generating AI and have the generating AI execute the application of management methods.

[0093] The management department optimizes the management algorithm by referring to past management data. For example, the management department selects the optimal management method based on past management data. The management department can also analyze past management data and optimize the management algorithm. Furthermore, the management department can improve the reliability of management results by referring to past management data. For example, the management department selects the optimal management method based on past management data. The management department can also analyze past management data and optimize the management algorithm. Furthermore, the management department can improve the reliability of management results by referring to past management data. As a result, the accuracy of the management algorithm is improved by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past management data into a generating AI and have the generating AI perform the optimization of the management algorithm.

[0094] The management department estimates the user's emotions and determines management priorities based on the estimated user emotions. The management department may, for example, use facial recognition technology to estimate the user's emotions. It may also use voice analysis technology to estimate the user's emotions. Furthermore, the management department may use biometric data (heart rate and skin electrical activity) to estimate the user's emotions. This allows for prioritizing management according to the user's emotions, enabling the implementation of important management items on a priority basis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, 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 management department may be performed using AI, for example, or without AI. For example, the management department can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0095] The management unit adjusts the plant's growing environment in real time when performing growth management. For example, if the temperature exceeds the set range, the management unit will activate a cooling device. The management unit can also activate a humidifier if the humidity drops. Furthermore, the management unit can activate a lighting device if the light level is insufficient. By adjusting the plant's growing environment in real time, the optimal growing environment can be maintained. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input temperature data into a generating AI and have the generating AI perform the adjustment of the growing environment.

[0096] The management department selects the optimal management method when managing plant growth, taking into account the plant's genetic information. For example, the management department can prioritize the supply of specific nutrients based on the plant's genetic information. The management department can also analyze the genetic information to determine the optimal amount of water and fertilizer to supply. Furthermore, the management department can implement disease prevention measures while taking the genetic information into consideration. For example, the management department can prioritize the supply of specific nutrients based on the plant's genetic information. The management department can also analyze the genetic information to determine the optimal amount of water and fertilizer to supply. Furthermore, the management department can implement disease prevention measures while taking the genetic information into consideration. This allows for more accurate management by considering the plant's genetic information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input the plant's genetic information into a generating AI and have the generating AI select the management method.

[0097] The harvesting unit estimates the user's emotions and adjusts the harvesting method based on the estimated emotions. The harvesting unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for optimal harvesting for the user by adjusting the harvesting method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, 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 harvesting unit may be performed using, for example, AI, or without AI. For example, the harvesting unit can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0098] The harvesting unit applies different harvesting algorithms depending on the plant's growth stage. For example, the harvesting unit recommends manual harvesting in the early stages of growth. It can also apply a semi-automated harvesting method in the mid-stages of growth. Furthermore, it can apply a fully automated harvesting method in the late stages of growth. This allows for appropriate harvesting by applying harvesting algorithms tailored to the plant's growth stage. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without AI. For example, the harvesting unit can input plant growth stage data into a generating AI and have the generating AI execute the application of the harvesting algorithm.

[0099] The harvesting unit optimizes the harvesting algorithm by referring to past harvesting data. For example, the harvesting unit determines the optimal harvesting time based on past harvesting data. The harvesting unit can also analyze past harvesting data and optimize the harvesting algorithm. Furthermore, the harvesting unit can improve the reliability of the harvesting results by referring to past harvesting data. For example, the harvesting unit determines the optimal harvesting time based on past harvesting data. The harvesting unit can also analyze past harvesting data and optimize the harvesting algorithm. Furthermore, the harvesting unit can improve the reliability of the harvesting results by referring to past harvesting data. This improves the accuracy of the harvesting algorithm by referring to past harvesting data. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without AI. For example, the harvesting unit can input past harvesting data into a generating AI and have the generating AI perform the optimization of the harvesting algorithm.

[0100] The harvesting unit estimates the user's emotions and determines the harvesting priority based on the estimated user emotions. The harvesting unit can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows important harvesting items to be prioritized by determining the harvesting priority according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the harvesting unit may be performed using, for example, AI, or without AI. For example, the harvesting unit can input user facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0101] The harvesting unit can improve harvesting efficiency by using drones during harvesting operations. For example, the harvesting unit can use drones to monitor a wide area of ​​the field and identify the target crops. The harvesting unit can also check the condition of the target crops using sensors mounted on the drones. Furthermore, the harvesting unit can automate the harvesting process using drones to improve efficiency. For example, the harvesting unit can use drones to monitor a wide area of ​​the field and identify the target crops. The harvesting unit can also check the condition of the target crops using sensors mounted on the drones. Furthermore, the harvesting unit can automate the harvesting process using drones to improve efficiency. This makes efficient harvesting possible by using drones for harvesting operations. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or not. For example, the harvesting unit can input data acquired by drones into a generating AI and have the generating AI perform tasks to improve the efficiency of the harvesting process.

