Intelligent agricultural data acquisition and analysis system and method based on Internet of Things

Abstract

The invention relates to the technical field of smart agriculture and the Internet of Things, and discloses a smart agriculture data acquisition and analysis system and method based on the Internet of Things, and the system comprises a sensing layer, a network layer, a platform layer, an application layer and a feedback optimization loop, the sensing layer is used for collecting agricultural environment multi-source data, and the sensing layer comprises sensor nodes and image acquisition equipment; the sensor node comprises an environment sensor group and a crop body sensor group, the network layer is connected with the sensing layer through an electric signal, the network layer comprises an Internet of Things gateway and a communication network, the communication network comprises one or more combinations of LoRa, ZigBee and 4G / 5G, the platform layer is connected with the network layer through an electric signal, and the platform layer is specifically a cloud service platform. By integrating a sensing layer, a network layer, a platform layer, an application layer and a feedback optimization loop, comprehensive acquisition, efficient transmission, intelligent analysis and accurate control of agricultural environment and crop body multi-source data are realized, and the problems existing in a traditional agricultural production mode are effectively solved.

Description

Technical Field

[0001] This invention relates to the fields of smart agriculture and Internet of Things (IoT) technology, and in particular to a smart agriculture data acquisition and analysis system and method based on IoT. Background Technology

[0002] Traditional agricultural production methods rely heavily on manual experience for environmental monitoring and agricultural operations, resulting in problems such as high labor intensity, low production efficiency, low utilization of water and fertilizer resources, and delayed early warning of pests and diseases. With the development of Internet of Things (IoT) technology, existing agricultural monitoring systems can typically collect and remotely transmit some environmental parameters (such as temperature, humidity, and soil moisture).

[0003] However, existing technologies still have significant shortcomings: Data uniformity: Most systems only collect basic environmental data, lacking multi-dimensional perception of crop growth status and external emergencies; Poor real-time transmission and reliability: Sensor nodes typically upload massive amounts of raw data directly to the cloud, which can lead to data delays, loss, or excessive node energy consumption when farmland network conditions are poor; Weak analytical and decision-making capabilities: Data analysis often remains at the level of simple data visualization and threshold alarms, lacking deep data fusion analysis models based on artificial intelligence, and unable to provide accurate predictive suggestions and automated control strategies; System silos: Each subsystem is independent of the others, forming "data islands" that make collaborative intelligent linkage difficult.

[0004] To this end, we propose a smart agriculture data collection and analysis system and method based on the Internet of Things. Summary of the Invention

[0005] The present invention mainly addresses the technical problems existing in the prior art and provides a smart agriculture data collection and analysis system and method based on the Internet of Things.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a smart agriculture data acquisition and analysis system based on the Internet of Things, comprising a perception layer, a network layer, a platform layer, an application layer, and a feedback optimization loop. The perception layer is used to acquire multi-source data of the agricultural environment. The perception layer includes sensor nodes and image acquisition devices. The image acquisition devices are used to acquire multispectral images and visible light images of the crop canopy. The sensor nodes include an environmental sensor group and a crop body sensor group. The environmental sensor group is used to acquire data on soil temperature and humidity, air temperature and humidity, light intensity, carbon dioxide concentration, and wind speed and direction. The crop body sensor group is used to acquire data on micro-changes in crop stem diameter, leaf surface temperature, and stem water content.

[0007] Preferably, the network layer and the perception layer are connected via electrical signals, and the network layer includes an IoT gateway and a communication network.

[0008] Preferably, the IoT gateway is used to receive, temporarily store and preprocess sensing layer data, and upload the processed data to the cloud platform through a communication network. The IoT gateway has a built-in edge computing unit for filtering, compressing and local fusion calculation of sensing layer data, and executing real-time control logic based on simple thresholds. The communication network includes one or more combinations of LoRa, ZigBee, and 4G / 5G.

[0009] Preferably, the platform layer and the network layer are connected by electrical signals. The platform layer is specifically a cloud service platform, which includes a data storage module, a data processing and analysis module, and a control and decision-making module.

