System

Abstract

An object of a system according to an embodiment is to make it possible to determine appropriate work in cultivation of fruit trees without expert knowledge.SOLUTION: A system includes an input part, an analysis part, a display part, a collection part, an identification part, and a cooperation part. The inputter captures an image or a moving image of a state of a fruit tree and inputs the image or the moving image to the generation AI. The analysis unit analyzes the state of the fruit tree input by the input unit, and specifies branches to be pruned and branches to be left. The display unit displays the information specified by the analysis unit on the goggles of the operator. The collection unit collects information on weather, crop conditions, disinfection, and additional fertilization. The specification unit specifies the work to be done now based on the information collected by the collection unit. The cooperation unit cooperates with a newly created cultivation technique.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques require specialized knowledge to determine the appropriate work to be done in fruit cultivation, which is a barrier to young people entering the farming industry.

[0005] The system according to the embodiment aims to enable even those without specialized knowledge to determine appropriate work in fruit tree cultivation. [Means for solving the problem]

[0006] The system according to the embodiment comprises an input unit, an analysis unit, a display unit, a collection unit, an identification unit, and a linking unit. The input unit takes images and videos of the condition of the fruit trees and inputs them into the generation AI. The analysis unit analyzes the condition of the fruit trees input by the input unit and identifies branches that should be pruned and branches that should be left. The display unit displays the information identified by the analysis unit on the worker's goggles. The collection unit collects information on weather, crop status, disinfection, and top dressing. The identification unit identifies the work that needs to be done now based on the information collected by the collection unit. The linking unit links with newly developed cultivation techniques. [Effects of the Invention]

[0007] The system according to the embodiment can enable a person without specialized knowledge to determine the appropriate work to be done in cultivating fruit trees. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A cultivation support system according to an embodiment of the present invention automatically analyzes the condition of fruit trees and provides optimal work instructions. The cultivation support system captures images and videos of the fruit tree's condition, analyzes them using a generation AI, and identifies branches that need pruning and branches that should be left. The cultivation support system also collects information on weather, crop conditions, disinfection, and top dressing to identify current work. Furthermore, the cultivation support system integrates with newly developed cultivation techniques to provide the latest information. For example, the cultivation support system captures images of the fruit tree's condition, and the generation AI analyzes them. The generation AI analyzes images of the fruit tree to identify branches that show signs of disease or are not growing properly. The cultivation support system then displays the information identified by the generation AI on the worker's goggles. For example, the goggles display branches that need pruning, allowing the worker to instantly determine which branches to leave. Furthermore, the cultivation support system analyzes weather data and crop growth status to determine the optimal timing for work. For example, it determines whether disinfection should be performed before rain falls. This significantly reduces the barriers to cultivation skills for young people entering the agricultural industry. This allows the cultivation support system to automatically analyze the condition of fruit trees and provide optimal work content. For example, even young people with little experience can work efficiently, which leads to stable harvest volume and quality. Furthermore, by providing optimal work timing according to the weather and crop conditions, it is possible to provide an environment where young people can start farming with peace of mind.

[0029] A cultivation support system according to an embodiment includes an input unit, an analysis unit, a display unit, a collection unit, an identification unit, and a linking unit. The input unit captures images and videos of the condition of a fruit tree and inputs them into the generation AI. For example, the input unit acquires images from a long distance to capture an overall view of the fruit tree. The input unit can also acquire images from a close distance to analyze the details of the fruit tree in detail. The input unit can also acquire images from multiple angles to analyze different aspects of the fruit tree. The analysis unit uses the generation AI to analyze the condition of the fruit tree input by the input unit and identify branches that should be pruned and branches that should be left. For example, the generation AI analyzes images of the fruit tree using deep learning to identify branches that show signs of disease or branches that are not growing properly. The analysis unit can also analyze the condition of the fruit tree using a neural network. The analysis unit can also use the generation AI to analyze the condition of the fruit tree and identify branches that should be pruned and branches that should be left. The display unit displays the information identified by the analysis unit on the operator's goggles. For example, the display unit can display the branches that need to be pruned on the goggles, allowing the worker to instantly determine which branches to leave. The display unit can also display information identified by the generation AI on the worker's goggles. The display unit can also dynamically change the information displayed according to the worker's line of sight. The collection unit collects information on weather, crop conditions, disinfection, and top dressing. For example, the collection unit collects weather information such as temperature, humidity, and precipitation. The collection unit can also collect crop conditions such as the growth stage of the crop and the presence or absence of pests and diseases. The collection unit can also collect disinfection information such as the pesticides to be used and the frequency of disinfection. The identification unit identifies the current work to be done based on the information collected by the collection unit. For example, the identification unit analyzes the information collected using the generation AI and identifies the optimal work content. The identification unit can also analyze weather data to determine whether disinfection should be performed before rain falls. The identification unit can also analyze the crop growth status and determine the timing of top dressing. The collaboration unit connects with newly developed cultivation techniques to provide the latest information. For example, the collaboration unit can collect information on new pruning techniques and pest control measures and provide it to workers. The collaboration unit can also collaborate with newly developed cultivation techniques using generative AI.The collaboration unit can also improve the collaboration method by reflecting user feedback. This allows the cultivation support system according to the embodiment to automatically analyze the condition of fruit trees and provide optimal work content. For example, even young people with little experience can work efficiently, which leads to stable harvest volume and quality. Furthermore, by providing optimal work timing according to the weather and crop conditions, an environment can be created in which young people can start farming with peace of mind.

[0030] The analysis unit can analyze the condition of the fruit tree using the generation AI and identify branches that should be pruned and branches that should be left. The analysis unit, for example, analyzes the condition of the fruit tree using the generation AI. For example, the generation AI can analyze images of the fruit tree using deep learning to identify branches that show signs of disease or branches that are not growing properly. The analysis unit can also analyze the condition of the fruit tree using a neural network. The analysis unit can also analyze the condition of the fruit tree using the generation AI to identify branches that should be pruned and branches that should be left. This allows the generation AI to accurately analyze the condition of the fruit tree and perform optimal pruning work. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data of the fruit tree into the generation AI and have the generation AI analyze the condition of the fruit tree.

[0031] The display unit can display the information identified by the generation AI on the worker's goggles. For example, the display unit displays the information identified by the generation AI on the worker's goggles. For example, the display unit displays branches to be pruned on the goggles, allowing the worker to instantly determine which branches to leave. The display unit can also dynamically change the information displayed in accordance with the worker's line of sight. The display unit can also update the information displayed in real time in accordance with the worker's work status. This allows the worker to check the necessary information in real time and work efficiently. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the information identified by the generation AI and cause the generation AI to execute the information to be displayed on the worker's goggles.

[0032] The collection unit can collect information on weather, crop conditions, disinfection, and top dressing. The collection unit collects weather information such as temperature, humidity, and precipitation. For example, the collection unit collects weather data from multiple weather services. The collection unit can also collect crop conditions such as the growth stage of the crop and the presence or absence of pests and diseases. For example, the collection unit collects crop condition data from different sensors. The collection unit can also collect disinfection information such as the chemicals used and the frequency of disinfection. For example, the collection unit collects disinfection information from multiple agricultural databases. The collection unit can also collect top dressing information such as the type of fertilizer used and the timing of fertilization. By collecting the necessary information, the optimal work timing can be identified. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input weather data into the generation AI and cause the generation AI to collect weather information.

[0033] The identification unit can identify the work that needs to be done now based on the information collected by the generation AI. The identification unit, for example, analyzes the information collected using the generation AI and identifies the optimal work content. For example, the identification unit analyzes weather data and determines whether disinfection should be performed before rain falls. The identification unit can also analyze the growth status of crops and identify the timing of top dressing. The identification unit can also identify the work that needs to be done now based on the collected information using the generation AI. In this way, the generation AI can be used to identify the optimal work content and perform the work efficiently. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the collected information into the generation AI and have the generation AI identify the work that needs to be done now.

[0034] The collaboration unit can collaborate with newly developed cultivation techniques and provide the latest information. For example, the collaboration unit collects information on new pruning techniques and pest control measures and provides it to workers. For example, the collaboration unit collaborates with newly developed cultivation techniques using the generation AI. The collaboration unit can also improve the collaboration method by reflecting user feedback. For example, the collaboration unit adjusts the collaboration method based on feedback provided by the user. The collaboration unit can also improve the collaboration accuracy by referring to past collaboration data. For example, the collaboration unit introduces a new pruning technique based on past collaboration data. This makes it possible to improve the efficiency and quality of work by utilizing the latest cultivation techniques. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input information on newly developed cultivation techniques into the generation AI and have the generation AI provide the latest information.

