System

Abstract

To provide a system according to an embodiment, the system intending to propose an optimum management method depending on a species of a plant and a state of soil.SOLUTION: A system according to an embodiment includes: a plant identification part; a soil analysis part; and a proposal part. The plant identification part identifies a species of a plant. The soil analysis part includes specific methods for analyzing a condition or a nutritional state of soil on the basis of the species of the plant identified by the plant identification part. The proposal part proposes a management method of the plant on the basis of the information analyzed by the soil analysis part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately suggest optimal management methods based on plant type and soil conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal management method based on the type of plant and the condition of the soil. [Means for solving the problem]

[0006] The system according to the embodiment includes a plant identification unit, a soil analysis unit, and a suggestion unit. The plant identification unit identifies the type of plant. The soil analysis unit includes a specific method for analyzing the type or nutritional state of soil based on the type of plant identified by the plant identification unit. The suggestion unit suggests a management method for the plant based on the information analyzed by the soil analysis unit. Effect of the Invention

[0007] The system according to the embodiment can propose optimal management methods based on the type of plant and the condition of the soil. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​gardening assistant system according to the embodiment of the present invention is a system that can be used by gardening beginners and professionals alike. This system identifies the type of plant, analyzes the type or nutritional state of the soil, and proposes the optimal plant management method. For example, a user takes a photo of a plant with a smartphone and inputs the photo into the AI. The AI ​​identifies the type of plant using image analysis technology. This allows the user to easily know the type of plant he or she is growing. Next, the type or nutritional state of the soil is analyzed based on the identified type of plant. For example, a dedicated sensor is used to measure the acidity and nutrient content of the soil. The AI ​​analyzes the measured data and determines the type of soil and nutritional state. This allows the user to know the soil condition suitable for the plant. Furthermore, a plant management method is proposed based on the analyzed information. For example, the AI ​​suggests the frequency of watering and the type of fertilizer based on the soil condition and the type of plant. This allows the user to know the optimal management method for maintaining the health of the plant. It is also possible to identify diseases and pests based on the information analyzed by the soil analysis unit. For example, the AI ​​identifies plant diseases and pests using image analysis technology. This allows the user to discover plant diseases and pests early and take appropriate measures. In addition, it is also possible to track the growth of plants. For example, the AI ​​will periodically take photos of the plants and record their growth progress. This allows the user to track the growth of the plants in detail and adjust the necessary management methods. This system allows anyone, from gardening beginners to professionals, to easily manage plants. For example, beginners can grow healthy plants by following the AI's suggestions to care for them. Professionals can also practice more advanced management methods by referring to the AI's analysis results. This allows the AI ​​gardening assistant system to be used by everyone, from gardening beginners to professionals, by identifying the type of plant, analyzing the soil type or nutritional state, and suggesting the optimal management method.

[0029] The AI ​​gardening assistant system according to the embodiment includes a plant identification unit, a soil analysis unit, and a suggestion unit. The plant identification unit identifies the type of plant. For example, the user takes a photo of a plant with a smartphone and inputs the photo to the AI. The AI ​​identifies the type of plant using image analysis technology. For example, the AI ​​uses an image classification algorithm using deep learning to extract and identify the characteristics of the plant. The plant identification unit can also refer to data on plants previously identified by the user to improve the accuracy of identification. The soil analysis unit analyzes the type or nutritional state of the soil based on the type of plant identified by the plant identification unit. The soil analysis unit measures the acidity and nutrient content of the soil using, for example, a dedicated sensor. The AI ​​analyzes the measured data and determines the type and nutritional state of the soil. For example, the soil analysis unit measures the acidity of the soil using a pH sensor and measures the electrical conductivity of the soil using an EC sensor. The soil analysis unit can also measure the nitrogen, phosphorus, and potassium content in the soil using an NPK sensor. The suggestion unit suggests a plant management method based on the information analyzed by the soil analysis unit. The suggestion unit suggests watering frequency and type of fertilizer based on, for example, soil condition and plant type. The AI ​​can also suggest optimal management methods by referring to past data. For example, the suggestion unit suggests different management methods depending on the growth stage of the plant. This allows the AI ​​gardening assistant system to identify the type of plant, analyze the soil type or nutritional state, and suggest the optimal management method.

[0030] The plant identification unit identifies the type of plant. For example, the user takes a photo of a plant with a smartphone and inputs the photo into the AI. The AI ​​uses image analysis technology to identify the type of plant. Specifically, the user takes a photo of the plant using the smartphone camera and uploads the image to a dedicated application. The application sends the image to an AI server on the cloud, and an image classification algorithm using deep learning analyzes the image. This algorithm is trained in advance with a large amount of plant image data set, and extracts features such as leaf shape, color, pattern, and flower structure to perform identification. For example, the AI ​​analyzes the shape of the leaf edge, the pattern of the vein, the color and shape of the flower, etc., and identifies the type of plant based on these features. In addition, the plant identification unit can also refer to the data of plants that the user has previously identified to improve the identification accuracy. As a result, when identifying the same plant again, the past data can be used to identify it more quickly and accurately. In addition, the plant identification unit also has a function that allows the user to provide feedback on the identification result, and if the user confirms that the identification result is correct, the information is incorporated as learning data for the AI, contributing to improving the identification accuracy. In this way, the plant identification unit enables the user to easily identify the type of plant, thereby improving the efficiency and enjoyment of gardening.

[0031] The soil analysis unit analyzes the type or nutritional state of the soil based on the type of plant identified by the plant identification unit. The soil analysis unit, for example, measures the acidity and nutrient content of the soil using a dedicated sensor. Specifically, a user inserts a soil sample into a dedicated sensor device, and the device measures the soil's pH value, electrical conductivity (EC), and the content of major nutrients (nitrogen, phosphorus, potassium). These sensor devices work with smartphones and tablets via Bluetooth (registered trademark) or Wi-Fi, and transmit the measurement data to an application in real time. The AI ​​analyzes the measured data and determines the type and nutritional state of the soil. For example, the soil analysis unit measures the acidity of the soil using a pH sensor and measures the electrical conductivity of the soil using an EC sensor. This makes it possible to determine whether the acidity of the soil is suitable for plant growth. It is also possible to measure the content of nitrogen, phosphorus, and potassium in the soil using an NPK sensor. This makes it possible to check whether the nutrients required by the plants are being supplied sufficiently. In addition, the soil analysis unit can provide more accurate analysis results by referring to past measurement data and a regional soil characteristics database. This allows users to accurately grasp the condition of the soil and take appropriate measures to maintain the health of their plants.

[0032] The suggestion unit proposes a management method for the plant based on the information analyzed by the soil analysis unit. The suggestion unit, for example, suggests the frequency of watering and the type of fertilizer based on the soil condition and the type of plant. Specifically, the AI ​​calculates the optimal management method taking into account the soil pH value and nutrient content, the type of plant, and the growth stage. For example, if the soil is acidic, the AI ​​suggests adding lime to adjust the pH. Also, if nitrogen in the soil is lacking, it recommends adding nitrogen fertilizer. Furthermore, the suggestion unit suggests different management methods depending on the growth stage of the plant. For example, frequent watering is necessary during the germination stage, but it is recommended to reduce the frequency of watering during the growth stage and add certain nutrients during the flowering stage. The AI ​​can also make suggestions based on management methods that have been successful under similar conditions by referring to past data. This allows the suggestion unit to provide specific advice to the user to maintain the health of the plant and promote optimal growth. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, by inputting the results of management performed by the user according to the suggestions into the application, the AI ​​can learn from the data and improve the accuracy of future suggestions. This enables the suggestion unit to always provide the user with the latest and most optimal management techniques, helping them achieve success in gardening.

[0033] The soil analysis unit can measure the acidity or nutrient content of the soil using a dedicated sensor. The soil analysis unit can measure the acidity of the soil using, for example, a pH sensor. For example, the soil analysis unit inserts a pH sensor into the soil and measures the pH value. The soil analysis unit can also measure the electrical conductivity of the soil using an EC sensor. For example, the soil analysis unit inserts an EC sensor into the soil and measures the electrical conductivity. The soil analysis unit can also measure the nitrogen, phosphorus, and potassium content in the soil using an NPK sensor. For example, the soil analysis unit inserts an NPK sensor into the soil and measures the nitrogen, phosphorus, and potassium content. As a result, the acidity and nutrient content of the soil can be accurately measured by using a dedicated sensor. Some or all of the above-mentioned processing in the soil analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the soil analysis unit can input data acquired by the sensor to the generation AI, which can analyze the data to determine the condition of the soil.

[0034] The soil analysis unit may include a disease or pest identification unit that identifies a disease or pest based on the information analyzed by the soil analysis unit. The disease or pest identification unit may, for example, identify a disease or pest based on the information analyzed by the soil analysis unit. For example, the disease or pest identification unit may determine the risk of occurrence of a particular disease or pest based on the nutritional state or acidity of the soil. The disease or pest identification unit may also identify plant diseases and pests using image analysis technology. For example, the disease or pest identification unit may analyze images of the leaves and stems of the plant to detect signs of disease or pests. This allows early measures to be taken by identifying diseases and pests based on the results of the soil analysis. Some or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI or may be performed without using AI. For example, the disease or pest identification unit may input data acquired by the soil analysis unit to the generation AI, and the generation AI may analyze the data to determine the risk of disease or pest.

[0035] The disease or pest identification unit can identify plant diseases or pests using image analysis technology. The disease or pest identification unit can, for example, identify plant diseases or pests using image analysis technology. For example, the disease or pest identification unit takes an image of the leaves and stems of a plant and detects signs of disease or pests using image analysis technology. For example, the disease or pest identification unit uses an image classification algorithm using deep learning to extract and identify the characteristics of plant diseases and pests. The disease or pest identification unit can also refer to past data to improve the accuracy of identification. As a result, plant diseases and pests can be accurately identified using image analysis technology. Some or all of the above-mentioned processing in the disease or pest identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the disease or pest identification unit can input image data of a plant to a generation AI, and the generation AI can analyze the data to detect signs of disease or pests.