[0010] Preferably, the data storage module is used to receive and store multi-source data from the network layer. The data processing and analysis module has a built-in multi-source data fusion model, crop growth model and AI prediction model, which are used to fuse, analyze and mine the stored data to generate crop growth status assessment, pest and disease early warning, yield prediction and agricultural operation suggestions. The AI ​​prediction model is a deep learning model trained on historical agricultural data, which is used to identify image data to realize early identification of pests and diseases and diagnosis of nutrient deficiency. The control decision module is used to generate control commands based on the output results of the data processing and analysis module.

[0011] Preferably, the application layer and the platform layer are connected via electrical signals, and the application layer includes a user terminal and an actuator.

[0012] Preferably, the user terminal is used to display data analysis results and early warning information to the user and to receive user instructions, while the actuator is used to receive and execute control instructions issued by the platform layer to realize automatic control of irrigation, fertilization, shading and ventilation equipment.

[0013] Preferably, the feedback optimization loop is connected to both the application layer and the platform layer via electrical signals. The feedback optimization loop feeds back the operating status data of the actuator to the platform layer for optimizing and calibrating the crop growth model and the AI ​​prediction model.

[0014] The method for data collection and analysis in smart agriculture based on the Internet of Things (IoT), including the aforementioned IoT-based smart agriculture data collection and analysis system, specifically includes the following steps:

[0015] S1: Multi-source data acquisition: Real-time acquisition of multi-source data on the agricultural environment and crop itself through various sensors and image acquisition devices in the perception layer;

[0016] S2: Multi-source data preprocessing and uploading: The IoT gateway aggregates and preprocesses multi-source data, and uploads the processed data to the platform layer through the network layer;

[0017] S3: Multi-source data reception and storage: The platform layer's data storage module receives and stores multi-source data;

[0018] S4: Multi-source data analysis and computation: The data processing and analysis module calls the multi-source data fusion model, crop growth model and AI prediction model to perform fusion analysis and intelligent computation on multi-source data, and generate analysis results including environmental situation, growth status and disaster early warning.

[0019] S5: Control command generation and result suggestion display: Based on the analysis results, the control decision module automatically generates control commands for the actuators, and the analysis results and control suggestions are pushed to the user terminal of the application layer for visualization display.

[0020] S6: Control command execution and execution status feedback: The actuator executes control commands to complete precision agricultural operations and feeds back the execution status to the platform layer;

[0021] S7: Analysis Model Optimization and Update: The platform layer continuously optimizes and updates the analysis model based on the execution results and new data collected subsequently, forming a closed-loop optimization.

[0022] This invention provides a smart agriculture data acquisition and analysis system and method based on the Internet of Things (IoT). It has the following beneficial effects:

[0023] 1. This IoT-based smart agriculture data acquisition and analysis system and method integrates a perception layer, network layer, platform layer, application layer, and feedback optimization loop to achieve comprehensive acquisition, efficient transmission, intelligent analysis, and precise control of multi-source data on the agricultural environment and crops, effectively solving the problems existing in traditional agricultural production models.

[0024] 2. This IoT-based smart agriculture data acquisition and analysis system and method, through the establishment of a perception layer and its multi-source data acquisition capabilities, ensures the comprehensiveness and accuracy of the data, providing a solid foundation for subsequent intelligent analysis. The combination of edge computing and multiple communication networks in the network layer greatly improves the real-time performance and reliability of data transmission while reducing node energy consumption.

[0025] 3. This IoT-based smart agriculture data acquisition and analysis system and method, through the establishment of a platform layer and a data processing and analysis module at the platform layer, achieves in-depth data mining and intelligent analysis through built-in multi-source data fusion models, crop growth models and AI prediction models, providing accurate predictive suggestions and automated control strategies for agricultural production.

[0026] 4. This IoT-based smart agriculture data acquisition and analysis system and method, by setting up an application layer, user terminals and execution mechanisms in the application layer, enables the analysis results and control commands to be displayed intuitively and executed effectively, thereby improving the efficiency and intelligence level of agricultural production.