[0102] The harvesting unit selects the optimal harvesting method when performing harvesting operations, taking into account the genetic information of the plants. For example, the harvesting unit selects a specific harvesting method based on the genetic information of the plants. The harvesting unit can also analyze the genetic information to determine the optimal harvesting time. Furthermore, the harvesting unit can optimize the harvesting method by taking the genetic information into consideration. For example, the harvesting unit selects a specific harvesting method based on the genetic information of the plants. The harvesting unit can also analyze the genetic information to determine the optimal harvesting time. Furthermore, the harvesting unit can optimize the harvesting method by taking the genetic information into consideration. This makes it possible to harvest with higher accuracy by taking the genetic information of the plants into consideration. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without using AI. For example, the harvesting unit can input the genetic information of the plants into a generating AI and have the generating AI select the harvesting method.

[0103] The delivery unit estimates the user's emotions and adjusts the delivery method based on the estimated emotions. The delivery unit can estimate the user's emotions using, for example, facial recognition technology. The delivery unit can also estimate the user's emotions using voice analysis technology. Furthermore, the delivery unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). For example, the delivery unit can estimate the user's emotions using facial recognition technology. The delivery unit can also estimate the user's emotions using voice analysis technology. Furthermore, the delivery unit can also estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for optimal delivery for the user by adjusting the delivery method according to the user's emotions. Emotion estimation is achieved using, 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 delivery unit may be performed using, for example, AI, or not using AI. For example, the delivery department can input user facial expression data into a generative AI and have the AI ​​perform emotion estimation.

[0104] The delivery department applies different delivery algorithms depending on the type of agricultural product. For example, the delivery department applies a rapid delivery algorithm to fresh produce. The delivery department can also apply a standard delivery algorithm to non-perishable foods. Furthermore, the delivery department can apply a temperature-controlled delivery algorithm to foods that require specific temperature control. This enables appropriate delivery by applying a delivery algorithm appropriate to the type of agricultural product. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input agricultural product type data into a generating AI and have the generating AI execute the application of the delivery algorithm.

[0105] The delivery unit optimizes the delivery algorithm by referring to past delivery data. For example, the delivery unit determines the optimal delivery route based on past delivery data. The delivery unit can also analyze past delivery data and optimize the delivery algorithm. Furthermore, the delivery unit can improve the reliability of delivery results by referring to past delivery data. For example, the delivery unit determines the optimal delivery route based on past delivery data. The delivery unit can also analyze past delivery data and optimize the delivery algorithm. Furthermore, the delivery unit can improve the reliability of delivery results by referring to past delivery data. This improves the accuracy of the delivery algorithm by referring to past delivery data. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input past delivery data into a generating AI and have the generating AI perform the optimization of the delivery algorithm.

[0106] The delivery unit estimates the user's emotions and determines delivery priorities based on the estimated emotions. The delivery unit can estimate user emotions using, for example, facial recognition technology. It can also estimate user emotions using voice analysis technology. Furthermore, it can estimate user emotions using biometric data (heart rate and skin electrical activity). This allows for prioritizing deliveries according to user emotions, enabling priority delivery of important items. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, 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 delivery unit may be performed using, for example, AI, or without AI. For example, the delivery department can input user facial expression data into a generative AI and have the AI ​​perform emotion estimation.

[0107] The delivery department can improve the efficiency of deliveries by using drones. For example, the delivery department can use drones to quickly deliver over a wide area. The delivery department can also use sensors mounted on the drones to check the condition of the items to be delivered. Furthermore, the delivery department can use drones to automate deliveries and improve efficiency. For example, the delivery department can use drones to quickly deliver over a wide area. The delivery department can also use sensors mounted on the drones to check the condition of the items to be delivered. Furthermore, the delivery department can use drones to automate deliveries and improve efficiency. This makes efficient deliveries possible by using drones for deliveries. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input data acquired by drones into a generating AI and have the generating AI perform tasks to improve the efficiency of deliveries.

[0108] The delivery department monitors the storage condition of agricultural products in real time when performing delivery operations and selects the optimal delivery method. For example, the delivery department monitors the storage condition of agricultural products in real time and selects the optimal delivery route. The delivery department can also select a delivery method that requires temperature control depending on the storage condition. Furthermore, the delivery department can also select a rapid delivery method considering the storage condition. For example, the delivery department monitors the storage condition of agricultural products in real time and selects the optimal delivery route. The delivery department can also select a delivery method that requires temperature control depending on the storage condition. Furthermore, the delivery department can also select a rapid delivery method considering the storage condition. This allows the optimal delivery method to be selected by monitoring the storage condition of agricultural products in real time. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input agricultural product storage condition data into a generating AI and have the generating AI select the optimal delivery method.