[0036] The plant growth tracking unit may include a growth tracking unit that tracks the growth of the plant based on the information identified by the disease or pest identification unit. The growth tracking unit tracks the growth of the plant based on, for example, the information identified by the disease or pest identification unit. For example, the growth tracking unit records the growth stage and health condition of the plant and proposes an appropriate management method. The growth tracking unit may also periodically take photos of the plant and record the progress of the growth. For example, the growth tracking unit measures the height of the plant and the number of leaves and tracks the progress of the growth. In this way, an appropriate management method can be provided by tracking the growth of the plant based on the identification result of the disease or pest. A part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the growth tracking unit inputs the growth data of the plant to the generation AI, and the generation AI analyzes the data to determine the progress of the growth.

[0037] The growth tracking unit can measure the height or number of leaves of a plant using image analysis technology. The growth tracking unit can measure the height or number of leaves of a plant using, for example, image analysis technology. For example, the growth tracking unit takes a photo of the plant and measures the height of the plant using image analysis technology. For example, the growth tracking unit uses an image analysis algorithm using deep learning to measure the height of the plant. The growth tracking unit can also measure the number of leaves of the plant. For example, the growth tracking unit takes a photo of the plant and counts the number of leaves using image analysis technology. As a result, the growth of the plant can be accurately tracked using image analysis technology. Some or all of the above-mentioned processing in the growth tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the growth tracking unit can input image data of the plant to the generation AI, and the generation AI can analyze the data to measure the height or number of leaves of the plant.

[0038] The growth tracking unit can periodically take photos of the plant and record the progress of the growth. The growth tracking unit, for example, periodically takes photos of the plant and records the progress of the growth. For example, the growth tracking unit takes photos of the plant every week and records the progress of the growth. The growth tracking unit can also adjust the shooting frequency according to the growth stage of the plant. For example, the growth tracking unit takes photos every day at the germination stage, every week at the growth stage, and every month at the flowering stage. The growth tracking unit can also analyze the photos taken and visually display the progress of the growth. For example, the growth tracking unit arranges the photos taken in chronological order and displays the progress of the growth as a graph or chart. In this way, the progress of the growth can be recorded in detail by taking photos of the plant periodically. Some or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth tracking unit can input the captured photo data to the generation AI, which can analyze the data to determine the progress of the growth.

[0039] The plant identification unit can improve the identification accuracy based on the user's past identification history when identifying a plant. The plant identification unit can improve the identification accuracy based on the user's past identification history when identifying a plant, for example. For example, the plant identification unit can improve the accuracy of identifying the same type of plant by referring to data of a plant previously identified by the user. The plant identification unit can also adjust the identification algorithm by taking into account the growth stage of a plant previously identified by the user. The plant identification unit can also improve the identification accuracy by referring to the environmental conditions of a plant previously identified by the user. As a result, the identification accuracy can be improved by referring to the past identification history. A part or all of the above-mentioned processing in the plant identification unit can be performed, for example, using AI or can be performed without using AI. For example, the plant identification unit can input past identification data to the generation AI, and the generation AI can analyze the data to improve the identification accuracy.

[0040] The plant identification unit can apply different identification algorithms according to the growth stage of the plant when identifying the plant. For example, the plant identification unit applies different identification algorithms according to the growth stage of the plant when identifying the plant. For example, when the plant is in the germination stage, the plant identification unit applies an algorithm that identifies characteristics specific to germination. In addition, the plant identification unit can also apply an algorithm that identifies characteristics of leaves and stems when the plant is in the growth stage. In addition, the plant identification unit can also apply an algorithm that identifies the shape and color of flowers when the plant is in the flowering stage. In this way, by applying an identification algorithm according to the growth stage of the plant, it is possible to improve the identification accuracy. Part or all of the above-mentioned processing in the plant identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the plant identification unit can input plant growth stage data to the generation AI, which can analyze the data and apply the identification algorithm.

[0041] The plant identification unit can preferentially identify plants specific to a region in consideration of the geographical location information of the user when identifying a plant. For example, the plant identification unit preferentially identifies plants specific to a region in consideration of the geographical location information of the user when identifying a plant. For example, when the user is in a specific region, the plant identification unit preferentially identifies plants specific to the region. In addition, when the user is traveling, the plant identification unit can also identify plants specific to the region of the travel destination. In addition, when the user is at home, the plant identification unit can also identify plants specific to the region around the user's home. In this way, by considering the geographical location information of the user, plants specific to a region can be preferentially identified. Part or all of the above-mentioned processing in the plant identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the plant identification unit can input the geographical location data of the user to the generation AI, and the generation AI can analyze the data to identify plants specific to the region.

[0042] The plant identification unit can analyze the social media activity of the user at the time of plant identification and identify related plants. For example, the plant identification unit can analyze the social media activity of the user at the time of plant identification and identify related plants. For example, the plant identification unit can analyze photos of plants shared by the user on social media and identify plants of the same type. The plant identification unit can also refer to information on gardening accounts that the user follows on social media and identify related plants. The plant identification unit can also analyze information on gardening communities that the user participates in on social media and identify related plants. In this way, related plants can be identified by analyzing the user's social media activity. Part or all of the above-mentioned processing in the plant identification unit may be performed, for example, using AI or may be performed without using AI. For example, the plant identification unit can input the user's social media data to the generation AI, and the generation AI can analyze the data to identify related plants.

[0043] The soil analysis unit can improve the analysis accuracy by referring to past soil data during soil analysis. The soil analysis unit can improve the analysis accuracy by referring to past soil data during soil analysis, for example. For example, the soil analysis unit can improve the accuracy when analyzing the same type of soil by referring to soil data previously analyzed by the user. The soil analysis unit can also adjust the analysis algorithm by taking into account the nutritional state of the soil previously analyzed by the user. The soil analysis unit can also improve the analysis accuracy by referring to the environmental conditions of the soil previously analyzed by the user. As a result, the analysis accuracy can be improved by referring to the past soil data. A part or all of the above-mentioned processing in the soil analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the soil analysis unit can input past soil data to the generation AI, and the generation AI can analyze the data to improve the analysis accuracy.

[0044] The soil analysis unit can apply different analysis algorithms depending on the type of soil during soil analysis. For example, the soil analysis unit applies different analysis algorithms depending on the type of soil during soil analysis. For example, when the soil is sandy, the soil analysis unit applies an algorithm for analyzing characteristics specific to sandy soil. In addition, when the soil is clayey, the soil analysis unit can also apply an algorithm for analyzing characteristics specific to clayey soil. In addition, when the soil is loamy, the soil analysis unit can also apply an algorithm for analyzing characteristics specific to loamy soil. Thereby, by applying an analysis algorithm according to the type of soil, the analysis accuracy can be improved. Part or all of the above-mentioned processing in the soil analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the soil analysis unit can input soil type data to the generation AI, which can analyze the data and apply the analysis algorithm.

[0045] The soil analysis unit can preferentially analyze region-specific soil characteristics in consideration of the geographical location information of the user during soil analysis. For example, the soil analysis unit preferentially analyzes region-specific soil characteristics in consideration of the geographical location information of the user during soil analysis. For example, when the user is in a specific region, the soil analysis unit preferentially analyzes soil characteristics specific to that region. In addition, when the user is traveling, the soil analysis unit can also analyze soil characteristics specific to the region of the travel destination. In addition, when the user is at home, the soil analysis unit can also analyze soil characteristics specific to the region around the home. In this way, by considering the geographical location information of the user, it is possible to preferentially analyze region-specific soil characteristics. Part or all of the above-mentioned processing in the soil analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the soil analysis unit can input the geographical location data of the user to the generation AI, and the generation AI can analyze the data to analyze region-specific soil characteristics.

[0046] The soil analysis unit can analyze the social media activity of the user during soil analysis and obtain related soil information. For example, the soil analysis unit can analyze the social media activity of the user during soil analysis and obtain related soil information. For example, the soil analysis unit can analyze a photo of soil shared by the user on social media and analyze the same type of soil. The soil analysis unit can also refer to information on a gardening account that the user follows on social media and obtain related soil information. The soil analysis unit can also analyze information on a gardening community that the user participates in on social media and obtain related soil information. In this way, the related soil information can be obtained by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the soil analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soil analysis unit can input the social media data of the user to the generation AI, and the generation AI can analyze the data to obtain related soil information.

[0047] The suggestion unit can adjust the level of detail of the proposal based on the importance of the plant at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the plant at the time of suggestion. For example, the suggestion unit proposes a detailed management method for an important plant. The suggestion unit can also propose a basic management method for a general plant. The suggestion unit can also make a customized proposal for a plant in which the user is particularly interested. This makes it possible to provide a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the plant. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the plant to the generation AI, and the generation AI can analyze the data and adjust the level of detail of the proposal.

[0048] The suggestion unit can apply different proposal algorithms depending on the category of the plant when making a proposal. For example, the suggestion unit applies different proposal algorithms depending on the category of the plant when making a proposal. For example, the suggestion unit applies an algorithm that proposes a flower management method to a flowering plant. The suggestion unit can also apply an algorithm that proposes a vegetable management method to a vegetable plant. The suggestion unit can also apply an algorithm that proposes a foliage plant management method to a foliage plant. In this way, by applying a proposal algorithm according to the category of the plant, a more appropriate proposal can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input plant category data to the generation AI, which can analyze the data and apply the proposal algorithm.