[0027] 5. The IoT-based smart agriculture data acquisition and analysis system and method, by setting up a feedback optimization loop, realizes continuous optimization and updating of the analysis model, forming a closed-loop optimization, and further improves the intelligence and adaptability of the system. Attached Figure Description

[0028] Figure 1 This is a system module diagram of the present invention;

[0029] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0030] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0031] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0032] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0033] In the description of the embodiments of the present invention, it should be noted that the terms "center," "upper," "lower," "inner," "outer," and "side," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.

[0035] 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, and 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.

[0036] Example 1: A smart agriculture data acquisition and analysis system based on the Internet of Things, such as Figure 1 As shown, the system comprises a perception layer, a network layer, a platform layer, an application layer, and a feedback optimization loop. The perception layer collects multi-source data on the agricultural environment and includes sensor nodes and image acquisition devices. The image acquisition devices acquire multispectral and visible light images of the crop canopy. The sensor nodes include environmental sensor groups and crop body sensor groups. The environmental sensor groups collect data on soil temperature and humidity, air temperature and humidity, light intensity, carbon dioxide concentration, and wind speed and direction. The crop body sensor groups collect data on micro-changes in crop stem diameter, leaf surface temperature, and stem water content. By integrating the perception layer, network layer, platform layer, application layer, and feedback optimization loop, the system achieves comprehensive collection, efficient transmission, intelligent analysis, and precise control of multi-source data on the agricultural environment and crop body, effectively solving the problems existing in traditional agricultural production models.

[0037] Example 2: Based on Example 1, as follows Figure 1As shown, the network layer and the perception layer are connected via electrical signals. The network layer includes an IoT gateway and a communication network. The IoT gateway receives, temporarily stores, and preprocesses perception layer data, and uploads the processed data to the cloud platform via the communication network. The IoT gateway has a built-in edge computing unit for filtering, compressing, and locally fusing perception layer data, and executing real-time control logic based on simple thresholds. The communication network includes one or more combinations of LoRa, ZigBee, and 4G / 5G. By setting up the perception layer, its multi-source data acquisition capabilities ensure the comprehensiveness and accuracy of the data, providing a solid foundation for subsequent intelligent analysis. The combination of edge computing in the network layer and multiple communication networks greatly improves the real-time performance and reliability of data transmission and reduces node energy consumption.

[0038] Example 3: Based on Examples 1 and 2, as follows... Figure 1 As shown, the platform layer and network layer are connected via electrical signals. The platform layer is specifically a cloud service platform, comprising a data storage module, a data processing and analysis module, and a control decision module. The data storage module receives and stores multi-source data from the network layer. The data processing and analysis module incorporates a multi-source data fusion model, a crop growth model, and an AI prediction model to fuse, analyze, and mine the stored data, generating crop growth status assessments, pest and disease warnings, yield predictions, and agricultural operation suggestions. The AI ​​prediction model is a deep learning model trained on historical agricultural data, used to recognize image data for early pest and disease identification and nutrient deficiency diagnosis. The control decision module generates control commands based on the output of the data processing and analysis module. By establishing the platform layer, and through its built-in multi-source data fusion model, crop growth model, and AI prediction model, the platform layer's data processing and analysis module achieves in-depth data mining and intelligent analysis, providing accurate predictive suggestions and automated control strategies for agricultural production.

[0039] Example 4: Based on Examples 1, 2, and 3, as follows... Figure 1As shown, the application layer and platform layer are connected via electrical signals. The application layer includes a user terminal and an actuator. The user terminal displays data analysis results and early warning information to the user and receives user commands. The actuator receives and executes control commands issued by the platform layer, enabling automatic control of irrigation, fertilization, shading, and ventilation equipment. A feedback optimization loop is connected to both the application layer and the platform layer via electrical signals. This loop feeds back the operating status data of the actuator to the platform layer for optimizing and calibrating the crop growth model and AI prediction model. By establishing the application layer, its user terminal, and the actuator, analysis results and control commands can be displayed intuitively and executed effectively, improving the efficiency and intelligence of agricultural production. The feedback optimization loop enables continuous optimization and updating of the analysis model, forming a closed-loop optimization that further enhances the system's intelligence and adaptability.