[0035] When photographing the condition of a fruit tree, the input unit acquires multiple images and videos from different angles and distances, thereby improving analysis accuracy. For example, the input unit acquires images from a long distance to capture an overall image of the fruit tree. For example, the input unit acquires images from a close distance to analyze the details of the fruit tree in detail. The input unit can also acquire images from multiple angles to analyze different aspects of the fruit tree. This allows for improved analysis accuracy by acquiring images and videos from different angles and distances. Some or all of the above-mentioned processing in the input unit may be performed using, or without, AI. For example, the input unit can input image data acquired from different angles and distances to the generation AI, causing the generation AI to improve analysis accuracy.

[0036] The input unit can take specific lighting conditions into consideration when photographing the condition of fruit trees. For example, the input unit takes photos during the time when morning sunlight hits the fruit trees to capture natural colors. For example, the input unit can take photos during the time when evening sunlight hits the fruit trees to analyze the shadows. The input unit can also take photos on a cloudy day to capture the condition of the fruit trees under uniform lighting conditions. This allows for more accurate photographing of the condition of the fruit trees by taking specific lighting conditions into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input image data photographed under specific lighting conditions to the generation AI and have the generation AI analyze the condition of the fruit trees.

[0037] When photographing the condition of a fruit tree, the input unit can customize the photographing method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the input unit focuses on photographing the parts that are growing quickly. For example, in the case of a mature fruit tree, the input unit photographs the condition of the fruit in detail. Furthermore, in the case of a fruit tree showing signs of disease, the input unit can also focus on photographing the diseased area. In this way, by customizing the photographing method according to the type and growth stage of the fruit tree, more appropriate data can be obtained. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input photographing data according to the type and growth stage of the fruit tree to the generation AI, and cause the generation AI to customize the photographing method.

[0038] The input unit can simultaneously acquire information about the surrounding environment when photographing the condition of the fruit tree. For example, the input unit acquires data using a soil humidity sensor when photographing the condition of the fruit tree. For example, the input unit acquires ambient temperature data using a temperature sensor when photographing the condition of the fruit tree. The input unit can also measure wind strength using an anemometer when photographing the condition of the fruit tree. This allows for more accurate analysis by simultaneously acquiring ambient environmental information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input ambient environmental information to the generation AI and cause the generation AI to analyze the condition of the fruit tree.

[0039] When photographing the condition of fruit trees, the input unit can automatically photograph a wide range of fruit trees using a drone or a robot. The input unit, for example, uses a drone to take aerial photographs of the entire orchard to check the condition of the wide range of fruit trees. For example, the input unit uses a robot to photograph the fruit trees in detail from the base to the top. The input unit can also combine a drone and a robot to simultaneously photograph the entire fruit tree and its details. This makes it possible to efficiently photograph a wide range of fruit trees using a drone or a robot. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input image data acquired by a drone or a robot into the generation AI and have the generation AI analyze the condition of the wide range of fruit trees.

[0040] When photographing the condition of fruit trees, the input unit can suggest the optimal photographing method by referring to the user's past photographing history. The input unit, for example, analyzes images taken by the user in the past and suggests the optimal photographing angle. For example, the input unit can suggest the optimal time of day for photographing based on the user's past photographing history. The input unit can also suggest the optimal equipment by referring to photographing equipment used by the user in the past. In this way, the optimal photographing method can be suggested by referring to the user's past photographing history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past photographing history into the generation AI and cause the generation AI to suggest the optimal photographing method.

[0041] When analyzing the condition of a fruit tree, the analysis unit can improve the accuracy of the analysis by referring to past analysis data. The analysis unit, for example, detects signs of disease early based on past analysis data. For example, the analysis unit can identify growth delays based on past analysis data. The analysis unit can also predict the optimal pruning timing based on past analysis data. In this way, by referring to past analysis data, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0042] When analyzing the condition of fruit trees, the analysis unit can improve the accuracy of the analysis by combining different analysis algorithms. For example, the analysis unit can combine an image analysis algorithm with a machine learning algorithm to detect signs of disease. For example, the analysis unit can combine an image analysis algorithm with a statistical analysis algorithm to identify growth delays. The analysis unit can also combine an image analysis algorithm with a data mining algorithm to predict the optimal pruning time. This allows the combination of different analysis algorithms to improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and cause the generation AI to execute the processing to improve the analysis accuracy.

[0043] When analyzing the condition of a fruit tree, the analysis unit can customize the analysis method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the analysis unit focuses on analyzing the parts that are growing quickly. For example, in the case of a mature fruit tree, the analysis unit analyzes the condition of the fruit in detail. Furthermore, in the case of a fruit tree that shows signs of disease, the analysis unit can also focus on analyzing the diseased parts. This enables more appropriate analysis by customizing the analysis method according to the type and growth stage of the fruit tree. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input analysis data according to the type and growth stage of the fruit tree into the generation AI and have the generation AI customize the analysis method.

[0044] When analyzing the condition of a fruit tree, the analysis unit can also take into account environmental information surrounding the fruit tree. The analysis unit, for example, analyzes the moisture status of the fruit tree based on soil humidity data. For example, the analysis unit analyzes the growth status of the fruit tree based on ambient temperature data. The analysis unit can also analyze the health status of the fruit tree based on wind speed data. This allows for more accurate analysis by taking ambient environmental information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ambient environmental information into the generation AI and cause the generation AI to analyze the condition of the fruit tree.

[0045] When analyzing the condition of fruit trees, the analysis unit can improve the accuracy of the analysis by comparing the conditions of different fruit trees. For example, the analysis unit can compare healthy fruit trees with diseased fruit trees to identify signs of disease. For example, the analysis unit can compare fast-growing fruit trees with slow-growing fruit trees to identify growth delays. The analysis unit can also compare different varieties of fruit trees to identify optimal cultivation methods. This allows the analysis accuracy to be improved by comparing the conditions of different fruit trees. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input condition data of different fruit trees into the generation AI and cause the generation AI to improve the analysis accuracy.

[0046] When analyzing the condition of fruit trees, the analysis unit can customize the analysis method by referring to the user's past analysis results. The analysis unit, for example, proposes an optimal analysis method based on the user's past analysis results. For example, the analysis unit may focus its analysis on specific problem areas based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. This makes it possible to optimize the analysis method by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's past analysis results into the generation AI and have the generation AI customize the analysis method.

[0047] The display unit can dynamically change the information displayed in accordance with the worker's line of sight. For example, when the worker is looking at a specific branch, the display unit displays information about that branch. For example, when the worker is looking at the entire fruit tree, the display unit displays the overall health status. Furthermore, when the worker is looking at a specific part, the display unit can also display detailed information about that part. This enables efficient work by dynamically changing information in accordance with the worker's line of sight. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's line of sight data to the generation AI and cause the generation AI to dynamically change the displayed information.

[0048] The display unit can update the displayed information in real time according to the worker's work status. For example, when the worker is pruning, the display unit updates information about branches to be pruned in real time. For example, when the worker is disinfecting, the display unit updates information about areas to be disinfected in real time. Furthermore, when the worker is applying top dressing, the display unit can also update information about areas to be fertilized in real time. This enables efficient work by updating information in real time according to the worker's work status. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data about the worker's work status to the generation AI and cause the generation AI to perform real-time updates of the displayed information.

[0049] The display unit can customize the information to be displayed by referring to the worker's past work history. The display unit, for example, suggests an optimal pruning method based on the worker's past pruning work history. For example, the display unit suggests an optimal disinfection method based on the worker's past disinfection work history. The display unit can also suggest an optimal top dressing method based on the worker's past top dressing work history. In this way, optimal information can be provided by referring to the worker's past work history. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's past work history into the generation AI and have the generation AI customize the display information.

[0050] The display unit can provide the displayed information to the worker using senses other than sight. For example, if the worker has difficulty visually confirming the information, the display unit provides the information by voice. For example, if the worker is wearing gloves, the display unit provides the information by vibration. Furthermore, if the worker uses both sight and hearing at the same time, the display unit can provide a combination of voice and visual information. This allows the worker to receive information more effectively by using senses other than sight. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input sensory data other than sight into the generation AI and cause the generation AI to execute the information provision method.