[0049] The suggestion unit can determine the priority of the proposal based on the growth stage of the plant at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the growth stage of the plant at the time of proposal. For example, the suggestion unit preferentially suggests a management method necessary for germination for a plant in the germination stage. The suggestion unit can also preferentially suggest a management method that promotes growth for a plant in the growth stage. The suggestion unit can also preferentially suggest a management method that maintains flowering for a plant in the flowering stage. In this way, by determining the priority of the proposal based on the growth stage of the plant, more appropriate proposals can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input plant growth stage data to the generation AI, and the generation AI can analyze the data to determine the priority of the proposal.

[0050] The suggestion unit can adjust the order of the suggestions based on the relevance of the plants when making suggestions. The suggestion unit, for example, adjusts the order of the suggestions based on the relevance of the plants when making suggestions. For example, the suggestion unit continuously suggests related management methods for plants of the same type. Also, the suggestion unit can preferentially suggest highly related management methods for plants of different types. Also, the suggestion unit can make a suggestion first for a plant in which the user is particularly interested. In this way, by adjusting the order of the suggestions based on the relevance of the plants, more appropriate suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input plant relevance data to the generation AI, and the generation AI can analyze the data and adjust the order of the suggestions.

[0051] The disease or pest identification unit can improve the identification accuracy by referring to past disease or pest data when identifying a disease or pest. The disease or pest identification unit can improve the identification accuracy by referring to past disease or pest data when identifying a disease or pest. For example, the disease or pest identification unit can improve the accuracy of identifying the same type of disease or pest by referring to data of a disease or pest identified by a user in the past. The disease or pest identification unit can also adjust the identification algorithm by taking into account the occurrence conditions of a disease or pest identified by a user in the past. The disease or pest identification unit can also improve the identification accuracy by referring to the environmental conditions of a disease or pest identified by a user in the past. Thereby, the identification accuracy can be improved by referring to the past disease or pest data. A part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI or may be performed without using AI. For example, the disease or pest identification unit can input past disease or pest data to the generation AI, and the generation AI can analyze the data to improve the identification accuracy.

[0052] The disease or pest identification unit can apply different identification algorithms depending on the type of plant when identifying a disease or pest. For example, the disease or pest identification unit applies different identification algorithms depending on the type of plant when identifying a disease or pest. For example, the disease or pest identification unit applies an algorithm for identifying a disease or pest specific to a flower plant to a floricultural plant. In addition, the disease or pest identification unit can also apply an algorithm for identifying a disease or pest specific to a vegetable plant to a vegetable plant. In addition, the disease or pest identification unit can also apply an algorithm for identifying a disease or pest specific to a foliage plant to a houseplant. Thereby, by applying an identification algorithm according to the type of plant, it is possible to improve the identification accuracy. Part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the disease or pest identification unit can input plant type data to the generation AI, which can analyze the data and apply the identification algorithm.

[0053] The disease or pest identification unit can preferentially identify region-specific diseases or pests in consideration of the geographical location information of the user when identifying a disease or pest. For example, the disease or pest identification unit preferentially identifies region-specific diseases or pests in consideration of the geographical location information of the user when identifying a disease or pest. For example, when the user is in a specific region, the disease or pest identification unit preferentially identifies diseases or pests specific to that region. In addition, when the user is traveling, the disease or pest identification unit can also identify diseases or pests specific to the region of the travel destination. In addition, when the user is at home, the disease or pest identification unit can also identify diseases or pests specific to the region around the home. In this way, by considering the geographical location information of the user, it is possible to preferentially identify region-specific diseases or pests. Part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the disease or pest identification unit inputs the geographical location data of the user to the generation AI, and the generation AI can analyze the data to identify region-specific diseases or pests.

[0054] The disease or pest identification unit can analyze the social media activity of the user at the time of disease or pest identification and obtain related disease or pest information. For example, the disease or pest identification unit can analyze the social media activity of the user at the time of disease or pest identification and obtain related disease or pest information. For example, the disease or pest identification unit can analyze a photo of a plant shared by the user on social media and identify the same type of disease or pest. The disease or pest identification unit can also refer to information of a gardening account that the user follows on social media and obtain related disease or pest information. The disease or pest identification unit can also analyze information of a gardening community that the user participates in on social media and obtain related disease or pest information. In this way, the related disease or pest information can be obtained by analyzing the user's social media activity. Part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI or may be performed without using AI. For example, the disease or pest identification unit can input the user's social media data to the generation AI, and the generation AI can analyze the data to obtain related disease or pest information.

[0055] The growth tracking unit can improve the tracking accuracy by referring to past growth data during growth tracking. The growth tracking unit can improve the tracking accuracy by referring to past growth data during growth tracking, for example. For example, the growth tracking unit can improve the accuracy when tracking the same type of plant by referring to the growth data of the plant tracked by the user in the past. The growth tracking unit can also adjust the tracking algorithm by taking into account the growth stage of the plant tracked by the user in the past. The growth tracking unit can also improve the tracking accuracy by referring to the environmental conditions of the plant tracked by the user in the past. In this way, the tracking accuracy can be improved by referring to the past growth data. A part or all of the above-mentioned processing in the growth tracking unit can be performed, for example, using AI or can be performed without using AI. For example, the growth tracking unit can input the past growth data to the generation AI, and the generation AI can analyze the data to improve the tracking accuracy.

[0056] The growth tracking unit can apply different tracking algorithms according to the growth stage of the plant during growth tracking. The growth tracking unit, for example, applies different tracking algorithms according to the growth stage of the plant during growth tracking. For example, when the plant is in the germination stage, the growth tracking unit applies an algorithm that tracks characteristics specific to germination. In addition, when the plant is in the growth stage, the growth tracking unit can also apply an algorithm that tracks characteristics of leaves and stems. In addition, when the plant is in the flowering stage, the growth tracking unit can also apply an algorithm that tracks the shape and color of flowers. In this way, by applying a tracking algorithm according to the growth stage of the plant, the tracking accuracy can be improved. Part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth tracking unit can input the growth stage data of the plant to the generation AI, which can analyze the data and apply the tracking algorithm.

[0057] The growth tracking unit can preferentially track region-specific growth characteristics in consideration of the geographical location information of the user during growth tracking. For example, the growth tracking unit preferentially tracks region-specific growth characteristics in consideration of the geographical location information of the user during growth tracking. For example, when the user is in a specific region, the growth tracking unit preferentially tracks growth characteristics specific to that region. In addition, when the user is traveling, the growth tracking unit can also track growth characteristics specific to the region of the travel destination. In addition, when the user is at home, the growth tracking unit can also track growth characteristics specific to the region around the home. In this way, region-specific growth characteristics can be preferentially tracked by considering the geographical location information of the user. Part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth tracking unit can input the geographical location data of the user to the generation AI, and the generation AI can analyze the data to track region-specific growth characteristics.

[0058] The growth tracking unit can analyze the user's social media activity during growth tracking and acquire related growth information. The growth tracking unit can, for example, analyze the user's social media activity during growth tracking and acquire related growth information. For example, the growth tracking unit can analyze a photo of a plant shared by the user on social media and acquire growth information of the same type of plant. The growth tracking unit can also refer to information of a gardening account that the user follows on social media and acquire related growth information. The growth tracking unit can also analyze information of a gardening community that the user participates in on social media and acquire related growth information. In this way, the related growth information can be acquired by analyzing the user's social media activity. A part or all of the above-mentioned processing in the growth tracking unit can be performed, for example, using AI or can be performed without using AI. For example, the growth tracking unit can input the user's social media data to the generation AI, and the generation AI can analyze the data to acquire related growth information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The AI ​​gardening assistant system may further include a plant growth prediction unit. The growth prediction unit analyzes the growth data of the plant and predicts future growth. For example, the growth prediction unit predicts how the plant will grow based on past growth data and current environmental conditions. The growth prediction unit may also detect abnormal growth patterns and issue a warning to the user. Furthermore, the growth prediction unit may suggest appropriate management methods based on the predicted growth. This allows the user to take appropriate measures in anticipation of future growth.

[0061] The AI ​​gardening assistant system can further include a plant health check unit. The health check unit periodically checks the health of the plant and detects abnormalities. For example, the health check unit can analyze the color and shape of the plant's leaves to detect signs of disease or nutritional deficiency. The health check unit can also monitor the plant's growth rate and detect abnormal delays or rapid growth. Furthermore, the health check unit can suggest appropriate measures based on the detected abnormalities. This allows the user to know the appropriate management method to maintain the health of the plant.

[0062] The AI ​​gardening assistant system can further include a plant growth simulation unit. The growth simulation unit simulates the growth of a plant and predicts future growth. For example, the growth simulation unit simulates how a plant will grow based on past growth data and current environmental conditions. The growth simulation unit can also try different management methods and simulate their effects. Furthermore, the growth simulation unit can suggest appropriate management methods based on the simulation results. This allows the user to take appropriate measures in anticipation of future growth.

[0063] The AI ​​gardening assistant system may further include a plant data analysis unit. The data analysis unit analyzes the plant growth data and environmental data and generates a detailed report. For example, the data analysis unit may analyze the growth rate and health condition of the plant and provide a report to the user. The data analysis unit may also analyze the effect on the plant growth according to changes in environmental conditions. Furthermore, the data analysis unit may compare past data with current data and predict future growth. This allows the user to obtain detailed information on the plant growth.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The plant identification unit identifies the type of plant. The user takes a photo of the plant with their smartphone and inputs the photo into the AI. The AI ​​uses image analysis technology to identify the type of plant. For example, it uses an image classification algorithm using deep learning to extract and identify the plant's characteristics. The plant identification unit can also refer to data on plants that the user has previously identified to improve the accuracy of the identification. Step 2: The soil analysis unit analyzes the type or nutritional status of the soil based on the type of plant identified by the plant identification unit. Dedicated sensors are used to measure the soil's acidity and nutrient content, and the AI ​​analyzes the measured data to determine the soil's type and nutritional status. For example, a pH sensor is used to measure the soil's acidity, and an EC sensor is used to measure the soil's electrical conductivity. An NPK sensor can also be used to measure the nitrogen, phosphorus, and potassium content in the soil. Step 3: The proposal unit proposes a plant management method based on the information analyzed by the soil analysis unit. It suggests watering frequency and type of fertilizer based on the soil condition and type of plant. The AI ​​can also refer to past data to suggest optimal management methods. For example, it suggests different management methods depending on the growth stage of the plant.

