A soil geography knowledge learning and intelligent diagnosis system and method

Abstract

The application provides a soil geography knowledge learning and intelligent diagnosis system and method, including a textbook learning module, which is used for digitizing and reconstructing soil geography textbook content, constructing a three-dimensional soil model, and providing a text introduction, feature description and distribution area of a typical soil type through a soil type database, and supporting keyword retrieval and condition filtering; a popular science video module, which is used for providing short video content of soil geography scientific knowledge; and an AI photographing and identifying module, which is used for receiving a soil image uploaded by a user, combining image recognition technology and geographic location information, and generating a soil type judgment result and an agricultural production suggestion. The application combines a soil geography knowledge system and artificial intelligence technology, realizes digital learning and intelligent diagnosis of soil geography knowledge, and is suitable for multiple scenes such as physical geography and agricultural teaching, popular science propaganda and agricultural production guidance.

Description

Technical Field

[0001] This invention relates to the field of agricultural information technology, and in particular to a soil geography knowledge learning and intelligent diagnosis system and method. Background Technology

[0002] Soil is the foundation of agricultural production and a vital component of the ecosystem. Soil geography plays a crucial guiding role in the rational utilization of soil resources. However, the learning and dissemination of soil geography knowledge currently faces numerous challenges. On the one hand, some universities rely heavily on textual textbooks and two-dimensional images in their professional courses such as "Soil Geography," making it difficult for students to intuitively understand the three-dimensional structure of soil profiles and their formation processes, thus limiting learning effectiveness. On the other hand, popular science content on soil geography for the general public is lacking, and the public's understanding of the ecological value and scientific implications of soil is insufficient. Furthermore, in actual agricultural production, farmers and horticultural enthusiasts often struggle to determine whether poor crop growth is caused by soil factors when encountering problems, lacking convenient and accurate soil diagnostic tools.

[0003] In recent years, with the development of artificial intelligence technology, image recognition technology has been widely used in agriculture, such as for identifying crop diseases and pests and judging fruit maturity. However, intelligent identification and diagnosis technology for soil remains relatively weak. Existing soil testing methods mostly rely on laboratory analysis, which is costly and time-consuming, making it difficult to meet the needs of grassroots users for rapid diagnosis. Although some mobile applications provide soil information query functions, they lack intelligent diagnostic capabilities that integrate image recognition and geographic information, and therefore cannot provide users with personalized planting guidance.

[0004] Therefore, how to provide a system and method that integrates soil geography knowledge learning, popular science dissemination and intelligent diagnosis has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address at least one of the technical problems mentioned above, this invention provides a soil geography knowledge learning and intelligent diagnosis system and method that combines soil science knowledge system with artificial intelligence technology to achieve digital learning and intelligent diagnosis of soil geography knowledge. It is applicable to multiple scenarios such as agricultural teaching, popular science promotion and planting guidance.

[0006] The present invention solves the technical problem by adopting the following technical solution: A soil geography knowledge learning and intelligent diagnosis system includes: The textbook learning module is used to digitally reconstruct the content of soil science textbooks, build three-dimensional soil models, and provide textual descriptions, feature descriptions and distribution areas of typical soil types through a soil type database, supporting keyword search and conditional filtering. The science popularization video module is used to provide short video content that provides knowledge about soil geography. The AI ​​photo recognition module receives soil images uploaded by users and combines image recognition technology with geographic location information to generate soil type judgment results and agricultural production suggestions.

[0007] Furthermore, the learning modules of the textbook include: The 3D model building unit is used to build a 3D visualization model based on the layered structure of the soil profile, and supports users to interactively view the characteristics of each soil layer. The soil type database unit is used to store soil type and its attribute information, including administrative divisions, topographic features, and physicochemical properties; The search unit supports keyword retrieval and conditional filtering, providing multi-dimensional filtering functions based on soil properties.

[0008] Furthermore, the AI ​​photo recognition module includes: The image quality assessment unit is used to determine the clarity of images uploaded by users and to prompt users or perform image enhancement processing when the clarity is insufficient. The image feature recognition unit is used to extract color, texture, and contour information from soil images and to identify soil texture, color, and structural features. The geographic information acquisition unit is used to acquire the user's geographic location information, including climate zone, average annual temperature, and precipitation. The knowledge base retrieval unit is used to match the image recognition results with geographic information to a pre-built soil geographic knowledge base and generate agricultural production suggestions. The feedback optimization unit is used to collect user feedback on the recognition results and optimize the recognition model based on the feedback results.