[0051] The display unit can simultaneously display the information on the worker's device. For example, if the worker is using a smartphone, the display unit simultaneously displays the information on the goggles and the smartphone. For example, if the worker is using a tablet, the display unit simultaneously displays the information on the goggles and the tablet. Furthermore, if the worker is using multiple devices, the display unit can simultaneously display the information on all devices. This allows the worker to efficiently check the information by displaying the information on multiple devices simultaneously. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's device data into the generation AI and have the generation AI execute the simultaneous display of information.

[0052] The display unit can customize the information to be displayed by reflecting the worker's feedback. The display unit, for example, adjusts the display content based on the feedback provided by the worker. For example, the display unit improves the display method based on the worker's feedback. The display unit can also change the priority of information based on the worker's feedback. This makes it possible to provide optimal information by reflecting the worker's feedback. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's feedback data into a generation AI and have the generation AI customize the display information.

[0053] The collection unit can improve accuracy by collecting information on weather, crop conditions, disinfection, and top dressing from different data sources. For example, the collection unit collects weather data from multiple weather services. For example, the collection unit collects crop condition data from different sensors. The collection unit can also collect disinfection and top dressing information from multiple agricultural databases. This allows for improved accuracy by collecting information from different data sources. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input information from different data sources into the generation AI and cause the generation AI to improve the accuracy of information collection.

[0054] The collection unit can compare the collected information with past data to detect abnormal values. For example, the collection unit can compare it with past weather data to detect abnormal weather conditions. For example, the collection unit can compare it with past crop growth data to detect abnormal growth patterns. The collection unit can also compare it with past disinfection and top dressing data to detect abnormal usage amounts. This makes it possible to detect abnormal values ​​by comparing it with past data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past data into the generation AI and have the generation AI detect abnormal values.

[0055] The collection unit can customize the information to be collected according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the collection unit focuses on collecting information about growth. For example, in the case of a mature fruit tree, the collection unit collects information about the condition of the fruit. In addition, in the case of a fruit tree showing signs of disease, the collection unit can also collect information about the diseased area. In this way, by collecting information according to the type and growth stage of the fruit tree, more appropriate data can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information according to the type and growth stage of the fruit tree into the generation AI and have the generation AI customize the information.

[0056] The collection unit can collect information on weather, crop conditions, disinfection, and top dressing by comparing it with data from different regions. For example, the collection unit compares weather data from different regions to identify the optimal work timing. For example, the collection unit compares crop growth data from different regions to identify the optimal cultivation method. The collection unit can also compare disinfection and top dressing data from different regions to identify the optimal amount to use. This makes it possible to collect optimal information by comparing it with data from different regions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data from different regions into the generation AI and cause the generation AI to collect information.

[0057] The collection unit can customize the information to be collected by referring to the user's past collection history. The collection unit, for example, suggests an optimal collection method based on weather data collected by the user in the past. For example, the collection unit suggests an optimal collection method based on the user's past crop growth data. The collection unit can also suggest an optimal collection method based on the user's past disinfection and top dressing data. In this way, optimal information can be collected by referring to the user's past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history into the generation AI and have the generation AI customize the information.

[0058] The collection unit can improve the information collection method by reflecting user feedback. The collection unit adjusts the collection method, for example, based on feedback provided by the user. For example, the collection unit changes the type of information to collect based on user feedback. The collection unit can also change the priority of the information to collect based on user feedback. This makes it possible to provide an optimal information collection method by reflecting user feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input user feedback data into the generation AI and cause the generation AI to improve the collection method.

[0059] The identification unit can improve accuracy when identifying the work that needs to be done now by referring to past work data. For example, the identification unit identifies the optimal timing for work based on past work data. For example, the identification unit identifies the optimal work method based on past work data. The identification unit can also identify the optimal work content based on past work data. In this way, by referring to past work data, the identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past work data into a generation AI and cause the generation AI to identify the work content.

[0060] The identification unit can improve accuracy when identifying the current task by combining different identification algorithms. The identification unit, for example, combines a machine learning algorithm and a statistical analysis algorithm to identify the optimal task content. For example, the identification unit combines a data mining algorithm and an image analysis algorithm to identify the optimal task content. The identification unit can also combine different algorithms to identify the optimal timing for the task. In this way, by combining different identification algorithms, identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input different identification algorithms into the generation AI and cause the generation AI to identify the task content.

[0061] When identifying the work that needs to be done, the identification unit can customize the identification method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the identification unit prioritizes identifying work related to growth. For example, in the case of a mature fruit tree, the identification unit identifies work related to the condition of the fruit. In addition, in the case of a fruit tree showing signs of disease, the identification unit can also identify work related to the diseased area. In this way, by customizing the identification method according to the type and growth stage of the fruit tree, more appropriate work content can be identified. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data according to the type and growth stage of the fruit tree into the generation AI and have the generation AI identify the work content.

[0062] When identifying the work that needs to be done, the identification unit can also take into consideration environmental information surrounding the fruit tree. The identification unit, for example, identifies the optimal timing for the work based on soil humidity data. For example, the identification unit can identify the optimal work content based on ambient temperature data. The identification unit can also identify the optimal work method based on wind speed data. This allows for more accurate identification of work content by taking ambient environmental information into consideration. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input ambient environmental information into the generation AI and have the generation AI identify the work content.

[0063] When identifying the work that needs to be done, the identification unit can improve the identification accuracy by comparing the conditions of different fruit trees. For example, the identification unit compares healthy fruit trees with diseased fruit trees to identify the optimal work content. For example, the identification unit compares fast-growing fruit trees with slow-growing fruit trees to identify the optimal timing for work. The identification unit can also compare different varieties of fruit trees to identify the optimal cultivation method. In this way, by comparing the conditions of different fruit trees, the identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input condition data of different fruit trees into the generation AI and cause the generation AI to identify the work content.

[0064] When identifying the work that needs to be done now, the identification unit can customize the identification method by referring to the user's past work results. The identification unit, for example, suggests an optimal work method based on the user's past work results. For example, the identification unit may focus on identifying specific problem areas based on the user's past work results. The identification unit can also improve the identification accuracy based on the user's past work results. This makes it possible to optimize the identification method by referring to the user's past work results. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input the user's past work results into the generation AI and have the generation AI identify the work content.

[0065] When linking with a newly created cultivation technique, the linking unit can improve accuracy by referring to past linking data. For example, the linking unit introduces a new pruning technique based on past linking data. For example, the linking unit introduces a new pest control measure based on past linking data. The linking unit can also introduce a new top dressing technique based on past linking data. In this way, linking accuracy can be improved by referring to past linking data. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input past linking data into the generation AI and cause the generation AI to improve linking accuracy.

[0066] When linking with a newly developed cultivation technique, the linking unit can improve the linking accuracy by combining different techniques. For example, the linking unit links a new pruning technique with an existing technique. For example, the linking unit links a new pest control technique with an existing technique. The linking unit can also link a new top dressing technique with an existing technique. This allows the linking accuracy to be improved by combining different techniques. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input different techniques into the generation AI and cause the generation AI to improve the linking accuracy.

[0067] When linking with newly developed cultivation techniques, the linking unit can customize the linking method according to the type and growth stage of the fruit tree. For example, in the case of young fruit trees, the linking unit introduces a new growth promotion technique. For example, in the case of mature fruit trees, the linking unit introduces a new fruit protection technique. In addition, in the case of fruit trees showing signs of disease, the linking unit can also introduce a new pest control technique. In this way, by customizing the linking method according to the type and growth stage of the fruit tree, more appropriate technology can be introduced. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input data according to the type and growth stage of the fruit tree into the generation AI and cause the generation AI to customize the linking method.

[0068] When linking a newly developed cultivation technique, the linking unit can compare it with techniques from different regions and link it. For example, the linking unit compares pruning techniques from different regions and introduces the optimal technique. For example, the linking unit compares pest control measures from different regions and introduces the optimal measure. The linking unit can also compare top dressing techniques from different regions and introduce the optimal technique. This makes it possible to introduce the optimal technology by comparing it with techniques from different regions. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input technical data from different regions into the generation AI and have the generation AI compare and link the techniques.

[0069] When linking with a newly created cultivation technique, the linking unit can customize the technology by referring to the user's past linking history. For example, the linking unit suggests the optimal technology based on the user's past linking history. For example, the linking unit prioritizes linking specific technologies based on the user's past linking history. The linking unit can also improve linking accuracy based on the user's past linking history. This makes it possible to introduce the optimal technology by referring to the user's past linking history. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's past linking history into the generation AI and have the generation AI customize the technology.