[0066] (Example 2) The AI ​​gardening assistant system according to the embodiment of the present invention is a system that can be used by gardening beginners and professionals alike. This system identifies the type of plant, analyzes the type or nutritional state of the soil, and proposes the optimal plant management method. For example, a user takes a photo of a plant with a smartphone and inputs the photo into the AI. The AI ​​identifies the type of plant using image analysis technology. This allows the user to easily know the type of plant he or she is growing. Next, the type or nutritional state of the soil is analyzed based on the identified type of plant. For example, a dedicated sensor is used to measure the acidity and nutrient content of the soil. The AI ​​analyzes the measured data and determines the type of soil and nutritional state. This allows the user to know the soil condition suitable for the plant. Furthermore, a plant management method is proposed based on the analyzed information. For example, the AI ​​suggests the frequency of watering and the type of fertilizer based on the soil condition and the type of plant. This allows the user to know the optimal management method for maintaining the health of the plant. It is also possible to identify diseases and pests based on the information analyzed by the soil analysis unit. For example, the AI ​​identifies plant diseases and pests using image analysis technology. This allows the user to discover plant diseases and pests early and take appropriate measures. In addition, it is also possible to track the growth of plants. For example, the AI ​​will periodically take photos of the plants and record their growth progress. This allows the user to track the growth of the plants in detail and adjust the necessary management methods. This system allows anyone, from gardening beginners to professionals, to easily manage plants. For example, beginners can grow healthy plants by following the AI's suggestions to care for them. Professionals can also practice more advanced management methods by referring to the AI's analysis results. This allows the AI ​​gardening assistant system to be used by everyone, from gardening beginners to professionals, by identifying the type of plant, analyzing the soil type or nutritional state, and suggesting the optimal management method.

[0067] The AI ​​gardening assistant system according to the embodiment includes a plant identification unit, a soil analysis unit, and a suggestion unit. The plant identification unit identifies the type of plant. For example, the user takes a photo of a plant with a smartphone and inputs the photo to the AI. The AI ​​identifies the type of plant using image analysis technology. For example, the AI ​​uses an image classification algorithm using deep learning to extract and identify the characteristics of the plant. The plant identification unit can also refer to data on plants previously identified by the user to improve the accuracy of identification. The soil analysis unit analyzes the type or nutritional state of the soil based on the type of plant identified by the plant identification unit. The soil analysis unit measures the acidity and nutrient content of the soil using, for example, a dedicated sensor. The AI ​​analyzes the measured data and determines the type and nutritional state of the soil. For example, the soil analysis unit measures the acidity of the soil using a pH sensor and measures the electrical conductivity of the soil using an EC sensor. The soil analysis unit can also measure the nitrogen, phosphorus, and potassium content in the soil using an NPK sensor. The suggestion unit suggests a plant management method based on the information analyzed by the soil analysis unit. The suggestion unit suggests watering frequency and type of fertilizer based on, for example, soil condition and plant type. The AI ​​can also suggest optimal management methods by referring to past data. For example, the suggestion unit suggests different management methods depending on the growth stage of the plant. This allows the AI ​​gardening assistant system to identify the type of plant, analyze the soil type or nutritional state, and suggest the optimal management method.

[0068] The plant identification unit identifies the type of plant. For example, the user takes a photo of a plant with a smartphone and inputs the photo into the AI. The AI ​​uses image analysis technology to identify the type of plant. Specifically, the user takes a photo of the plant using the smartphone camera and uploads the image to a dedicated application. The application sends the image to an AI server on the cloud, and an image classification algorithm using deep learning analyzes the image. This algorithm is trained in advance with a large amount of plant image data set, and extracts features such as leaf shape, color, pattern, and flower structure to perform identification. For example, the AI ​​analyzes the shape of the leaf edge, the pattern of the vein, the color and shape of the flower, etc., and identifies the type of plant based on these features. In addition, the plant identification unit can also refer to the data of plants that the user has previously identified to improve the identification accuracy. As a result, when identifying the same plant again, the past data can be used to identify it more quickly and accurately. In addition, the plant identification unit also has a function that allows the user to provide feedback on the identification result, and if the user confirms that the identification result is correct, the information is incorporated as learning data for the AI, contributing to improving the identification accuracy. In this way, the plant identification unit enables the user to easily identify the type of plant, thereby improving the efficiency and enjoyment of gardening.

[0069] The soil analysis unit analyzes the type or nutritional state of the soil based on the type of plant identified by the plant identification unit. The soil analysis unit, for example, measures the acidity and nutrient content of the soil using a dedicated sensor. Specifically, the user inserts a soil sample into a dedicated sensor device, and the device measures the soil's pH value, electrical conductivity (EC), and the content of major nutrients (nitrogen, phosphorus, and potassium). These sensor devices connect to smartphones and tablets via Bluetooth or Wi-Fi and transmit the measurement data to an application in real time. The AI ​​analyzes the measured data and determines the type and nutritional state of the soil. For example, the soil analysis unit measures the acidity of the soil using a pH sensor and measures the electrical conductivity of the soil using an EC sensor. This makes it possible to determine whether the acidity of the soil is suitable for plant growth. It is also possible to measure the content of nitrogen, phosphorus, and potassium in the soil using an NPK sensor. This makes it possible to check whether the nutrients required by the plants are being supplied sufficiently. In addition, the soil analysis unit can provide more accurate analysis results by referring to past measurement data and a regional soil characteristics database. This allows users to accurately grasp the condition of the soil and take appropriate measures to maintain the health of their plants.

[0070] The suggestion unit proposes a management method for the plant based on the information analyzed by the soil analysis unit. The suggestion unit, for example, suggests the frequency of watering and the type of fertilizer based on the soil condition and the type of plant. Specifically, the AI ​​calculates the optimal management method taking into account the soil pH value and nutrient content, the type of plant, and the growth stage. For example, if the soil is acidic, the AI ​​suggests adding lime to adjust the pH. Also, if nitrogen in the soil is lacking, it recommends adding nitrogen fertilizer. Furthermore, the suggestion unit suggests different management methods depending on the growth stage of the plant. For example, frequent watering is necessary during the germination stage, but it is recommended to reduce the frequency of watering during the growth stage and add certain nutrients during the flowering stage. The AI ​​can also make suggestions based on management methods that have been successful under similar conditions by referring to past data. This allows the suggestion unit to provide specific advice to the user to maintain the health of the plant and promote optimal growth. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. For example, by inputting the results of management performed by the user according to the suggestions into the application, the AI ​​can learn from the data and improve the accuracy of future suggestions. This enables the suggestion unit to always provide the user with the latest and most optimal management techniques, helping them achieve success in gardening.

[0071] The soil analysis unit can measure the acidity or nutrient content of the soil using a dedicated sensor. The soil analysis unit can measure the acidity of the soil using, for example, a pH sensor. For example, the soil analysis unit inserts a pH sensor into the soil and measures the pH value. The soil analysis unit can also measure the electrical conductivity of the soil using an EC sensor. For example, the soil analysis unit inserts an EC sensor into the soil and measures the electrical conductivity. The soil analysis unit can also measure the nitrogen, phosphorus, and potassium content in the soil using an NPK sensor. For example, the soil analysis unit inserts an NPK sensor into the soil and measures the nitrogen, phosphorus, and potassium content. As a result, the acidity and nutrient content of the soil can be accurately measured by using a dedicated sensor. Some or all of the above-mentioned processing in the soil analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the soil analysis unit can input data acquired by the sensor to the generation AI, which can analyze the data to determine the condition of the soil.

[0072] The soil analysis unit may include a disease or pest identification unit that identifies a disease or pest based on the information analyzed by the soil analysis unit. The disease or pest identification unit may, for example, identify a disease or pest based on the information analyzed by the soil analysis unit. For example, the disease or pest identification unit may determine the risk of occurrence of a particular disease or pest based on the nutritional state or acidity of the soil. The disease or pest identification unit may also identify plant diseases and pests using image analysis technology. For example, the disease or pest identification unit may analyze images of the leaves and stems of the plant to detect signs of disease or pests. This allows early measures to be taken by identifying diseases and pests based on the results of the soil analysis. Some or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI or may be performed without using AI. For example, the disease or pest identification unit may input data acquired by the soil analysis unit to the generation AI, and the generation AI may analyze the data to determine the risk of disease or pest.

[0073] The disease or pest identification unit can identify plant diseases or pests using image analysis technology. The disease or pest identification unit can, for example, identify plant diseases or pests using image analysis technology. For example, the disease or pest identification unit takes an image of the leaves and stems of a plant and detects signs of disease or pests using image analysis technology. For example, the disease or pest identification unit uses an image classification algorithm using deep learning to extract and identify the characteristics of plant diseases and pests. The disease or pest identification unit can also refer to past data to improve the accuracy of identification. As a result, plant diseases and pests can be accurately identified using image analysis technology. Some or all of the above-mentioned processing in the disease or pest identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the disease or pest identification unit can input image data of a plant to a generation AI, and the generation AI can analyze the data to detect signs of disease or pests.