[0009] Furthermore, the image feature recognition unit compares the user's image with soil sample images in the database, and uses contour, color distribution and texture information to perform similarity matching to determine the soil type.

[0010] Furthermore, the knowledge base calling unit outputs soil improvement suggestions and suitable crop recommendations based on the soil type identification results and geographical location information.

[0011] A method for learning and intelligently diagnosing soil geography knowledge includes the following steps: Step 1: Construct a soil type database and a soil image sample library; Step 2: Receive soil images uploaded by users and obtain user geolocation information; Step 3: Perform quality assessment and feature extraction on the soil images to identify the soil's color, texture, and structure; Step 4: Combine geographic location information with image recognition results, match with soil type database, and determine soil type; Step 5: Based on soil type and geographical location information, generate agricultural production suggestions from the knowledge base; Step six: Return the identification results and agricultural production suggestions to the user, and collect user feedback for model optimization.

[0012] Furthermore, in step three, image feature extraction includes image noise reduction, resolution adjustment, contour extraction, color space conversion, and texture analysis.

[0013] Furthermore, in step four, when matching the soil type database, a joint judgment is made by combining administrative divisions, climate zones, and topographical elements to narrow down the range of soil types to be screened.

[0014] Furthermore, in step five, the agricultural production recommendations include soil improvement measures, suitable crop recommendations, fertilization recommendations, irrigation recommendations, and tillage recommendations.

[0015] The beneficial effects of this invention are as follows: This invention reconstructs soil profiles using 3D models, allowing students to intuitively understand soil structure and formation processes, lowering the learning threshold and improving teaching effectiveness. Short videos combined with knowledge cards facilitate fragmented learning of soil science knowledge for the public, enhancing awareness of scientific agricultural production. AI photo recognition combined with geographic information allows users to obtain preliminary diagnoses and agricultural production suggestions simply by uploading soil photos, eliminating the need for laboratory testing, resulting in low cost and high efficiency. The system generates targeted agricultural production suggestions based on soil type and geographical location, providing improvement measures and crop recommendations, demonstrating strong practicality. The recognition model is continuously optimized through user feedback mechanisms, with system accuracy improving with the accumulation of data. It can be widely applied in various scenarios such as physical geography and agricultural teaching, agricultural technology extension, farmer planting, and horticulture, possessing significant social benefits and market value. Attached Figure Description

[0016] Figure 1 This is a system architecture diagram of the present invention.

[0017] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] refer to Figure 1 This invention provides a soil geography knowledge learning and intelligent diagnosis system, comprising: The textbook learning module is used to digitally reconstruct the content of soil science textbooks, build three-dimensional soil models, and provide textual descriptions, characteristic descriptions and distribution areas of soils through a soil type database, supporting keyword search and condition filtering; The science popularization video module disseminates soil science knowledge in short video format, lowering the barrier to public understanding. Specifically, the videos cover topics such as soil formation processes, characteristics of different soil types, soil biodiversity, and the relationship between soil and the ecological environment. Each video is 3-5 minutes long and uses a combination of animation and live-action footage to enhance watchability. Knowledge point cards are embedded in the videos; users can click to view extended content or jump to the textbook learning module for in-depth learning.

[0020] The AI ​​photo recognition module receives soil images uploaded by users and combines image recognition technology with geographic location information to generate soil assessment results and agricultural production suggestions.

[0021] The learning modules of the textbook include: The 3D model building unit constructs a 3D visualization model based on the layered structure of the soil profile (such as layer O, layer A, layer B, layer C, etc.). Users can rotate and zoom the model by touching the screen to view the morphological characteristics of each layer. The model supports dynamic adjustment of environmental factors such as climate, water, and vegetation, and displays the real-time impact of these factors on the soil profile morphology. For example, increasing precipitation can simulate enhanced leaching and changes in soil color.