[0070] When linking with a newly created cultivation technique, the linking unit can improve the linking method by reflecting user feedback. The linking unit, for example, adjusts the linking method based on feedback provided by the user. For example, the linking unit changes the type of technology to be linked based on user feedback. The linking unit can also change the priority of the technologies to be linked based on user feedback. This makes it possible to provide an optimal linking method by reflecting user feedback. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input user feedback data into the generation AI and cause the generation AI to improve the linking method.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When analyzing the condition of a fruit tree, the analysis unit can customize the analysis method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the analysis focuses on the parts that are growing quickly. In the case of a mature fruit tree, the condition of the fruit can be analyzed in detail. Furthermore, in the case of a fruit tree that shows signs of disease, the analysis can also focus on the diseased areas. This allows for more appropriate analysis by customizing the analysis method according to the type and growth stage of the fruit tree. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input analysis data according to the type and growth stage of the fruit tree into the generation AI and have the generation AI customize the analysis method.

[0073] The collection unit can improve accuracy by collecting information on weather, crop conditions, disinfection, and top dressing from different data sources. For example, weather data can be collected from multiple weather services. Crop condition data can be collected from different sensors. Disinfection and top dressing information can also be collected from multiple agricultural databases. This allows for collecting information from different data sources, thereby improving accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information from different data sources into the generation AI and cause the generation AI to improve the accuracy of information collection.

[0074] When identifying the work that needs to be done now, the identification unit can improve accuracy by referring to past work data. For example, the optimal timing for work can be identified based on past work data. The optimal work method can be identified based on past work data. The optimal work content can also be identified based on past work data. In this way, by referring to past work data, the identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past work data into a generation AI and have the generation AI identify the work content.

[0075] When linking with a newly created cultivation technique, the linking unit can improve accuracy by referring to past linking data. For example, a new pruning technique can be introduced based on past linking data. A new pest control measure can be introduced based on past linking data. A new top dressing technique can also be introduced based on past linking data. In this way, linking accuracy can be improved by referring to past linking data. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input past linking data into the generation AI and have the generation AI improve linking accuracy.

[0076] When analyzing the condition of fruit trees, the analysis unit can improve the accuracy of the analysis by combining different analysis algorithms. For example, an image analysis algorithm can be combined with a machine learning algorithm to detect signs of disease. An image analysis algorithm can be combined with a statistical analysis algorithm to identify growth delays. An image analysis algorithm can also be combined with a data mining algorithm to predict the optimal pruning time. In this way, the analysis accuracy can be improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and cause the generation AI to execute the processing to improve the analysis accuracy.

[0077] The display unit can dynamically change the information displayed in accordance with the worker's line of sight. For example, if the worker is looking at a specific branch, information about that branch is displayed. If the worker is looking at the entire fruit tree, the overall health status is displayed. Furthermore, if the worker is looking at a specific part, detailed information about that part can be displayed. This allows for efficient work by dynamically changing information in accordance with the worker's line of sight. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the worker's line of sight data into the generation AI and have the generation AI dynamically change the displayed information.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The input unit takes images and videos of the condition of the fruit tree and inputs them into the generative AI. For example, the input unit can acquire images from a long distance to capture the overall view of the fruit tree. It can also acquire images from a close distance to analyze the tree's details in detail, and it can also acquire images from multiple angles to analyze different aspects. Step 2: The analysis unit uses the generation AI to analyze the condition of the fruit tree input by the input unit and identify branches that should be pruned and branches that should be left. For example, the generation AI can use deep learning to analyze images of the fruit tree and identify branches that show signs of disease or are not growing well. It can also use neural networks to analyze the condition of the fruit tree. Step 3: The display unit displays the information identified by the analysis unit on the worker's goggles. For example, the goggles can display the branches that need to be pruned, allowing the worker to instantly determine which branches to keep. The information displayed can also be dynamically changed according to the worker's line of sight. Step 4: The collection unit collects information on weather, crop conditions, disinfection, and top dressing. For example, it can collect weather information such as temperature, humidity, and precipitation, crop conditions such as the growth stage and presence of pests and diseases, and disinfection information such as the chemicals used and frequency of disinfection. Step 5: The identification unit identifies the work that needs to be done based on the information collected by the collection unit. For example, it can analyze the collected information using generative AI and identify the optimal work. It can also analyze weather data and determine whether disinfection should be carried out before rain falls. Step 6: The collaboration unit collaborates with newly developed cultivation techniques and provides the latest information. For example, it collects information on new pruning techniques and pest control measures and provides it to workers. It can also collaborate with newly developed cultivation techniques using generative AI.

[0080] (Example 2) A cultivation support system according to an embodiment of the present invention automatically analyzes the condition of fruit trees and provides optimal work instructions. The cultivation support system captures images and videos of the fruit tree's condition, analyzes them using a generation AI, and identifies branches that need pruning and branches that should be left. The cultivation support system also collects information on weather, crop conditions, disinfection, and top dressing to identify current work. Furthermore, the cultivation support system integrates with newly developed cultivation techniques to provide the latest information. For example, the cultivation support system captures images of the fruit tree's condition, and the generation AI analyzes them. The generation AI analyzes images of the fruit tree to identify branches that show signs of disease or are not growing properly. The cultivation support system then displays the information identified by the generation AI on the worker's goggles. For example, the goggles display branches that need pruning, allowing the worker to instantly determine which branches to leave. Furthermore, the cultivation support system analyzes weather data and crop growth status to determine the optimal timing for work. For example, it determines whether disinfection should be performed before rain falls. This significantly reduces the barriers to cultivation skills for young people entering the agricultural industry. This allows the cultivation support system to automatically analyze the condition of fruit trees and provide optimal work content. For example, even young people with little experience can work efficiently, which leads to stable harvest volume and quality. Furthermore, by providing optimal work timing according to the weather and crop conditions, it is possible to provide an environment where young people can start farming with peace of mind.

[0081] A cultivation support system according to an embodiment includes an input unit, an analysis unit, a display unit, a collection unit, an identification unit, and a linking unit. The input unit captures images and videos of the condition of a fruit tree and inputs them into the generation AI. For example, the input unit acquires images from a long distance to capture an overall view of the fruit tree. The input unit can also acquire images from a close distance to analyze the details of the fruit tree in detail. The input unit can also acquire images from multiple angles to analyze different aspects of the fruit tree. The analysis unit uses the generation AI to analyze the condition of the fruit tree input by the input unit and identify branches that should be pruned and branches that should be left. For example, the generation AI analyzes images of the fruit tree using deep learning to identify branches that show signs of disease or branches that are not growing properly. The analysis unit can also analyze the condition of the fruit tree using a neural network. The analysis unit can also use the generation AI to analyze the condition of the fruit tree and identify branches that should be pruned and branches that should be left. The display unit displays the information identified by the analysis unit on the operator's goggles. For example, the display unit can display the branches that need to be pruned on the goggles, allowing the worker to instantly determine which branches to leave. The display unit can also display information identified by the generation AI on the worker's goggles. The display unit can also dynamically change the information displayed according to the worker's line of sight. The collection unit collects information on weather, crop conditions, disinfection, and top dressing. For example, the collection unit collects weather information such as temperature, humidity, and precipitation. The collection unit can also collect crop conditions such as the growth stage of the crop and the presence or absence of pests and diseases. The collection unit can also collect disinfection information such as the pesticides to be used and the frequency of disinfection. The identification unit identifies the current work to be done based on the information collected by the collection unit. For example, the identification unit analyzes the information collected using the generation AI and identifies the optimal work content. The identification unit can also analyze weather data to determine whether disinfection should be performed before rain falls. The identification unit can also analyze the crop growth status and determine the timing of top dressing. The collaboration unit connects with newly developed cultivation techniques to provide the latest information. For example, the collaboration unit can collect information on new pruning techniques and pest control measures and provide it to workers. The collaboration unit can also collaborate with newly developed cultivation techniques using generative AI.The collaboration unit can also improve the collaboration method by reflecting user feedback. This allows the cultivation support system according to the embodiment to automatically analyze the condition of fruit trees and provide optimal work content. For example, even young people with little experience can work efficiently, which leads to stable harvest volume and quality. Furthermore, by providing optimal work timing according to the weather and crop conditions, an environment can be created in which young people can start farming with peace of mind.