[0074] The plant growth tracking unit may include a growth tracking unit that tracks the growth of the plant based on the information identified by the disease or pest identification unit. The growth tracking unit tracks the growth of the plant based on, for example, the information identified by the disease or pest identification unit. For example, the growth tracking unit records the growth stage and health condition of the plant and proposes an appropriate management method. The growth tracking unit may also periodically take photos of the plant and record the progress of the growth. For example, the growth tracking unit measures the height of the plant and the number of leaves and tracks the progress of the growth. In this way, an appropriate management method can be provided by tracking the growth of the plant based on the identification result of the disease or pest. A part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI or may be performed without using AI. For example, the growth tracking unit inputs the growth data of the plant to the generation AI, and the generation AI analyzes the data to determine the progress of the growth.

[0075] The growth tracking unit can measure the height or number of leaves of a plant using image analysis technology. The growth tracking unit can measure the height or number of leaves of a plant using, for example, image analysis technology. For example, the growth tracking unit takes a photo of the plant and measures the height of the plant using image analysis technology. For example, the growth tracking unit uses an image analysis algorithm using deep learning to measure the height of the plant. The growth tracking unit can also measure the number of leaves of the plant. For example, the growth tracking unit takes a photo of the plant and counts the number of leaves using image analysis technology. As a result, the growth of the plant can be accurately tracked using image analysis technology. Some or all of the above-mentioned processing in the growth tracking unit can be performed using, for example, AI, or can be performed without using AI. For example, the growth tracking unit can input image data of the plant to the generation AI, and the generation AI can analyze the data to measure the height or number of leaves of the plant.

[0076] The growth tracking unit can periodically take photos of the plant and record the progress of the growth. The growth tracking unit, for example, periodically takes photos of the plant and records the progress of the growth. For example, the growth tracking unit takes photos of the plant every week and records the progress of the growth. The growth tracking unit can also adjust the shooting frequency according to the growth stage of the plant. For example, the growth tracking unit takes photos every day at the germination stage, every week at the growth stage, and every month at the flowering stage. The growth tracking unit can also analyze the photos taken and visually display the progress of the growth. For example, the growth tracking unit arranges the photos taken in chronological order and displays the progress of the growth as a graph or chart. In this way, the progress of the growth can be recorded in detail by taking photos of the plant periodically. Some or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth tracking unit can input the captured photo data to the generation AI, which can analyze the data to determine the progress of the growth.

[0077] The plant identification unit can determine the user's emotion and adjust the timing of plant identification based on the determined user's emotion. The plant identification unit, for example, estimates the user's emotion and adjusts the timing of plant identification based on the estimated user's emotion. For example, when the user is stressed, the plant identification unit delays the timing of plant identification and performs the identification in a relaxed state. In addition, when the user is excited, the plant identification unit can immediately perform plant identification to keep the user's interest. In addition, when the user is tired, the plant identification unit can postpone the identification to the next day so that the user can take a rest. In this way, by adjusting the timing of plant identification according to the user's emotion, plant identification can be performed at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plant identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plant identification unit can input the user's facial expression data into the generation AI, which can then analyze the data to infer the user's emotions.

[0078] The plant identification unit can improve the identification accuracy based on the user's past identification history when identifying a plant. The plant identification unit can improve the identification accuracy based on the user's past identification history when identifying a plant, for example. For example, the plant identification unit can improve the accuracy of identifying the same type of plant by referring to data of a plant previously identified by the user. The plant identification unit can also adjust the identification algorithm by taking into account the growth stage of a plant previously identified by the user. The plant identification unit can also improve the identification accuracy by referring to the environmental conditions of a plant previously identified by the user. As a result, the identification accuracy can be improved by referring to the past identification history. A part or all of the above-mentioned processing in the plant identification unit can be performed, for example, using AI or can be performed without using AI. For example, the plant identification unit can input past identification data to the generation AI, and the generation AI can analyze the data to improve the identification accuracy.

[0079] The plant identification unit can apply different identification algorithms according to the growth stage of the plant when identifying the plant. For example, the plant identification unit applies different identification algorithms according to the growth stage of the plant when identifying the plant. For example, when the plant is in the germination stage, the plant identification unit applies an algorithm that identifies characteristics specific to germination. In addition, the plant identification unit can also apply an algorithm that identifies characteristics of leaves and stems when the plant is in the growth stage. In addition, the plant identification unit can also apply an algorithm that identifies the shape and color of flowers when the plant is in the flowering stage. In this way, by applying an identification algorithm according to the growth stage of the plant, it is possible to improve the identification accuracy. Part or all of the above-mentioned processing in the plant identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the plant identification unit can input plant growth stage data to the generation AI, which can analyze the data and apply the identification algorithm.

[0080] The plant identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. The plant identification unit, for example, estimates the user's emotion and adjusts the display method of the identification result based on the estimated user's emotion. For example, the plant identification unit provides a simple and highly visible display method when the user is nervous. The plant identification unit can also provide a display method including detailed information when the user is relaxed. The plant identification unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide a more appropriate display method by adjusting the display method of the identification result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the plant identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plant identification unit inputs the user's facial expression data to the generation AI, which analyzes the data to estimate the user's emotion and adjusts the display method.

[0081] The plant identification unit can preferentially identify plants specific to a region in consideration of the geographical location information of the user when identifying a plant. For example, the plant identification unit preferentially identifies plants specific to a region in consideration of the geographical location information of the user when identifying a plant. For example, when the user is in a specific region, the plant identification unit preferentially identifies plants specific to the region. In addition, when the user is traveling, the plant identification unit can also identify plants specific to the region of the travel destination. In addition, when the user is at home, the plant identification unit can also identify plants specific to the region around the user's home. In this way, by considering the geographical location information of the user, plants specific to a region can be preferentially identified. Part or all of the above-mentioned processing in the plant identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the plant identification unit can input the geographical location data of the user to the generation AI, and the generation AI can analyze the data to identify plants specific to the region.

[0082] The plant identification unit can analyze the social media activity of the user at the time of plant identification and identify related plants. For example, the plant identification unit can analyze the social media activity of the user at the time of plant identification and identify related plants. For example, the plant identification unit can analyze photos of plants shared by the user on social media and identify plants of the same type. The plant identification unit can also refer to information on gardening accounts that the user follows on social media and identify related plants. The plant identification unit can also analyze information on gardening communities that the user participates in on social media and identify related plants. In this way, related plants can be identified by analyzing the user's social media activity. Part or all of the above-mentioned processing in the plant identification unit may be performed, for example, using AI or may be performed without using AI. For example, the plant identification unit can input the user's social media data to the generation AI, and the generation AI can analyze the data to identify related plants.

[0083] The soil analysis unit can estimate the user's emotion and adjust the timing of the soil analysis based on the estimated user's emotion. The soil analysis unit, for example, estimates the user's emotion and adjusts the timing of the soil analysis based on the estimated user's emotion. For example, when the user is stressed, the soil analysis unit delays the timing of the soil analysis and performs the analysis in a relaxed state. In addition, when the user is excited, the soil analysis unit can immediately perform the soil analysis to keep the user interested. In addition, when the user is tired, the soil analysis unit can postpone the analysis to the next day so that the user can rest. In this way, the soil analysis can be performed at a more appropriate timing by adjusting the timing of the soil analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the soil analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the soil analysis unit can input the user's facial expression data into the generation AI, which can then analyze the data to infer the user's emotions and adjust the timing of the soil analysis.

[0084] The soil analysis unit can improve the analysis accuracy by referring to past soil data during soil analysis. The soil analysis unit can improve the analysis accuracy by referring to past soil data during soil analysis, for example. For example, the soil analysis unit can improve the accuracy when analyzing the same type of soil by referring to soil data previously analyzed by the user. The soil analysis unit can also adjust the analysis algorithm by taking into account the nutritional state of the soil previously analyzed by the user. The soil analysis unit can also improve the analysis accuracy by referring to the environmental conditions of the soil previously analyzed by the user. As a result, the analysis accuracy can be improved by referring to the past soil data. A part or all of the above-mentioned processing in the soil analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the soil analysis unit can input past soil data to the generation AI, and the generation AI can analyze the data to improve the analysis accuracy.

[0085] The soil analysis unit can apply different analysis algorithms depending on the type of soil during soil analysis. For example, the soil analysis unit applies different analysis algorithms depending on the type of soil during soil analysis. For example, when the soil is sandy, the soil analysis unit applies an algorithm for analyzing characteristics specific to sandy soil. In addition, when the soil is clayey, the soil analysis unit can also apply an algorithm for analyzing characteristics specific to clayey soil. In addition, when the soil is loamy, the soil analysis unit can also apply an algorithm for analyzing characteristics specific to loamy soil. Thereby, by applying an analysis algorithm according to the type of soil, the analysis accuracy can be improved. Part or all of the above-mentioned processing in the soil analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the soil analysis unit can input soil type data to the generation AI, which can analyze the data and apply the analysis algorithm.

[0086] The soil analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The soil analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, the soil analysis unit provides a simple and highly visible display method when the user is nervous. In addition, the soil analysis unit can also provide a display method including detailed information when the user is relaxed. In addition, the soil analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This makes it possible to provide a more appropriate display method by adjusting the display method of the analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the soil analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the soil analysis unit inputs the user's facial expression data to the generation AI, which analyzes the data to estimate the user's emotion and adjusts the display method.

[0087] The soil analysis unit can preferentially analyze region-specific soil characteristics in consideration of the geographical location information of the user during soil analysis. For example, the soil analysis unit preferentially analyzes region-specific soil characteristics in consideration of the geographical location information of the user during soil analysis. For example, when the user is in a specific region, the soil analysis unit preferentially analyzes soil characteristics specific to that region. In addition, when the user is traveling, the soil analysis unit can also analyze soil characteristics specific to the region of the travel destination. In addition, when the user is at home, the soil analysis unit can also analyze soil characteristics specific to the region around the home. In this way, by considering the geographical location information of the user, it is possible to preferentially analyze region-specific soil characteristics. Part or all of the above-mentioned processing in the soil analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the soil analysis unit can input the geographical location data of the user to the generation AI, and the generation AI can analyze the data to analyze region-specific soil characteristics.