[0022] The Soil Type Database unit collects soil survey data to construct a database covering major soil types. Each record includes information such as soil name, administrative division, topographic features, parent material type, profile characteristics, physical properties (including texture, bulk density, and porosity), and chemical properties (including pH value, organic matter content, and cation exchange capacity). Users can search for soil types by clicking on the map or selecting administrative division tags to view their textual descriptions, feature descriptions, and distribution areas.

[0023] The soil profile display unit shows a standard soil profile map on the soil type details page. Users can click on a specific layer in the profile (such as humus layer, leaching layer, or sedimentary layer), and the system will pop up an information card showing the name, characteristics, formation cause, and typical thickness of that layer.

[0024] The search unit supports keyword search and conditional filtering. Users can perform precise searches by entering soil names such as "yellow-red sandy soil," or combine queries using filter conditions such as pH range, texture type, and organic matter content. Search results are linked to a soil type database, facilitating efficient access to target information for professional users.

[0025] The AI ​​photo recognition module includes: The image quality assessment unit receives soil cross-section photos uploaded by users and automatically evaluates the image sharpness. If the image is blurry or poorly lit, the system will prompt insufficient sharpness, suggest retaking the photo, and can call image enhancement algorithms to preprocess the image and extract core features; The image feature recognition unit uses a convolutional neural network to extract color, texture, and contour features from soil images. Color features are used to determine soil color; texture features are used to determine soil texture; and contour features are used to identify structural information such as roots, gravel, and new growths. To determine texture, the system can incorporate user-assisted operations: prompting the user to take a small amount of soil, moisten it, and then roll it into a long strip; user feedback helps determine the texture type.

[0026] The geographic information acquisition unit, after obtaining user authorization, acquires the user's geographical location through GPS positioning and combines it with the geographic information system to obtain background data such as the climate zone, average annual temperature, precipitation, topography, and main vegetation of the region.

[0027] The knowledge base retrieval unit takes image recognition results and geographic information as input, matches them against a soil type database, and determines the most likely soil type. For example, if the soil is identified as reddish in color, sticky in texture, and located in a subtropical humid region, it is classified as either red soil or yellow soil. Based on the determination, the knowledge base is invoked to generate personalized agricultural production suggestions. The knowledge base stores information such as soil improvement methods, suitable crops, fertilization recommendations, and irrigation suggestions for various soil types.

[0028] The feedback optimization unit allows users to evaluate the identification results and supplement them with information on the actual soil conditions. The system collects feedback data for continuous model optimization. Users who provide high-quality feedback are rewarded with membership privileges or free usage sessions.

[0029] refer to Figure 2 The present invention also provides a method for soil geographical knowledge learning and intelligent diagnosis, comprising the following steps: Step 1: Construct a soil type database and a soil image sample library; Step 2: Receive soil images uploaded by users and obtain user geolocation information; Step 3: Perform quality assessment and feature extraction on the soil images to identify the soil's color, texture, and structure; Step 4: Combine geographic location information with image recognition results, match with soil type database, and determine soil type; Step 5: Based on soil type and geographical location information, generate agricultural production suggestions from the knowledge base; Step six: Return the identification results and agricultural production suggestions to the user, and collect user feedback for model optimization.

[0030] Further optimize the technical solution. In step three, image feature extraction includes noise reduction, resolution adjustment, contour extraction, color space conversion, and texture analysis.

[0031] To further optimize the technical solution, in step four, when matching the soil type database, a joint judgment is made by combining administrative divisions, climate zones, and topographical elements to narrow down the range of soil type selection.

[0032] Further optimize the technical solution. In step five, the agricultural production recommendations include soil improvement measures, suitable crop recommendations, fertilization recommendations, irrigation recommendations, and tillage recommendations.

[0033] This invention reconstructs soil profiles using 3D models, allowing students to intuitively understand soil structure and formation processes, lowering the learning threshold and improving teaching effectiveness. Short videos combined with knowledge cards facilitate fragmented learning of soil science knowledge for the public, enhancing environmental awareness. AI photo recognition combined with geographic information allows users to obtain preliminary diagnoses and agricultural production suggestions simply by uploading soil photos, eliminating the need for laboratory testing, resulting in low cost and high efficiency. The system generates targeted agricultural production suggestions based on soil type and geographical location, providing improvement measures and crop recommendations, making it highly practical. The recognition model is continuously optimized through user feedback mechanisms, and the system's accuracy improves with the accumulation of data. It can be widely applied in various scenarios such as physical geography and agricultural teaching, agricultural technology extension, farmer planting, and horticulture, possessing significant social benefits and market value.