[0109] The distribution department simulates the supply and demand of agricultural products when carrying out distribution operations, and supplies agricultural products to the optimal time and region. For example, the distribution department conducts supply and demand simulations to determine the optimal distribution time. The distribution department can also select the optimal distribution area based on the supply and demand simulations. Furthermore, the distribution department can use supply and demand simulations to formulate efficient distribution plans. For example, the distribution department conducts supply and demand simulations to determine the optimal distribution time. The distribution department can also select the optimal distribution area based on the supply and demand simulations. Furthermore, the distribution department can use supply and demand simulations to formulate efficient distribution plans. In this way, by simulating the supply and demand of agricultural products, they can be supplied to the optimal time and region. Some or all of the above processes in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input supply and demand data into a generating AI and have the generating AI execute supply and demand simulations.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The detection unit can also use drones to monitor a wide area when monitoring the growth status of plants. For example, it can periodically photograph a wide field using a drone and detect anomalies using image recognition technology. It can also measure temperature and humidity with sensors mounted on the drone and detect anomalies. Furthermore, it can acquire high-resolution images using a drone and perform detailed anomaly detection. This enables efficient anomaly detection by monitoring a wide area using a drone. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input image data acquired by a drone into a generating AI and have the generating AI perform anomaly detection.

[0112] The management unit can also estimate the user's emotions and adjust management methods based on the estimated emotions. For example, it can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for optimal management for the user by adjusting management methods 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 management unit may be performed using AI, or not using AI. For example, the management unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The diagnostic unit can also apply different diagnostic algorithms depending on the abnormality or the stage of disease progression. For example, an algorithm that suggests preventive measures can be applied to diseases in their early stages. An algorithm that suggests treatment methods can be applied to diseases in progress. Furthermore, an algorithm that suggests emergency measures can be applied to serious diseases. In this way, appropriate measures can be suggested by applying a diagnostic algorithm that is appropriate to the abnormality or the stage of disease progression. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input disease progression data into a generating AI and have the generating AI execute the application of the diagnostic algorithm.

[0114] The harvesting unit can also estimate the user's emotions and adjust the harvesting method based on the estimated emotions. For example, it can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for optimal harvesting for the user by adjusting the harvesting method 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 harvesting unit may be performed using AI, or not using AI. For example, the harvesting unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0115] The delivery department can also monitor the storage condition of agricultural products in real time and select the optimal delivery method. For example, it can monitor the storage condition of agricultural products in real time and select the optimal delivery route. It can also select a delivery method that requires temperature control depending on the storage condition. Furthermore, it can select a rapid delivery method considering the storage condition. In this way, the optimal delivery method can be selected by monitoring the storage condition of agricultural products in real time. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input agricultural product storage condition data into a generating AI and have the generating AI select the optimal delivery method.

[0116] The detection unit can also estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, it can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biometric data (heart rate or skin electrical activity). This reduces the burden on 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. 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0117] The management department can also optimize management algorithms by referring to past management data. For example, it can select the optimal management method based on past management data. It can also analyze past management data and optimize management algorithms. Furthermore, it can improve the reliability of management results by referring to past management data. In this way, the accuracy of the management algorithm is improved by referring to past management data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past management data into a generating AI and have the generating AI perform the optimization of the management algorithm.

[0118] The diagnostic unit can also estimate the user's emotions and adjust the display method of the diagnostic results based on the estimated user emotions. For example, it can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions using biometric data (heart rate and skin electrical activity). This allows for a display that is easy for the user to understand by adjusting the display method of the diagnostic results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0119] The harvesting unit can also optimize its harvesting algorithm by referring to past harvesting data. For example, it can determine the optimal harvesting time based on past harvesting data. It can also analyze past harvesting data and optimize the harvesting algorithm. Furthermore, it can improve the reliability of harvesting results by referring to past harvesting data. This improves the accuracy of the harvesting algorithm by referring to past harvesting data. Some or all of the above processes in the harvesting unit may be performed using AI, for example, or without AI. For example, the harvesting unit can input past harvesting data into a generating AI and have the generating AI perform the optimization of the harvesting algorithm.