[0088] The soil analysis unit can analyze the social media activity of the user during soil analysis and obtain related soil information. For example, the soil analysis unit can analyze the social media activity of the user during soil analysis and obtain related soil information. For example, the soil analysis unit can analyze a photo of soil shared by the user on social media and analyze the same type of soil. The soil analysis unit can also refer to information on a gardening account that the user follows on social media and obtain related soil information. The soil analysis unit can also analyze information on a gardening community that the user participates in on social media and obtain related soil information. In this way, the related soil information can be obtained by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the soil analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soil analysis unit can input the social media data of the user to the generation AI, and the generation AI can analyze the data to obtain related soil information.

[0089] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the expression method of the proposal based on the estimated user's emotion. For example, the suggestion unit provides a simple and highly visible proposal method when the user is nervous. The suggestion unit can also provide a proposal method including detailed information when the user is relaxed. The suggestion unit can also provide a proposal method that focuses on the main points when the user is in a hurry. This allows the proposal to be adjusted according to the user's emotion, thereby providing a more appropriate proposal. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit inputs the user's facial expression data to the generation AI, which analyzes the data to estimate the user's emotion and adjusts the expression method of the proposal.

[0090] The suggestion unit can adjust the level of detail of the proposal based on the importance of the plant at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the plant at the time of suggestion. For example, the suggestion unit proposes a detailed management method for an important plant. The suggestion unit can also propose a basic management method for a general plant. The suggestion unit can also make a customized proposal for a plant in which the user is particularly interested. This makes it possible to provide a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the plant. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the importance data of the plant to the generation AI, and the generation AI can analyze the data and adjust the level of detail of the proposal.

[0091] The suggestion unit can apply different proposal algorithms depending on the category of the plant when making a proposal. For example, the suggestion unit applies different proposal algorithms depending on the category of the plant when making a proposal. For example, the suggestion unit applies an algorithm that proposes a flower management method to a flowering plant. The suggestion unit can also apply an algorithm that proposes a vegetable management method to a vegetable plant. The suggestion unit can also apply an algorithm that proposes a foliage plant management method to a foliage plant. In this way, by applying a proposal algorithm according to the category of the plant, a more appropriate proposal can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input plant category data to the generation AI, which can analyze the data and apply the proposal algorithm.

[0092] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the proposal based on the estimated user's emotion. For example, when the user is in a hurry, the suggestion unit makes a short and to the point proposal. Also, when the user is relaxed, the suggestion unit can make a longer proposal including a detailed explanation. Also, when the user is excited, the suggestion unit can make a proposal with a visually stimulating effect. In this way, by adjusting the length of the proposal according to the user's emotion, a more appropriate proposal can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI, which can analyze the data to estimate the user's emotion and adjust the length of the proposal.

[0093] The suggestion unit can determine the priority of the proposal based on the growth stage of the plant at the time of proposal. The suggestion unit, for example, determines the priority of the proposal based on the growth stage of the plant at the time of proposal. For example, the suggestion unit preferentially suggests a management method necessary for germination for a plant in the germination stage. The suggestion unit can also preferentially suggest a management method that promotes growth for a plant in the growth stage. The suggestion unit can also preferentially suggest a management method that maintains flowering for a plant in the flowering stage. In this way, by determining the priority of the proposal based on the growth stage of the plant, more appropriate proposals can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input plant growth stage data to the generation AI, and the generation AI can analyze the data to determine the priority of the proposal.

[0094] The suggestion unit can adjust the order of the suggestions based on the relevance of the plants when making suggestions. The suggestion unit, for example, adjusts the order of the suggestions based on the relevance of the plants when making suggestions. For example, the suggestion unit continuously suggests related management methods for plants of the same type. Also, the suggestion unit can preferentially suggest highly related management methods for plants of different types. Also, the suggestion unit can make a suggestion first for a plant in which the user is particularly interested. In this way, by adjusting the order of the suggestions based on the relevance of the plants, more appropriate suggestions can be provided. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input plant relevance data to the generation AI, and the generation AI can analyze the data and adjust the order of the suggestions.

[0095] The disease or pest identification unit can estimate the user's emotions and adjust the timing of disease or pest identification based on the estimated user's emotions. The disease or pest identification unit, for example, estimates the user's emotions and adjusts the timing of disease or pest identification based on the estimated user's emotions. For example, if the user is stressed, the disease or pest identification unit delays the timing of disease or pest identification and performs the identification in a relaxed state. In addition, if the user is excited, the disease or pest identification unit can immediately perform disease or pest identification to keep the user interested. In addition, if the user is tired, the disease or pest identification unit can postpone the identification to the next day so that the user can rest. In this way, by adjusting the timing of disease or pest identification according to the user's emotions, identification can be performed at a more appropriate timing. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the disease or pest identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the disease or pest identification unit inputs the user's facial expression data to the generation AI, which analyzes the data to estimate the user's emotions and adjusts the timing of identification.

[0096] The disease or pest identification unit can improve the identification accuracy by referring to past disease or pest data when identifying a disease or pest. The disease or pest identification unit can improve the identification accuracy by referring to past disease or pest data when identifying a disease or pest. For example, the disease or pest identification unit can improve the accuracy of identifying the same type of disease or pest by referring to data of a disease or pest identified by a user in the past. The disease or pest identification unit can also adjust the identification algorithm by taking into account the occurrence conditions of a disease or pest identified by a user in the past. The disease or pest identification unit can also improve the identification accuracy by referring to the environmental conditions of a disease or pest identified by a user in the past. Thereby, the identification accuracy can be improved by referring to the past disease or pest data. A part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI or may be performed without using AI. For example, the disease or pest identification unit can input past disease or pest data to the generation AI, and the generation AI can analyze the data to improve the identification accuracy.

[0097] The disease or pest identification unit can apply different identification algorithms depending on the type of plant when identifying a disease or pest. For example, the disease or pest identification unit applies different identification algorithms depending on the type of plant when identifying a disease or pest. For example, the disease or pest identification unit applies an algorithm for identifying a disease or pest specific to a flower plant to a floricultural plant. In addition, the disease or pest identification unit can also apply an algorithm for identifying a disease or pest specific to a vegetable plant to a vegetable plant. In addition, the disease or pest identification unit can also apply an algorithm for identifying a disease or pest specific to a foliage plant to a houseplant. Thereby, by applying an identification algorithm according to the type of plant, it is possible to improve the identification accuracy. Part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the disease or pest identification unit can input plant type data to the generation AI, which can analyze the data and apply the identification algorithm.

[0098] The disease or pest identification unit can estimate the user's emotions and adjust the display method of the identification result based on the estimated user's emotions. The disease or pest identification unit, for example, estimates the user's emotions and adjusts the display method of the identification result based on the estimated user's emotions. For example, the disease or pest identification unit provides a simple and highly visible display method when the user is nervous. The disease or pest identification unit can also provide a display method including detailed information when the user is relaxed. The disease or pest identification unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows a more appropriate display method to be provided by adjusting the display method of the identification result according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the disease or pest identification unit can input the user's facial expression data into the generation AI, which can then analyze the data to infer the user's emotions and adjust the display method.

[0099] The disease or pest identification unit can preferentially identify region-specific diseases or pests in consideration of the geographical location information of the user when identifying a disease or pest. For example, the disease or pest identification unit preferentially identifies region-specific diseases or pests in consideration of the geographical location information of the user when identifying a disease or pest. For example, when the user is in a specific region, the disease or pest identification unit preferentially identifies diseases or pests specific to that region. In addition, when the user is traveling, the disease or pest identification unit can also identify diseases or pests specific to the region of the travel destination. In addition, when the user is at home, the disease or pest identification unit can also identify diseases or pests specific to the region around the home. In this way, by considering the geographical location information of the user, it is possible to preferentially identify region-specific diseases or pests. Part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the disease or pest identification unit inputs the geographical location data of the user to the generation AI, and the generation AI can analyze the data to identify region-specific diseases or pests.

[0100] The disease or pest identification unit can analyze the social media activity of the user at the time of disease or pest identification and obtain related disease or pest information. For example, the disease or pest identification unit can analyze the social media activity of the user at the time of disease or pest identification and obtain related disease or pest information. For example, the disease or pest identification unit can analyze a photo of a plant shared by the user on social media and identify the same type of disease or pest. The disease or pest identification unit can also refer to information of a gardening account that the user follows on social media and obtain related disease or pest information. The disease or pest identification unit can also analyze information of a gardening community that the user participates in on social media and obtain related disease or pest information. In this way, the related disease or pest information can be obtained by analyzing the user's social media activity. Part or all of the above-mentioned processing in the disease or pest identification unit may be performed, for example, using AI or may be performed without using AI. For example, the disease or pest identification unit can input the user's social media data to the generation AI, and the generation AI can analyze the data to obtain related disease or pest information.

[0101] The growth tracking unit can estimate the user's emotion and adjust the timing of growth tracking based on the estimated user's emotion. The growth tracking unit, for example, estimates the user's emotion and adjusts the timing of growth tracking based on the estimated user's emotion. For example, when the user is stressed, the growth tracking unit delays the timing of growth tracking and performs tracking in a relaxed state. In addition, when the user is excited, the growth tracking unit can immediately perform growth tracking to keep the user interested. In addition, when the user is tired, the growth tracking unit can postpone tracking to the next day so that the user can rest. In this way, by adjusting the timing of growth tracking according to the user's emotion, growth tracking can be performed at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the growth tracking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the growth tracking unit can input the user's facial expression data into the generation AI, which can then analyze the data to infer the user's emotions and adjust the timing of tracking.