[0034] The soil geography knowledge learning and intelligent diagnosis system and method proposed in this invention fully utilizes the camera and positioning functions of existing smart terminal devices, combined with artificial intelligence technology, to achieve digital dissemination and intelligent diagnosis of soil geography knowledge. The system has a clear structure, feasible technical solutions, and good replicability and promotion value. It is applicable to multiple fields such as physical geography and agricultural education, agricultural technology promotion, farmer planting guidance, and horticulture enthusiasts, and possesses significant industrial applicability.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A soil geography knowledge learning and intelligent diagnosis system, characterized in that, include: The textbook learning module is used to digitally reconstruct the content of soil geography textbooks, build three-dimensional soil models, and provide textual descriptions, feature descriptions and distribution areas of typical soil types through a soil type database, supporting keyword search and conditional filtering. The science popularization video module is used to provide short video content that provides knowledge about soil geography. The AI ​​photo recognition module receives soil images uploaded by users and combines image recognition technology with geographic location information to generate soil type judgment results and agricultural production suggestions.

2. The soil geography knowledge learning and intelligent diagnosis system according to claim 1, characterized in that, The learning modules of the textbook include: The 3D model building unit is used to build a 3D visualization model based on the layered structure of the soil profile, and supports users to interactively view the characteristics of each soil layer. The soil type database unit is used to store soil type and its attribute information, including administrative divisions, topographic features, and physicochemical properties; The search unit supports keyword retrieval and conditional filtering, providing multi-dimensional filtering functions based on soil properties.

3. The soil geography knowledge learning and intelligent diagnosis system according to claim 2, characterized in that, The AI ​​photo recognition module includes: The image quality assessment unit is used to determine the clarity of images uploaded by users and to prompt users or perform image enhancement processing when the clarity is insufficient. The image feature recognition unit is used to extract color, texture, and contour information from soil images and to identify soil texture, color, and structural features. The geographic information acquisition unit is used to acquire the user's geographic location information, including climate zone, average annual temperature, and precipitation. The knowledge base retrieval unit is used to match the image recognition results with geographic information to a pre-built soil geographic knowledge base and generate agricultural production suggestions. The feedback optimization unit is used to collect user feedback on the recognition results and optimize the recognition model based on the feedback results.

4. The soil geography knowledge learning and intelligent diagnosis system according to claim 3, characterized in that, The image feature recognition unit compares the user's image with soil sample images in the database, and uses contour, color distribution and texture information to perform similarity matching to determine the soil type.

5. The soil geography knowledge learning and intelligent diagnosis system according to claim 4, characterized in that, The knowledge base calling unit outputs soil improvement suggestions and suitable crop recommendations based on the soil type identification results and geographical location information.

6. A method for learning and intelligently diagnosing soil geographical knowledge, characterized in that, Includes the following steps: Step 1: Construct a soil type database and a soil image sample library; Step 2: Receive soil images uploaded by users and obtain user geolocation information; Step 3: Perform quality assessment and feature extraction on the soil images to identify the soil's color, texture, and structure; Step 4: Combine geographical location information with image recognition results, match with soil type database, and determine soil type; Step 5: Based on soil type and geographical location information, generate agricultural production suggestions from the knowledge base; Step six: Return the identification results and agricultural production suggestions to the user, and collect user feedback for model optimization.

7. The method for learning and intelligent diagnosis of soil geographical knowledge according to claim 6, characterized in that, In step three, image feature extraction includes noise reduction, resolution adjustment, contour extraction, color space conversion, and texture analysis.

8. The method for learning and intelligent diagnosis of soil geographical knowledge according to claim 6, characterized in that, In step four, when matching the soil type database, a joint judgment is made by combining administrative divisions, climate zones, and topographical elements to narrow down the range of soil types to be screened.

9. The method for learning and intelligent diagnosis of soil geographical knowledge according to claim 6, characterized in that, Step five includes agricultural production recommendations such as soil improvement measures, suitable crop recommendations, fertilization recommendations, irrigation recommendations, and tillage recommendations.

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