[0082] The analysis unit can analyze the condition of the fruit tree using the generation AI and identify branches that should be pruned and branches that should be left. The analysis unit, for example, analyzes the condition of the fruit tree using the generation AI. For example, the generation AI can analyze images of the fruit tree using deep learning to identify branches that show signs of disease or branches that are not growing properly. The analysis unit can also analyze the condition of the fruit tree using a neural network. The analysis unit can also analyze the condition of the fruit tree using the generation AI to identify branches that should be pruned and branches that should be left. This allows the generation AI to accurately analyze the condition of the fruit tree and perform optimal pruning work. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data of the fruit tree into the generation AI and have the generation AI analyze the condition of the fruit tree.

[0083] The display unit can display the information identified by the generation AI on the worker's goggles. For example, the display unit displays the information identified by the generation AI on the worker's goggles. For example, the display unit displays branches to be pruned on the goggles, allowing the worker to instantly determine which branches to leave. The display unit can also dynamically change the information displayed in accordance with the worker's line of sight. The display unit can also update the information displayed in real time in accordance with the worker's work status. This allows the worker to check the necessary information in real time and work efficiently. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the information identified by the generation AI and cause the generation AI to execute the information to be displayed on the worker's goggles.

[0084] The collection unit can collect information on weather, crop conditions, disinfection, and top dressing. The collection unit collects weather information such as temperature, humidity, and precipitation. For example, the collection unit collects weather data from multiple weather services. The collection unit can also collect crop conditions such as the growth stage of the crop and the presence or absence of pests and diseases. For example, the collection unit collects crop condition data from different sensors. The collection unit can also collect disinfection information such as the chemicals used and the frequency of disinfection. For example, the collection unit collects disinfection information from multiple agricultural databases. The collection unit can also collect top dressing information such as the type of fertilizer used and the timing of fertilization. By collecting the necessary information, the optimal work timing can be identified. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input weather data into the generation AI and cause the generation AI to collect weather information.

[0085] The identification unit can identify the work that needs to be done now based on the information collected by the generation AI. The identification unit, for example, analyzes the information collected using the generation AI and identifies the optimal work content. For example, the identification unit analyzes weather data and determines whether disinfection should be performed before rain falls. The identification unit can also analyze the growth status of crops and identify the timing of top dressing. The identification unit can also identify the work that needs to be done now based on the collected information using the generation AI. In this way, the generation AI can be used to identify the optimal work content and perform the work efficiently. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the collected information into the generation AI and have the generation AI identify the work that needs to be done now.

[0086] The collaboration unit can collaborate with newly developed cultivation techniques and provide the latest information. For example, the collaboration unit collects information on new pruning techniques and pest control measures and provides it to workers. For example, the collaboration unit collaborates with newly developed cultivation techniques using the generation AI. The collaboration unit can also improve the collaboration method by reflecting user feedback. For example, the collaboration unit adjusts the collaboration method based on feedback provided by the user. The collaboration unit can also improve the collaboration accuracy by referring to past collaboration data. For example, the collaboration unit introduces a new pruning technique based on past collaboration data. This makes it possible to improve the efficiency and quality of work by utilizing the latest cultivation techniques. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input information on newly developed cultivation techniques into the generation AI and have the generation AI provide the latest information.

[0087] The input unit can estimate the user's emotions and adjust the timing of photographing the state of the fruit tree based on the estimated user's emotions. For example, if the user is feeling stressed, the input unit photographs the state of the fruit tree at a time when the user is able to relax. For example, if the user is concentrating, the input unit photographs the state of the fruit tree at the optimal timing to maximize work efficiency. Furthermore, if the user is tired, the input unit can also adjust the timing to photograph the state of the fruit tree after a break. This allows the state of the fruit tree to be photographed at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of photographing.

[0088] When photographing the condition of a fruit tree, the input unit acquires multiple images and videos from different angles and distances, thereby improving analysis accuracy. For example, the input unit acquires images from a long distance to capture an overall image of the fruit tree. For example, the input unit acquires images from a close distance to analyze the details of the fruit tree in detail. The input unit can also acquire images from multiple angles to analyze different aspects of the fruit tree. This allows for improved analysis accuracy by acquiring images and videos from different angles and distances. Some or all of the above-mentioned processing in the input unit may be performed using, or without, AI. For example, the input unit can input image data acquired from different angles and distances to the generation AI, causing the generation AI to improve analysis accuracy.

[0089] The input unit can take specific lighting conditions into consideration when photographing the condition of fruit trees. For example, the input unit takes photos during the time when morning sunlight hits the fruit trees to capture natural colors. For example, the input unit can take photos during the time when evening sunlight hits the fruit trees to analyze the shadows. The input unit can also take photos on a cloudy day to capture the condition of the fruit trees under uniform lighting conditions. This allows for more accurate photographing of the condition of the fruit trees by taking specific lighting conditions into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input image data photographed under specific lighting conditions to the generation AI and have the generation AI analyze the condition of the fruit trees.

[0090] When photographing the condition of a fruit tree, the input unit can customize the photographing method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the input unit focuses on photographing the parts that are growing quickly. For example, in the case of a mature fruit tree, the input unit photographs the condition of the fruit in detail. Furthermore, in the case of a fruit tree showing signs of disease, the input unit can also focus on photographing the diseased area. In this way, by customizing the photographing method according to the type and growth stage of the fruit tree, more appropriate data can be obtained. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input photographing data according to the type and growth stage of the fruit tree to the generation AI, and cause the generation AI to customize the photographing method.

[0091] The input unit can estimate the user's emotions and select a part of the fruit tree to photograph based on the estimated user's emotions. For example, when the user is relaxed, the input unit photographs the entire fruit tree to check the user's overall health. For example, when the user is concentrating, the input unit focuses on photographing specific problem areas. Furthermore, when the user is tired, the input unit can photograph key parts that can be easily identified. This allows the optimal part of the fruit tree to be photographed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to select a part of the fruit tree to photograph.

[0092] The input unit can simultaneously acquire information about the surrounding environment when photographing the condition of the fruit tree. For example, the input unit acquires data using a soil humidity sensor when photographing the condition of the fruit tree. For example, the input unit acquires ambient temperature data using a temperature sensor when photographing the condition of the fruit tree. The input unit can also measure wind strength using an anemometer when photographing the condition of the fruit tree. This allows for more accurate analysis by simultaneously acquiring ambient environmental information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input ambient environmental information to the generation AI and cause the generation AI to analyze the condition of the fruit tree.

[0093] When photographing the condition of fruit trees, the input unit can automatically photograph a wide range of fruit trees using a drone or a robot. The input unit, for example, uses a drone to take aerial photographs of the entire orchard to check the condition of the wide range of fruit trees. For example, the input unit uses a robot to photograph the fruit trees in detail from the base to the top. The input unit can also combine a drone and a robot to simultaneously photograph the entire fruit tree and its details. This makes it possible to efficiently photograph a wide range of fruit trees using a drone or a robot. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input image data acquired by a drone or a robot into the generation AI and have the generation AI analyze the condition of the wide range of fruit trees.

[0094] When photographing the condition of fruit trees, the input unit can suggest the optimal photographing method by referring to the user's past photographing history. The input unit, for example, analyzes images taken by the user in the past and suggests the optimal photographing angle. For example, the input unit can suggest the optimal time of day for photographing based on the user's past photographing history. The input unit can also suggest the optimal equipment by referring to photographing equipment used by the user in the past. In this way, the optimal photographing method can be suggested by referring to the user's past photographing history. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's past photographing history into the generation AI and cause the generation AI to suggest the optimal photographing method.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to provide an optimal display method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0096] When analyzing the condition of a fruit tree, the analysis unit can improve the accuracy of the analysis by referring to past analysis data. The analysis unit, for example, detects signs of disease early based on past analysis data. For example, the analysis unit can identify growth delays based on past analysis data. The analysis unit can also predict the optimal pruning timing based on past analysis data. In this way, by referring to past analysis data, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0097] When analyzing the condition of fruit trees, the analysis unit can improve the accuracy of the analysis by combining different analysis algorithms. For example, the analysis unit can combine an image analysis algorithm with a machine learning algorithm to detect signs of disease. For example, the analysis unit can combine an image analysis algorithm with a statistical analysis algorithm to identify growth delays. The analysis unit can also combine an image analysis algorithm with a data mining algorithm to predict the optimal pruning time. This allows the combination of different analysis algorithms to improve the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and cause the generation AI to execute the processing to improve the analysis accuracy.