[0102] The growth tracking unit can improve the tracking accuracy by referring to past growth data during growth tracking. The growth tracking unit can improve the tracking accuracy by referring to past growth data during growth tracking, for example. For example, the growth tracking unit can improve the accuracy when tracking the same type of plant by referring to the growth data of the plant tracked by the user in the past. The growth tracking unit can also adjust the tracking algorithm by taking into account the growth stage of the plant tracked by the user in the past. The growth tracking unit can also improve the tracking accuracy by referring to the environmental conditions of the plant tracked by the user in the past. In this way, the tracking accuracy can be improved by referring to the past growth data. A part or all of the above-mentioned processing in the growth tracking unit can be performed, for example, using AI or can be performed without using AI. For example, the growth tracking unit can input the past growth data to the generation AI, and the generation AI can analyze the data to improve the tracking accuracy.

[0103] The growth tracking unit can apply different tracking algorithms according to the growth stage of the plant during growth tracking. The growth tracking unit, for example, applies different tracking algorithms according to the growth stage of the plant during growth tracking. For example, when the plant is in the germination stage, the growth tracking unit applies an algorithm that tracks characteristics specific to germination. In addition, when the plant is in the growth stage, the growth tracking unit can also apply an algorithm that tracks characteristics of leaves and stems. In addition, when the plant is in the flowering stage, the growth tracking unit can also apply an algorithm that tracks the shape and color of flowers. In this way, by applying a tracking algorithm according to the growth stage of the plant, the tracking accuracy can be improved. Part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth tracking unit can input the growth stage data of the plant to the generation AI, which can analyze the data and apply the tracking algorithm.

[0104] The growth tracking unit can estimate the user's emotion and adjust the display method of the growth tracking result based on the estimated user's emotion. The growth tracking unit, for example, estimates the user's emotion and adjusts the display method of the growth tracking result based on the estimated user's emotion. For example, the growth tracking unit provides a simple and highly visible display method when the user is nervous. In addition, the growth tracking unit can also provide a display method including detailed information when the user is relaxed. In addition, the growth tracking unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows a more appropriate display method to be provided by adjusting the display method of the growth tracking result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the growth tracking unit can input a user's facial expression data into the generation AI, which can then analyze the data to infer the user's emotions and adjust the display method.

[0105] The growth tracking unit can preferentially track region-specific growth characteristics in consideration of the geographical location information of the user during growth tracking. For example, the growth tracking unit preferentially tracks region-specific growth characteristics in consideration of the geographical location information of the user during growth tracking. For example, when the user is in a specific region, the growth tracking unit preferentially tracks growth characteristics specific to that region. In addition, when the user is traveling, the growth tracking unit can also track growth characteristics specific to the region of the travel destination. In addition, when the user is at home, the growth tracking unit can also track growth characteristics specific to the region around the home. In this way, region-specific growth characteristics can be preferentially tracked by considering the geographical location information of the user. Part or all of the above-mentioned processing in the growth tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth tracking unit can input the geographical location data of the user to the generation AI, and the generation AI can analyze the data to track region-specific growth characteristics.

[0106] The growth tracking unit can analyze the user's social media activity during growth tracking and acquire related growth information. The growth tracking unit can, for example, analyze the user's social media activity during growth tracking and acquire related growth information. For example, the growth tracking unit can analyze a photo of a plant shared by the user on social media and acquire growth information of the same type of plant. The growth tracking unit can also refer to information of a gardening account that the user follows on social media and acquire related growth information. The growth tracking unit can also analyze information of a gardening community that the user participates in on social media and acquire related growth information. In this way, the related growth information can be acquired by analyzing the user's social media activity. A part or all of the above-mentioned processing in the growth tracking unit can be performed, for example, using AI or can be performed without using AI. For example, the growth tracking unit can input the user's social media data to the generation AI, and the generation AI can analyze the data to acquire related growth information.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The AI ​​gardening assistant system may further include a plant growth prediction unit. The growth prediction unit analyzes the growth data of the plant and predicts future growth. For example, the growth prediction unit predicts how the plant will grow based on past growth data and current environmental conditions. The growth prediction unit may also detect abnormal growth patterns and issue a warning to the user. Furthermore, the growth prediction unit may suggest appropriate management methods based on the predicted growth. This allows the user to take appropriate measures in anticipation of future growth.

[0109] The AI ​​gardening assistant system can further include a plant health check unit. The health check unit periodically checks the health of the plant and detects abnormalities. For example, the health check unit can analyze the color and shape of the plant's leaves to detect signs of disease or nutritional deficiency. The health check unit can also monitor the plant's growth rate and detect abnormal delays or rapid growth. Furthermore, the health check unit can suggest appropriate measures based on the detected abnormalities. This allows the user to know the appropriate management method to maintain the health of the plant.

[0110] The AI ​​gardening assistant system may further include a plant emotion estimation unit. The emotion estimation unit analyzes the condition of the plant and estimates whether the plant is feeling stressed. For example, the emotion estimation unit may analyze the color, shape, and growth rate of the plant's leaves to detect signs of stress. The emotion estimation unit may also identify the cause of the plant's stress by taking into account the state of the soil and environmental conditions. Furthermore, the emotion estimation unit may suggest an appropriate management method based on the estimated emotion. This allows the user to know the appropriate management method according to the plant's emotion.

[0111] The AI ​​gardening assistant system may further include a plant communication unit. The communication unit notifies the user of the plant's condition and engages in a dialogue with the user. For example, the communication unit may notify the user of the plant's health and growth. The communication unit may also provide appropriate answers to questions from the user. Furthermore, the communication unit may estimate the user's emotions and adjust the content and tone of the dialogue based on the estimated emotions. This allows the user to build a closer relationship with the plant through dialogue.

[0112] The AI ​​gardening assistant system may further include a plant nutrition management unit. The nutrition management unit monitors the nutritional state of the plant and suggests appropriate nutritional supplementation. For example, the nutrition management unit may analyze the nutrient content of the soil and identify the nutrients required by the plant. The nutrition management unit may also suggest the appropriate type and amount of fertilizer depending on the growth stage of the plant. Furthermore, the nutrition management unit may estimate the user's emotions and adjust the timing and method of nutritional supplementation based on the estimated emotions. This allows the user to appropriately manage the nutrition of the plant.

[0113] The AI ​​gardening assistant system may further include a plant environmental adaptation unit. The environmental adaptation unit monitors the environmental conditions of the plant and proposes appropriate environmental adjustments. For example, the environmental adaptation unit may analyze environmental conditions such as temperature, humidity, and light intensity to identify the optimal environment for the plant. The environmental adaptation unit may also propose environmental adjustments in response to seasonal and weather changes. Furthermore, the environmental adaptation unit may estimate the user's emotions and adjust the timing and method of environmental adjustments based on the estimated emotions. This allows the user to appropriately manage the plant's environment.

[0114] The AI ​​gardening assistant system can further include a plant growth simulation unit. The growth simulation unit simulates the growth of a plant and predicts future growth. For example, the growth simulation unit simulates how a plant will grow based on past growth data and current environmental conditions. The growth simulation unit can also try different management methods and simulate their effects. Furthermore, the growth simulation unit can suggest appropriate management methods based on the simulation results. This allows the user to take appropriate measures in anticipation of future growth.

[0115] The AI ​​gardening assistant system can further include an automatic plant irrigation unit. The automatic irrigation unit monitors the moisture status of the plants and automatically supplies water at an appropriate time. For example, the automatic irrigation unit analyzes the moisture content of the soil and identifies the amount of moisture required by the plants. The automatic irrigation unit can also adjust the frequency and amount of irrigation depending on the growth stage of the plants and environmental conditions. Furthermore, the automatic irrigation unit can estimate the user's emotions and adjust the timing and method of irrigation based on the estimated emotions. This allows the user to properly manage the moisture of the plants.

[0116] The AI ​​gardening assistant system may further include a plant community collaboration unit. The community collaboration unit allows the user to share information with other gardening enthusiasts and receive advice. For example, the community collaboration unit may share information about plants the user is growing and receive feedback from other users. The community collaboration unit may also analyze information about gardening communities in which the user participates and provide related advice. Furthermore, the community collaboration unit may estimate the user's emotions and adjust the content and tone of communication based on the estimated emotions. This allows the user to collaborate with other gardening enthusiasts and have a better gardening experience.

[0117] The AI ​​gardening assistant system may further include a plant data analysis unit. The data analysis unit analyzes the plant growth data and environmental data and generates a detailed report. For example, the data analysis unit may analyze the growth rate and health condition of the plant and provide a report to the user. The data analysis unit may also analyze the effect on the plant growth according to changes in environmental conditions. Furthermore, the data analysis unit may compare past data with current data and predict future growth. This allows the user to obtain detailed information on the plant growth.

[0118] The process flow of the second embodiment will be briefly described below.

[0119] Step 1: The plant identification unit identifies the type of plant. The user takes a photo of the plant with their smartphone and inputs the photo into the AI. The AI ​​uses image analysis technology to identify the type of plant. For example, it uses an image classification algorithm using deep learning to extract and identify the plant's characteristics. The plant identification unit can also refer to data on plants that the user has previously identified to improve the accuracy of the identification. Step 2: The soil analysis unit analyzes the type or nutritional status of the soil based on the type of plant identified by the plant identification unit. Dedicated sensors are used to measure the soil's acidity and nutrient content, and the AI ​​analyzes the measured data to determine the soil's type and nutritional status. For example, a pH sensor is used to measure the soil's acidity, and an EC sensor is used to measure the soil's electrical conductivity. An NPK sensor can also be used to measure the nitrogen, phosphorus, and potassium content in the soil. Step 3: The proposal unit proposes a plant management method based on the information analyzed by the soil analysis unit. It suggests watering frequency and type of fertilizer based on the soil condition and type of plant. The AI ​​can also refer to past data to suggest optimal management methods. For example, it suggests different management methods depending on the growth stage of the plant.