[0120] The delivery unit can also estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, it can use facial recognition technology to estimate the user's emotions. It can also use voice analysis technology to estimate the user's emotions. Furthermore, it can use biometric data (heart rate and skin electrical activity) to estimate the user's emotions. This allows for optimal delivery for the user by adjusting the delivery method 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 delivery unit may be performed using AI, or not using AI. For example, the delivery unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The detection unit monitors the growth status of plants and detects abnormalities and disease symptoms. For example, it uses image recognition technology to analyze the color and shape of plant leaves to detect abnormalities and signs of disease. It can also use sensors to monitor soil moisture and nutrient status and detect abnormalities. Furthermore, it can use drones to monitor wide areas of fields and detect abnormalities. Step 2: The diagnostic unit analyzes the abnormalities and disease symptoms detected by the detection unit and performs diagnosis and prediction in real time. For example, it uses a multimodal LLM to analyze the abnormalities and disease symptoms and outputs a diagnostic result. It can also improve the accuracy of the diagnosis by referring to past diagnostic data. Furthermore, different diagnostic algorithms can be applied depending on the progression of the abnormality or disease. Step 3: The management unit performs growth management based on the diagnostic results obtained by the diagnostic unit. For example, it monitors soil moisture and nutrient status using sensors and controls the supply of optimal water and fertilizer. It can also apply different management methods depending on the stage of plant growth. Furthermore, it can optimize the management algorithm by referring to past management data. Step 4: The harvesting unit harvests the plants managed by the management unit. For example, harvesting is performed using AI-controlled agricultural robots. It is also possible to optimize the harvesting time and method. Furthermore, the harvesting algorithm can be optimized by referring to past harvesting data. Step 5: The distribution department distributes the agricultural products harvested by the harvesting department based on supply and demand. For example, AI simulates the supply and demand of agricultural products and supplies them to the optimal time and region. It can also monitor the storage condition of agricultural products in real time and select the optimal distribution method. Furthermore, it can optimize the distribution algorithm by referring to past distribution data.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the detection unit, diagnostic unit, management unit, harvesting unit, and delivery unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the detection unit monitors the growth status of plants using the camera 42 and sensors of the smart device 14 and detects abnormalities and disease symptoms. The diagnostic unit analyzes abnormalities and disease symptoms using a multimodal LLM by the identification processing unit 290 of the data processing unit 12 and outputs the diagnostic results. The management unit monitors soil moisture and nutrient status by the control unit 46A of the smart device 14 and controls the supply of optimal water and fertilizer. The harvesting unit performs harvesting work using an AI-controlled agricultural robot and optimizes the harvesting time and method. The delivery unit simulates the supply and demand of agricultural products by the identification processing unit 290 of the data processing unit 12 and supplies agricultural products at the optimal time and location. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the detection unit, diagnostic unit, management unit, harvesting unit, and delivery unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit monitors the growth status of plants using the camera 42 and sensors of the smart glasses 214 and detects abnormalities and disease symptoms. The diagnostic unit analyzes abnormalities and disease symptoms using a multimodal LLM by the identification processing unit 290 of the data processing unit 12 and outputs the diagnostic results. The management unit monitors soil moisture and nutrient status by the control unit 46A of the smart glasses 214 and controls the supply of optimal water and fertilizer. The harvesting unit performs harvesting work using an AI-controlled agricultural robot and optimizes the harvesting time and method. The delivery unit simulates the supply and demand of agricultural products by the identification processing unit 290 of the data processing unit 12 and supplies agricultural products at the optimal time and location. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the detection unit, diagnostic unit, management unit, harvesting unit, and delivery unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit monitors the growth status of plants using the camera 42 and sensors of the headset terminal 314 and detects abnormalities and disease symptoms. The diagnostic unit analyzes abnormalities and disease symptoms using a multimodal LLM by the identification processing unit 290 of the data processing unit 12 and outputs the diagnostic results. The management unit monitors soil moisture and nutrient status by the control unit 46A of the headset terminal 314 and controls the supply of optimal water and fertilizer. The harvesting unit performs harvesting work using an AI-controlled agricultural robot and optimizes the harvesting time and method. The delivery unit simulates the supply and demand of agricultural products by the identification processing unit 290 of the data processing unit 12 and supplies agricultural products at the optimal time and location. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the detection unit, diagnostic unit, management unit, harvesting unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the detection unit monitors the growth status of plants using the camera 42 and sensors of the robot 414 and detects abnormalities and disease symptoms. The diagnostic unit analyzes abnormalities and disease symptoms using a multimodal LLM by the identification processing unit 290 of the data processing unit 12 and outputs the diagnostic results. The management unit monitors soil moisture and nutrient status by the control unit 46A of the robot 414 and controls the supply of optimal water and fertilizer. The harvesting unit performs harvesting work using an AI-controlled agricultural robot and optimizes the harvesting time and method. The delivery unit simulates the supply and demand of agricultural products by the identification processing unit 290 of the data processing unit 12 and supplies agricultural products at the optimal time and location. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A detection unit that monitors the growth status of plants and detects abnormalities or disease symptoms, A diagnostic unit analyzes the abnormalities and disease symptoms detected by the aforementioned detection unit and performs diagnosis and prediction in real time. A management unit that performs growth management based on the diagnostic results obtained by the aforementioned diagnostic unit, A harvesting unit that harvests plants managed by the aforementioned management unit, The system includes a distribution unit that delivers agricultural products harvested by the harvesting unit based on supply and demand. A system characterized by the following features. (Note 2) The aforementioned management department, It is equipped with a monitoring unit that monitors soil moisture and nutrient status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned management department, It is equipped with a supply unit that controls the optimal supply of water and fertilizer. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned harvesting section is It is equipped with an optimization unit that optimizes the harvesting time and method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned delivery department, We simulate the supply and demand of agricultural products and provide a marketplace that supplies agricultural products to the optimal time and region. The system described in Appendix 1, characterized by the features described herein. (Note 6) The detection unit is 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 detection unit is Apply different monitoring algorithms depending on the plant's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is Set specific parameters to detect different abnormalities and disease symptoms for each plant species. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is It estimates the user's emotions and prioritizes abnormalities and illnesses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is It monitors the plant's growing environment in real time and issues an alert if an abnormality occurs. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is When monitoring the growth status of plants, drones are used to conduct wide-area monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts how the diagnostic results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned diagnostic unit, Apply different diagnostic algorithms depending on the abnormality or the stage of disease progression. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned diagnostic unit, Improve diagnostic accuracy by referring to past diagnostic data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned diagnostic unit, It estimates the user's emotions and prioritizes the diagnostic results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned diagnostic unit, When analyzing symptoms of abnormalities or diseases, other modal data such as audio data and temperature data are used in conjunction. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned diagnostic unit, When analyzing the symptoms of abnormalities or diseases, the plant's genetic information is taken into consideration during diagnosis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, We estimate the user's emotions and adjust management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, Different management methods are applied depending on the type of abnormality or disease. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, Optimize the management algorithm by referring to past management data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When managing plant growth, adjust the plant's growing environment in real time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, When managing plant growth, the optimal management method is selected by considering the plant's genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned harvesting section is It estimates the user's emotions and adjusts the harvesting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned harvesting section is Apply different harvesting algorithms depending on the plant's growth stage. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned harvesting section is Optimize the harvesting algorithm by referring to past harvesting data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned harvesting section is It estimates user sentiment and determines harvest priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned harvesting section is Using drones to improve harvesting efficiency during harvesting operations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned harvesting section is When harvesting, the optimal harvesting method is selected by considering the genetic information of the plants. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned delivery department, Apply different delivery algorithms depending on the type of agricultural product. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned delivery department, Optimize the delivery algorithm by referring to past delivery data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned delivery department, The system estimates the user's emotions and determines delivery priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned delivery department, Using drones to improve delivery efficiency during delivery operations. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned delivery department, During delivery operations, the storage condition of agricultural products is monitored in real time, and the optimal delivery method is selected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned delivery department, When carrying out delivery operations, we simulate the supply and demand of agricultural products and supply them to the optimal time and region. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. A detection unit that monitors the growth status of plants and detects abnormalities or disease symptoms, A diagnostic unit analyzes the abnormalities and disease symptoms detected by the aforementioned detection unit and performs diagnosis and prediction in real time. A management unit that performs growth management based on the diagnostic results obtained by the aforementioned diagnostic unit, A harvesting unit that harvests plants managed by the aforementioned management unit, The system includes a distribution unit that delivers agricultural products harvested by the harvesting unit based on supply and demand. A system characterized by the following features.

2. The aforementioned management department, It is equipped with a monitoring unit that monitors soil moisture and nutrient status. The system according to feature 1.

3. The aforementioned management department, It is equipped with a supply unit that controls the optimal supply of water and fertilizer. The system according to feature 1.

4. The aforementioned harvesting section is It is equipped with an optimization unit that optimizes the harvesting time and method. The system according to feature 1.

5. The aforementioned delivery department, We simulate the supply and demand of agricultural products and provide a marketplace that supplies agricultural products to the optimal time and region. The system according to feature 1.

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

7. The detection unit is Apply different monitoring algorithms depending on the plant's growth stage. The system according to feature 1.

8. The detection unit is Set specific parameters to detect different abnormalities and disease symptoms for each plant species. The system according to feature 1.

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