[0040] Example 5: Based on Examples 1, 2, 3, and 4, as follows... Figure 2 As shown, the method for data collection and analysis in smart agriculture based on the Internet of Things (IoT), including the aforementioned IoT-based smart agriculture data collection and analysis system, specifically includes the following steps:

[0041] S1: Multi-source data acquisition: Real-time acquisition of multi-source data on the agricultural environment and crop itself through various sensors and image acquisition devices in the perception layer;

[0042] S2: Multi-source data preprocessing and uploading: The IoT gateway aggregates and preprocesses multi-source data, and uploads the processed data to the platform layer through the network layer;

[0043] S3: Multi-source data reception and storage: The platform layer's data storage module receives and stores multi-source data;

[0044] S4: Multi-source data analysis and computation: The data processing and analysis module calls the multi-source data fusion model, crop growth model and AI prediction model to perform fusion analysis and intelligent computation on multi-source data, and generate analysis results including environmental situation, growth status and disaster early warning.

[0045] S5: Control command generation and result suggestion display: Based on the analysis results, the control decision module automatically generates control commands for the actuators, and the analysis results and control suggestions are pushed to the user terminal of the application layer for visualization display.

[0046] S6: Control command execution and execution status feedback: The actuator executes control commands to complete precision agricultural operations and feeds back the execution status to the platform layer;

[0047] S7: Analysis Model Optimization and Update: The platform layer continuously optimizes and updates the analysis model based on the execution results and new data collected subsequently, forming a closed-loop optimization.

[0048] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A smart agriculture data acquisition and analysis system based on the Internet of Things, characterized in that, It includes a perception layer, a network layer, a platform layer, an application layer, and a feedback optimization loop. The perception layer includes sensor nodes and image acquisition devices. The sensor nodes include environmental sensor groups and crop body sensor groups.

2. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The network layer and the sensing layer are connected by electrical signals. The network layer includes an IoT gateway and a communication network. The communication network includes one or more combinations of LoRa, ZigBee, and 4G / 5G.

3. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The platform layer and the network layer are connected via electrical signals, and the platform layer is specifically a cloud service platform.

4. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The platform layer includes a data storage module, a data processing and analysis module, and a control and decision-making module.

5. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The data processing and analysis module has built-in multi-source data fusion model, crop growth model and AI prediction model.

6. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The application layer and the platform layer are connected via electrical signals.

7. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The application layer includes user terminals and execution mechanisms.

8. The smart agriculture data acquisition and analysis system based on the Internet of Things according to claim 1, characterized in that: The feedback optimization loop is connected to both the application layer and the platform layer via electrical signals.

9. A method for data collection and analysis in smart agriculture based on the Internet of Things, characterized in that, The smart agriculture data acquisition and analysis system based on the Internet of Things, as described in any one of claims 1-8, specifically includes the following steps: S1: Multi-source data acquisition: Real-time acquisition of multi-source data on the agricultural environment and crop itself through various sensors and image acquisition devices in the perception layer; S2: Multi-source data preprocessing and uploading: The IoT gateway aggregates and preprocesses multi-source data, and uploads the processed data to the platform layer through the network layer; S3: Multi-source data reception and storage: The platform layer's data storage module receives and stores multi-source data; S4: Multi-source data analysis and computation: The data processing and analysis module calls the multi-source data fusion model, crop growth model and AI prediction model to perform fusion analysis and intelligent computation on multi-source data, and generate analysis results including environmental situation, growth status and disaster early warning. S5: Control command generation and result suggestion display: Based on the analysis results, the control decision module automatically generates control commands for the actuators, and the analysis results and control suggestions are pushed to the user terminal of the application layer for visualization display. S6: Control command execution and execution status feedback: The actuator executes control commands to complete precision agricultural operations and feeds back the execution status to the platform layer; S7: Analysis Model Optimization and Update: The platform layer continuously optimizes and updates the analysis model based on the execution results and new data collected subsequently, forming a closed-loop optimization.

Images