[0098] When analyzing the condition of a fruit tree, the analysis unit can customize the analysis method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the analysis unit focuses on analyzing the parts that are growing quickly. For example, in the case of a mature fruit tree, the analysis unit analyzes the condition of the fruit in detail. Furthermore, in the case of a fruit tree that shows signs of disease, the analysis unit can also focus on analyzing the diseased parts. This enables more appropriate analysis by customizing the analysis method according to the type and growth stage of the fruit tree. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input analysis data according to the type and growth stage of the fruit tree into the generation AI and have the generation AI customize the analysis method.

[0099] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize displaying important information. For example, if the user is relaxed, the analysis unit can prioritize displaying detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying information that covers the main points. This makes it possible to provide analysis results with optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0100] When analyzing the condition of a fruit tree, the analysis unit can also take into account environmental information surrounding the fruit tree. The analysis unit, for example, analyzes the moisture status of the fruit tree based on soil humidity data. For example, the analysis unit analyzes the growth status of the fruit tree based on ambient temperature data. The analysis unit can also analyze the health status of the fruit tree based on wind speed data. This allows for more accurate analysis by taking ambient environmental information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input ambient environmental information into the generation AI and cause the generation AI to analyze the condition of the fruit tree.

[0101] When analyzing the condition of fruit trees, the analysis unit can improve the accuracy of the analysis by comparing the conditions of different fruit trees. For example, the analysis unit can compare healthy fruit trees with diseased fruit trees to identify signs of disease. For example, the analysis unit can compare fast-growing fruit trees with slow-growing fruit trees to identify growth delays. The analysis unit can also compare different varieties of fruit trees to identify optimal cultivation methods. This allows the analysis accuracy to be improved by comparing the conditions of different fruit trees. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input condition data of different fruit trees into the generation AI and cause the generation AI to improve the analysis accuracy.

[0102] When analyzing the condition of fruit trees, the analysis unit can customize the analysis method by referring to the user's past analysis results. The analysis unit, for example, proposes an optimal analysis method based on the user's past analysis results. For example, the analysis unit may focus its analysis on specific problem areas based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. This makes it possible to optimize the analysis method by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's past analysis results into the generation AI and have the generation AI customize the analysis method.

[0103] The display unit can estimate the user's emotions and adjust the display content based on the estimated user emotions. For example, when the user is nervous, the display unit provides a simple, highly visible display method. For example, when the user is relaxed, the display unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This makes it possible to provide optimal display content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display content.

[0104] The display unit can dynamically change the information displayed in accordance with the worker's line of sight. For example, when the worker is looking at a specific branch, the display unit displays information about that branch. For example, when the worker is looking at the entire fruit tree, the display unit displays the overall health status. Furthermore, when the worker is looking at a specific part, the display unit can also display detailed information about that part. This enables efficient work by dynamically changing information in accordance with the worker's line of sight. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's line of sight data to the generation AI and cause the generation AI to dynamically change the displayed information.

[0105] The display unit can update the displayed information in real time according to the worker's work status. For example, when the worker is pruning, the display unit updates information about branches to be pruned in real time. For example, when the worker is disinfecting, the display unit updates information about areas to be disinfected in real time. Furthermore, when the worker is applying top dressing, the display unit can also update information about areas to be fertilized in real time. This enables efficient work by updating information in real time according to the worker's work status. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data about the worker's work status to the generation AI and cause the generation AI to perform real-time updates of the displayed information.

[0106] The display unit can customize the information to be displayed by referring to the worker's past work history. The display unit, for example, suggests an optimal pruning method based on the worker's past pruning work history. For example, the display unit suggests an optimal disinfection method based on the worker's past disinfection work history. The display unit can also suggest an optimal top dressing method based on the worker's past top dressing work history. In this way, optimal information can be provided by referring to the worker's past work history. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's past work history into the generation AI and have the generation AI customize the display information.

[0107] The display unit can estimate the user's emotions and determine the priority of information to display based on the estimated user emotions. For example, when the user is nervous, the display unit prioritizes displaying important information. For example, when the user is relaxed, the display unit prioritizes displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that summarizes the main points. This makes it possible to provide information in an optimal priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the information to be displayed.

[0108] The display unit can provide the displayed information to the worker using senses other than sight. For example, if the worker has difficulty visually confirming the information, the display unit provides the information by voice. For example, if the worker is wearing gloves, the display unit provides the information by vibration. Furthermore, if the worker uses both sight and hearing at the same time, the display unit can provide a combination of voice and visual information. This allows the worker to receive information more effectively by using senses other than sight. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input sensory data other than sight into the generation AI and cause the generation AI to execute the information provision method.

[0109] The display unit can simultaneously display the information on the worker's device. For example, if the worker is using a smartphone, the display unit simultaneously displays the information on the goggles and the smartphone. For example, if the worker is using a tablet, the display unit simultaneously displays the information on the goggles and the tablet. Furthermore, if the worker is using multiple devices, the display unit can simultaneously display the information on all devices. This allows the worker to efficiently check the information by displaying the information on multiple devices simultaneously. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's device data into the generation AI and have the generation AI execute the simultaneous display of information.

[0110] The display unit can customize the information to be displayed by reflecting the worker's feedback. The display unit, for example, adjusts the display content based on the feedback provided by the worker. For example, the display unit improves the display method based on the worker's feedback. The display unit can also change the priority of information based on the worker's feedback. This makes it possible to provide optimal information by reflecting the worker's feedback. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input the worker's feedback data into a generation AI and have the generation AI customize the display information.

[0111] The collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit prioritizes collecting important information. For example, if the user is relaxed, the collection unit collects detailed information. Furthermore, if the user is in a hurry, the collection unit can also collect information that focuses on the main points. This allows optimal information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the type of information to be collected.

[0112] The collection unit can improve accuracy by collecting information on weather, crop conditions, disinfection, and top dressing from different data sources. For example, the collection unit collects weather data from multiple weather services. For example, the collection unit collects crop condition data from different sensors. The collection unit can also collect disinfection and top dressing information from multiple agricultural databases. This allows for improved accuracy by collecting information from different data sources. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input information from different data sources into the generation AI and cause the generation AI to improve the accuracy of information collection.

[0113] The collection unit can compare the collected information with past data to detect abnormal values. For example, the collection unit can compare it with past weather data to detect abnormal weather conditions. For example, the collection unit can compare it with past crop growth data to detect abnormal growth patterns. The collection unit can also compare it with past disinfection and top dressing data to detect abnormal usage amounts. This makes it possible to detect abnormal values ​​by comparing it with past data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past data into the generation AI and have the generation AI detect abnormal values.

[0114] The collection unit can customize the information to be collected according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the collection unit focuses on collecting information about growth. For example, in the case of a mature fruit tree, the collection unit collects information about the condition of the fruit. In addition, in the case of a fruit tree showing signs of disease, the collection unit can also collect information about the diseased area. In this way, by collecting information according to the type and growth stage of the fruit tree, more appropriate data can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information according to the type and growth stage of the fruit tree into the generation AI and have the generation AI customize the information.

[0115] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit prioritizes collecting important information. For example, if the user is relaxed, the collection unit prioritizes collecting detailed information. Furthermore, if the user is in a hurry, the collection unit can also prioritize collecting information that summarizes the main points. This allows information to be collected in an optimal priority order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information to be collected.

[0116] The collection unit can collect information on weather, crop conditions, disinfection, and top dressing by comparing it with data from different regions. For example, the collection unit compares weather data from different regions to identify the optimal work timing. For example, the collection unit compares crop growth data from different regions to identify the optimal cultivation method. The collection unit can also compare disinfection and top dressing data from different regions to identify the optimal amount to use. This makes it possible to collect optimal information by comparing it with data from different regions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data from different regions into the generation AI and cause the generation AI to collect information.

[0117] The collection unit can customize the information to be collected by referring to the user's past collection history. The collection unit, for example, suggests an optimal collection method based on weather data collected by the user in the past. For example, the collection unit suggests an optimal collection method based on the user's past crop growth data. The collection unit can also suggest an optimal collection method based on the user's past disinfection and top dressing data. In this way, optimal information can be collected by referring to the user's past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history into the generation AI and have the generation AI customize the information.

[0118] The collection unit can improve the information collection method by reflecting user feedback. The collection unit adjusts the collection method, for example, based on feedback provided by the user. For example, the collection unit changes the type of information to collect based on user feedback. The collection unit can also change the priority of the information to collect based on user feedback. This makes it possible to provide an optimal information collection method by reflecting user feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input user feedback data into the generation AI and cause the generation AI to improve the collection method.