[0120] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0121] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0122] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0123] For example, the plant identification unit takes a photo of the plant using the camera 42 of the smart device 14, and the specific processing unit 290 of the data processing device 12 uses image analysis technology to identify the type of plant. The soil analysis unit measures the acidity and nutrient content of the soil using, for example, a sensor of the smart device 14, and analyzes the measured values ​​using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal management method based on the soil condition and the type of plant using, for example, the specific processing unit 290 of the data processing device 12. This allows the AI ​​gardening assistant system to identify the type of plant, analyze the type or nutritional state of the soil, and suggest the optimal management method. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0127] 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.

[0128] 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 the voice according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0130] 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 performed in a secure state.

[0131] Fig. 4 shows an example of 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.

[0132] 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.

[0133] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0136] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0137] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0138] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] For example, the plant identification unit takes a photo of the plant using the camera 42 of the smart glasses 214, and identifies the type of plant using image analysis technology by the specific processing unit 290 of the data processing device 12. The soil analysis unit measures the acidity and nutrient content of the soil using, for example, the sensor of the smart glasses 214, and analyzes the measured values ​​by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal management method based on the soil condition and the type of plant, for example, by the specific processing unit 290 of the data processing device 12. This allows the AI ​​gardening assistant system to identify the type of plant, analyze the type or nutritional state of the soil, and suggest the optimal management method. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0143] 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.

[0144] 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 the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0146] 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 performed in a secure state.

[0147] 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.

[0148] 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.

[0149] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0150] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0151] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0152] 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 voice indicating a user input for 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.

[0153] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0154] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0155] For example, the plant identification unit takes a photo of the plant using the camera 42 of the headset terminal 314, and identifies the type of plant using image analysis technology by the specific processing unit 290 of the data processing device 12. The soil analysis unit measures the acidity and nutrient content of the soil using, for example, a sensor of the headset terminal 314, and analyzes the measured values ​​by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal management method based on the soil condition and the type of plant, for example, by the specific processing unit 290 of the data processing device 12. This allows the AI ​​gardening assistant system to identify the type of plant, analyze the type or nutritional state of the soil, and suggest the optimal management method. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0159] 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.

[0160] 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 the voice according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0162] 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 performed in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0164] 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.

[0165] 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.

[0166] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0168] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] 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 a voice indicating a user input for 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.

[0170] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0172] For example, the plant identification unit takes a photo of the plant using the camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 uses image analysis technology to identify the type of plant. The soil analysis unit measures the acidity and nutrient content of the soil using, for example, a sensor of the robot 414, and analyzes the measured values ​​using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal management method based on the soil condition and the type of plant using, for example, the specific processing unit 290 of the data processing device 12. This allows the AI ​​gardening assistant system to identify the type of plant, analyze the type or nutritional state of the soil, and suggest the optimal management method. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0174] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0176] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0178] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0180] 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.

[0181] 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 Universal Serial Bus (USB) 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.

[0182] In addition, 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 upon request from the data processing device 12.

[0183] 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.

[0184] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0185] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0186] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0187] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0189] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0191] (Appendix 1) a plant identification unit for identifying the type of plant; a soil analysis unit including a specific method for analyzing the type or nutritional state of soil based on the type of plant identified by the plant identification unit; a proposal unit that proposes a management method for the plant based on the information analyzed by the soil analysis unit. A system characterized by: (Appendix 2) The soil analysis unit includes: Measure soil acidity or nutrient content in a specific way using dedicated sensors 2. The system of claim 1, (Appendix 3) A disease or pest identification unit is provided for identifying a disease or pest in a specific manner based on the information analyzed by the soil analysis unit. 2. The system of claim 1, (Appendix 4) The disease or pest identification unit includes: Identifying plant diseases or pests using specific image analysis techniques 4. The system of claim 3. (Appendix 5) a growth tracking unit for tracking the growth of the plant in a specific manner based on the information identified by the disease or pest identification unit; 4. The system of claim 3. (Appendix 6) The growth tracking unit is Measure plant height or leaf number in a specific way using a specific image analysis technique 6. The system of claim 5, (Appendix 7) The growth tracking unit is Take regular photos of your plants to document their progress in a concrete way. 6. The system of claim 5, (Appendix 8) The plant identification unit includes: To determine a user's emotion in a specific manner and to adjust timing of plant identification based on the determined user's emotion. 2. The system of claim 1, (Appendix 9) The plant identification unit includes: When identifying plants, improve the identification accuracy in a specific way based on the user's past identification history. 2. The system of claim 1, (Appendix 10) The plant identification unit includes: When identifying plants, different specific identification algorithms are applied depending on the plant's growth stage. 2. The system of claim 1, (Appendix 11) The plant identification unit includes: Estimating a user's emotion and adjusting the display method of the classification result based on the estimated user's emotion 2. The system of claim 1, (Appendix 12) The plant identification unit includes: When identifying plants, the system takes into account the user's geographic location information to prioritize identifying plants that are specific to the region. 2. The system of claim 1, (Appendix 13) The plant identification unit includes: During plant identification, the system analyzes users' social media activity to identify related plants. 2. The system of claim 1, (Appendix 14) The soil analysis unit includes: To estimate a user's emotion and adjust the timing of soil analysis based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The soil analysis unit includes: When analyzing soil, refer to past soil data to improve analysis accuracy. 2. The system of claim 1, (Appendix 16) The soil analysis unit includes: During soil analysis, different analysis algorithms are applied depending on the soil type 2. The system of claim 1, (Appendix 17) The soil analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The soil analysis unit includes: During soil analysis, the geographic location of the user is taken into consideration to prioritize analysis of area-specific soil characteristics. 2. The system of claim 1, (Appendix 19) The soil analysis unit includes: During soil analysis, users' social media activities are analyzed to obtain relevant soil information 2. The system of claim 1, (Appendix 20) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the plant 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the plant category. 2. The system of claim 1, (Appendix 23) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 24) The suggestion unit, At the time of proposal, prioritize proposals based on the stage of plant development 2. The system of claim 1, (Appendix 25) The suggestion unit, When making suggestions, adjust the order of suggestions based on plant relevance 2. The system of claim 1, (Appendix 26) The disease or pest identification unit includes: To estimate a user's emotion and adjust a timing of disease or pest identification based on the estimated user's emotion. 4. The system of claim 3. (Appendix 27) The disease or pest identification unit includes: When identifying diseases or pests, improve the accuracy of identification by referring to past disease or pest data 4. The system of claim 3. (Appendix 28) The disease or pest identification unit includes: Apply different identification algorithms depending on the plant type when identifying diseases or pests 4. The system of claim 3. (Appendix 29) The disease or pest identification unit includes: Estimating a user's emotion and adjusting the display method of the classification result based on the estimated user's emotion 4. The system of claim 3. (Appendix 30) The disease or pest identification unit includes: When identifying diseases or pests, the geographic location of the user is taken into consideration to prioritize identifying diseases or pests specific to that region. 4. The system of claim 3. (Appendix 31) The disease or pest identification unit includes: Upon disease or pest identification, the user's social media activity is analyzed to obtain relevant disease or pest information. 4. The system of claim 3. (Appendix 32) The growth tracking unit is Inferring a user's emotion and adjusting the timing of growth tracking based on the estimated user's emotion 6. The system of claim 5, (Appendix 33) The growth tracking unit is When tracking growth, refer to past growth data to improve tracking accuracy 6. The system of claim 5, (Appendix 34) The growth tracking unit is When tracking growth, apply different tracking algorithms depending on the plant's growth stage 6. The system of claim 5, (Appendix 35) The growth tracking unit is Inferring a user's emotion and adjusting the display method of the growth tracking result based on the estimated user's emotion 6. The system of claim 5, (Appendix 36) The growth tracking unit is When tracking growth, the geographic location of the user is taken into consideration to prioritize tracking of region-specific growth characteristics. 6. The system of claim 5, (Appendix 37) The growth tracking unit is During growth tracking, analyze users' social media activity to obtain relevant growth information 6. The system of claim 5, [Explanation of symbols]

[0192] 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. a plant identification unit for identifying the type of plant; a soil analysis unit including a specific method for analyzing the type or nutritional state of soil based on the type of plant identified by the plant identification unit; a proposal unit that proposes a management method for the plant based on the information analyzed by the soil analysis unit. A system characterized by:

2. The soil analysis unit includes: Measure soil acidity or nutrient content in a specific way using dedicated sensors 2. The system of claim 1 .

3. A disease or pest identification unit is provided for identifying a disease or pest in a specific manner based on the information analyzed by the soil analysis unit.

2. The system of claim 1 .

4. The disease or pest identification unit includes: Identifying plant diseases or pests using specific image analysis techniques 4. The system of claim 3.

5. a growth tracking unit for tracking the growth of the plant in a specific manner based on the information identified by the disease or pest identification unit; 4. The system of claim 3.

6. The growth tracking unit is Measure plant height or leaf number in a specific way using a specific image analysis technique 6. The system of claim 5.

7. The growth tracking unit is Take regular photos of your plants to document their progress in a concrete way.

6. The system of claim 5.

8. The plant identification unit includes: To determine a user's emotion in a specific manner and to adjust timing of plant identification based on the determined user's emotion.

2. The system of claim 1 .

9. The plant identification unit includes: When identifying plants, improve the identification accuracy in a specific way based on the user's past identification history.

2. The system of claim 1 .

10. The plant identification unit includes: When identifying plants, different specific identification algorithms are applied depending on the plant's growth stage.

2. The system of claim 1 .

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