[0119] The identification unit can estimate the user's emotions and adjust the identified task content based on the estimated user emotions. For example, if the user is nervous, the identification unit prioritizes identifying important tasks. For example, if the user is relaxed, the identification unit can identify detailed task content. Furthermore, if the user is in a hurry, the identification unit can also identify task content that focuses on the main points. This makes it possible to identify optimal task content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using an AI, for example, or without an AI. For example, the identification unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the task content.

[0120] The identification unit can improve accuracy when identifying the work that needs to be done now by referring to past work data. For example, the identification unit identifies the optimal timing for work based on past work data. For example, the identification unit identifies the optimal work method based on past work data. The identification unit can also identify the optimal work content based on past work data. In this way, by referring to past work data, the identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past work data into a generation AI and cause the generation AI to identify the work content.

[0121] The identification unit can improve accuracy when identifying the current task by combining different identification algorithms. The identification unit, for example, combines a machine learning algorithm and a statistical analysis algorithm to identify the optimal task content. For example, the identification unit combines a data mining algorithm and an image analysis algorithm to identify the optimal task content. The identification unit can also combine different algorithms to identify the optimal timing for the task. In this way, by combining different identification algorithms, identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input different identification algorithms into the generation AI and cause the generation AI to identify the task content.

[0122] When identifying the work that needs to be done, the identification unit can customize the identification method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the identification unit prioritizes identifying work related to growth. For example, in the case of a mature fruit tree, the identification unit identifies work related to the condition of the fruit. In addition, in the case of a fruit tree showing signs of disease, the identification unit can also identify work related to the diseased area. In this way, by customizing the identification method according to the type and growth stage of the fruit tree, more appropriate work content can be identified. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data according to the type and growth stage of the fruit tree into the generation AI and have the generation AI identify the work content.

[0123] The identification unit can estimate the user's emotions and determine the priority of tasks to be identified based on the estimated user emotions. For example, if the user is nervous, the identification unit prioritizes identifying important tasks. For example, if the user is relaxed, the identification unit prioritizes identifying detailed task content. Furthermore, if the user is in a hurry, the identification unit can also prioritize identifying task content that focuses on the main points. This makes it possible to identify task content with optimal priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the task content.

[0124] When identifying the work that needs to be done, the identification unit can also take into consideration environmental information surrounding the fruit tree. The identification unit, for example, identifies the optimal timing for the work based on soil humidity data. For example, the identification unit can identify the optimal work content based on ambient temperature data. The identification unit can also identify the optimal work method based on wind speed data. This allows for more accurate identification of work content by taking ambient environmental information into consideration. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input ambient environmental information into the generation AI and have the generation AI identify the work content.

[0125] When identifying the work that needs to be done, the identification unit can improve the identification accuracy by comparing the conditions of different fruit trees. For example, the identification unit compares healthy fruit trees with diseased fruit trees to identify the optimal work content. For example, the identification unit compares fast-growing fruit trees with slow-growing fruit trees to identify the optimal timing for work. The identification unit can also compare different varieties of fruit trees to identify the optimal cultivation method. In this way, by comparing the conditions of different fruit trees, the identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input condition data of different fruit trees into the generation AI and cause the generation AI to identify the work content.

[0126] When identifying the work that needs to be done now, the identification unit can customize the identification method by referring to the user's past work results. The identification unit, for example, suggests an optimal work method based on the user's past work results. For example, the identification unit may focus on identifying specific problem areas based on the user's past work results. The identification unit can also improve the identification accuracy based on the user's past work results. This makes it possible to optimize the identification method by referring to the user's past work results. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input the user's past work results into the generation AI and have the generation AI identify the work content.

[0127] The linking unit can estimate the user's emotions and adjust the linked technologies and information based on the estimated user emotions. For example, if the user is nervous, the linking unit prioritizes linking important technologies and information. For example, if the user is relaxed, the linking unit can link detailed technologies and information. Furthermore, if the user is in a hurry, the linking unit can link technologies and information that focus on the main points. This allows the linking of optimal technologies and information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the linked technologies and information.

[0128] When linking with a newly created cultivation technique, the linking unit can improve accuracy by referring to past linking data. For example, the linking unit introduces a new pruning technique based on past linking data. For example, the linking unit introduces a new pest control measure based on past linking data. The linking unit can also introduce a new top dressing technique based on past linking data. In this way, linking accuracy can be improved by referring to past linking data. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input past linking data into the generation AI and cause the generation AI to improve linking accuracy.

[0129] When linking with a newly developed cultivation technique, the linking unit can improve the linking accuracy by combining different techniques. For example, the linking unit links a new pruning technique with an existing technique. For example, the linking unit links a new pest control technique with an existing technique. The linking unit can also link a new top dressing technique with an existing technique. This allows the linking accuracy to be improved by combining different techniques. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input different techniques into the generation AI and cause the generation AI to improve the linking accuracy.

[0130] When linking with newly developed cultivation techniques, the linking unit can customize the linking method according to the type and growth stage of the fruit tree. For example, in the case of young fruit trees, the linking unit introduces a new growth promotion technique. For example, in the case of mature fruit trees, the linking unit introduces a new fruit protection technique. In addition, in the case of fruit trees showing signs of disease, the linking unit can also introduce a new pest control technique. In this way, by customizing the linking method according to the type and growth stage of the fruit tree, more appropriate technology can be introduced. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input data according to the type and growth stage of the fruit tree into the generation AI and cause the generation AI to customize the linking method.

[0131] The linking unit can estimate the user's emotions and determine the priority of technologies and information to be linked based on the estimated user emotions. For example, if the user is nervous, the linking unit prioritizes linking important technologies and information. For example, if the user is relaxed, the linking unit prioritizes linking detailed technologies and information. Furthermore, if the user is in a hurry, the linking unit can prioritize linking technologies and information that highlight the key points. This allows technologies and information to be linked in the optimal priority order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of technologies and information to be linked.

[0132] When linking a newly developed cultivation technique, the linking unit can compare it with techniques from different regions and link it. For example, the linking unit compares pruning techniques from different regions and introduces the optimal technique. For example, the linking unit compares pest control measures from different regions and introduces the optimal measure. The linking unit can also compare top dressing techniques from different regions and introduce the optimal technique. This makes it possible to introduce the optimal technology by comparing it with techniques from different regions. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input technical data from different regions into the generation AI and have the generation AI compare and link the techniques.

[0133] When linking with a newly created cultivation technique, the linking unit can customize the technology by referring to the user's past linking history. For example, the linking unit suggests the optimal technology based on the user's past linking history. For example, the linking unit prioritizes linking specific technologies based on the user's past linking history. The linking unit can also improve linking accuracy based on the user's past linking history. This makes it possible to introduce the optimal technology by referring to the user's past linking history. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's past linking history into the generation AI and have the generation AI customize the technology.

[0134] When linking with a newly created cultivation technique, the linking unit can improve the linking method by reflecting user feedback. The linking unit, for example, adjusts the linking method based on feedback provided by the user. For example, the linking unit changes the type of technology to be linked based on user feedback. The linking unit can also change the priority of the technologies to be linked based on user feedback. This makes it possible to provide an optimal linking method by reflecting user feedback. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input user feedback data into the generation AI and cause the generation AI to improve the linking method. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, display unit, collection unit, identification unit, and linking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit photographs the condition of the fruit tree using the camera 42 of the smart device 14 and transmits the image data to the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the condition of the fruit tree using generative AI. The display unit displays the analysis results to the worker, for example, by using the display 40A of the smart device 14. The collection unit collects information on weather and crop conditions using, for example, a sensor of the smart device 14 or the identification processing unit 290 of the data processing device 12. The identification unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 and identifies the optimal work content. The linking unit, for example, links to new cultivation techniques using the identification processing unit 290 of the data processing device 12 to provide the latest information. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, display unit, collection unit, identification unit, and linking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit photographs the condition of the fruit tree using the camera 42 of the smart glasses 214 and transmits the image data to the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the condition of the fruit tree using generative AI. The display unit displays the analysis results to the worker, for example, by using the display of the smart glasses 214. The collection unit collects information on weather and crop conditions using, for example, the sensors of the smart glasses 214 or the identification processing unit 290 of the data processing device 12. The identification unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 and identifies the optimal work content. The linking unit, for example, links to new cultivation techniques using the identification processing unit 290 of the data processing device 12 to provide the latest information. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, analysis unit, display unit, collection unit, identification unit, and linking unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit photographs the condition of the fruit tree using the camera 42 of the headset terminal 314 and transmits the image data to the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the condition of the fruit tree using a generative AI. The display unit displays the analysis results to the worker, for example, by the display 343 of the headset terminal 314. The collection unit collects information on weather and crop conditions using, for example, a sensor of the headset terminal 314 or the identification processing unit 290 of the data processing device 12. The identification unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 and identifies the optimal work content. The linking unit, for example, links to new cultivation techniques using the identification processing unit 290 of the data processing device 12 to provide the latest information. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, display unit, collection unit, identification unit, and linking unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit photographs the condition of the fruit trees using the camera 42 of the robot 414 and transmits the image data to the identification processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the condition of the fruit trees using generative AI. The display unit displays the analysis results to the worker, for example, using the display of the robot 414. The collection unit collects information on weather and crop conditions using, for example, the sensors of the robot 414 or the identification processing unit 290 of the data processing device 12. The identification unit analyzes the information collected by, for example, the identification processing unit 290 of the data processing device 12 and identifies the optimal work content. The linking unit, for example, links to new cultivation techniques using the identification processing unit 290 of the data processing device 12 to provide the latest information.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] When analyzing the condition of a fruit tree, the analysis unit can customize the analysis method according to the type and growth stage of the fruit tree. For example, in the case of a young fruit tree, the analysis focuses on the parts that are growing quickly. In the case of a mature fruit tree, the condition of the fruit can be analyzed in detail. Furthermore, in the case of a fruit tree that shows signs of disease, the analysis can also focus on the diseased areas. This allows for more appropriate analysis by customizing the analysis method according to the type and growth stage of the fruit tree. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input analysis data according to the type and growth stage of the fruit tree into the generation AI and have the generation AI customize the analysis method.

[0137] The display unit can estimate the user's emotions and adjust the display content based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide optimal display content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the display unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display content.

[0138] The collection unit can improve accuracy by collecting information on weather, crop conditions, disinfection, and top dressing from different data sources. For example, weather data can be collected from multiple weather services. Crop condition data can be collected from different sensors. Disinfection and top dressing information can also be collected from multiple agricultural databases. This allows for collecting information from different data sources, thereby improving accuracy. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input information from different data sources into the generation AI and cause the generation AI to improve the accuracy of information collection.

[0139] When identifying the work that needs to be done now, the identification unit can improve accuracy by referring to past work data. For example, the optimal timing for work can be identified based on past work data. The optimal work method can be identified based on past work data. The optimal work content can also be identified based on past work data. In this way, by referring to past work data, the identification accuracy can be improved. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past work data into a generation AI and have the generation AI identify the work content.

[0140] When linking with a newly created cultivation technique, the linking unit can improve accuracy by referring to past linking data. For example, a new pruning technique can be introduced based on past linking data. A new pest control measure can be introduced based on past linking data. A new top dressing technique can also be introduced based on past linking data. In this way, linking accuracy can be improved by referring to past linking data. Some or all of the above-mentioned processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input past linking data into the generation AI and have the generation AI improve linking accuracy.

[0141] The input unit can estimate the user's emotions and adjust the timing of photographing the state of the fruit tree based on the estimated user's emotions. For example, if the user is feeling stressed, the input unit can photograph the state of the fruit tree at a time when the user is able to relax. If the user is concentrating, the input unit can photograph the state of the fruit tree at the optimal timing to maximize work efficiency. Furthermore, if the user is tired, the input unit can adjust the timing to photograph the state of the fruit tree after a break. This allows the input unit to photograph the state of the fruit tree at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of photographing.

[0142] When analyzing the condition of fruit trees, the analysis unit can improve the accuracy of the analysis by combining different analysis algorithms. For example, an image analysis algorithm can be combined with a machine learning algorithm to detect signs of disease. An image analysis algorithm can be combined with a statistical analysis algorithm to identify growth delays. An image analysis algorithm can also be combined with a data mining algorithm to predict the optimal pruning time. In this way, the analysis accuracy can be improved by combining different analysis algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different analysis algorithms into the generation AI and cause the generation AI to execute the processing to improve the analysis accuracy.

[0143] The display unit can dynamically change the information displayed in accordance with the worker's line of sight. For example, if the worker is looking at a specific branch, information about that branch is displayed. If the worker is looking at the entire fruit tree, the overall health status is displayed. Furthermore, if the worker is looking at a specific part, detailed information about that part can be displayed. This allows for efficient work by dynamically changing information in accordance with the worker's line of sight. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the worker's line of sight data into the generation AI and have the generation AI dynamically change the displayed information.

[0144] The identification unit can estimate the user's emotions and adjust the identified task content based on the estimated user emotions. For example, if the user is nervous, it can prioritize identifying important tasks. If the user is relaxed, it can identify detailed task content. Furthermore, if the user is in a hurry, it can identify task content that focuses on the main points. This makes it possible to identify the optimal task content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the identification unit can input the user's emotion data into the generation AI and have the generation AI adjust the task content.

[0145] The linking unit can estimate the user's emotions and adjust the linked technologies and information based on the estimated user emotions. For example, if the user is nervous, important technologies and information are linked first. If the user is relaxed, detailed technologies and information are linked. Furthermore, if the user is in a hurry, key technologies and information can be linked. This allows the optimal technologies and information to be linked according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the linking unit may be performed using AI, or may be performed without AI. For example, the linking unit can input the user's emotion data into the generation AI and have the generation AI adjust the linked technologies and information.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The input unit takes images and videos of the condition of the fruit tree and inputs them into the generative AI. For example, the input unit can acquire images from a long distance to capture the overall view of the fruit tree. It can also acquire images from a close distance to analyze the tree's details in detail, and it can also acquire images from multiple angles to analyze different aspects. Step 2: The analysis unit uses the generation AI to analyze the condition of the fruit tree input by the input unit and identify branches that should be pruned and branches that should be left. For example, the generation AI can use deep learning to analyze images of the fruit tree and identify branches that show signs of disease or are not growing well. It can also use neural networks to analyze the condition of the fruit tree. Step 3: The display unit displays the information identified by the analysis unit on the worker's goggles. For example, the goggles can display the branches that need to be pruned, allowing the worker to instantly determine which branches to keep. The information displayed can also be dynamically changed according to the worker's line of sight. Step 4: The collection unit collects information on weather, crop conditions, disinfection, and top dressing. For example, it can collect weather information such as temperature, humidity, and precipitation, crop conditions such as the growth stage and presence of pests and diseases, and disinfection information such as the chemicals used and frequency of disinfection. Step 5: The identification unit identifies the work that needs to be done based on the information collected by the collection unit. For example, it can analyze the collected information using generative AI and identify the optimal work. It can also analyze weather data and determine whether disinfection should be carried out before rain falls. Step 6: The collaboration unit collaborates with newly developed cultivation techniques and provides the latest information. For example, it collects information on new pruning techniques and pest control measures and provides it to workers. It can also collaborate with newly developed cultivation techniques using generative AI.

[0148] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0170] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0187] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0188] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0189] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0190] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0191] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0192] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0194] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0197] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0198] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0199] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0202] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0203] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0204] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0205] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0206] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0207] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0208] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0209] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0210] 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.

[0211] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0212] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0213] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0214] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0215] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0216] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0217] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0218] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0219] [Explanation of symbols]

[0220] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input unit that takes images and videos of the state of the fruit trees and inputs them into the generation AI; an analysis unit that analyzes the state of the fruit tree input by the input unit and identifies branches that should be pruned and branches that should be left; a display unit that displays the information identified by the analysis unit on goggles of the worker; A collection department that collects information on weather, crop conditions, disinfection, and top dressing. an identification unit that identifies an operation to be performed based on the information collected by the collection unit; A system equipped with a linking unit that links with newly developed cultivation techniques.

2. The analysis unit Generative AI analyzes the condition of fruit trees and identifies branches that should be pruned and those that should be left. The system of claim 1 .

3. The display unit Information identified by the generated AI is displayed on the worker's goggles. The system of claim 1 .

4. The collecting unit Collect information on weather, crop conditions, disinfection, and fertilization The system of claim 1 .

5. The identification unit Identify the work that needs to be done now based on the information collected by generative AI 2. The system of claim 1.

6. The linking unit is Collaborating with newly developed cultivation techniques to provide the latest information 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the timing of photographing the state of fruit trees based on the estimated user emotions.

2. The system of claim 1.

8. The input unit When photographing the condition of fruit trees, multiple images and videos are taken from different angles and distances to improve analysis accuracy.

2. The system of claim 1.

Images