Farmland ecological environment intelligent monitoring and precise regulation and control system and method based on multi-source remote sensing and AI

Abstract

The invention relates to the technical field of agricultural information, in particular to a farmland ecological environment intelligent monitoring and precise regulation and control system and method based on multi-source remote sensing and AI, and the system comprises the following modules: a multi-source remote sensing data collection module, a data preprocessing module, an AI data analysis module, a decision support module, a precise regulation and control execution module and a user interaction module. The multi-source remote sensing data acquisition module fuses satellite, unmanned aerial vehicle and ground remote sensing data, realizes comprehensive monitoring of farmland ecological environment from macroscopic to microscopic and high temporal-spatial resolution, and improves the accuracy and integrity of monitoring data.

Description

Technical Field

[0001] The present invention relates to the field of agricultural information technology, and specifically to a system and method for intelligent monitoring and precise control of farmland ecological environment based on multi-source remote sensing and AI. Background Art

[0002] In the process of modern agricultural development, the monitoring and regulation of farmland ecological environment is of vital importance. Traditional farmland environmental monitoring mainly relies on manual field measurements and a small number of fixed monitoring stations, which has many disadvantages. Manual measurement is inefficient and has a long cycle, making it difficult to obtain data for the entire farmland area; fixed monitoring stations are limited in distribution and cannot cover the complex and changing farmland environment, resulting in insufficient data representativeness. Although satellite remote sensing monitoring has been introduced in some areas, a single remote sensing data source is limited by factors such as weather and temporal resolution and cannot meet the needs of real-time and accurate monitoring of the farmland ecological environment. In addition, the existing monitoring system lacks intelligent analysis and precise control capabilities, and is unable to take effective measures in response to changes in the farmland ecological environment in a timely manner, making it difficult to achieve a balance between efficient utilization of farmland resources and ecological environmental protection. To this end, an intelligent monitoring and precise control system and method for the farmland ecological environment based on multi-source remote sensing and AI is proposed. Summary of the Invention

[0003] In view of this, the present invention provides an intelligent monitoring and precise control system and method for farmland ecological environment based on multi-source remote sensing and AI to solve or alleviate the technical problems existing in the existing technology and at least provide a beneficial option.

[0004] The technical solution of the present invention is implemented as follows: an intelligent monitoring and precise control system for farmland ecological environment based on multi-source remote sensing and AI, including the following modules: a multi-source remote sensing data acquisition module, a data preprocessing module, an AI data analysis module, a decision support module, a precise control execution module and a user interaction module.

[0005] Further preferably, the multi-source remote sensing data acquisition module integrates satellite remote sensing, UAV remote sensing and ground remote sensing equipment. Satellite remote sensing uses high-resolution optical satellites and multispectral satellites to obtain macro data such as land use and vegetation coverage in large-scale farmland areas; UAV remote sensing is equipped with hyperspectral cameras, thermal infrared cameras, etc., which can perform high-temporal and spatial resolution data collection on specific farmland areas at low altitudes to obtain detailed information such as crop growth conditions, diseases and pests; ground remote sensing equipment, such as portable spectrometers, soil moisture sensors, etc., are used to collect local soil physical and chemical properties of farmland, near-ground meteorological data, etc.

[0006] Further preferably, the data preprocessing module performs preprocessing operations such as radiation correction, geometric correction, and atmospheric correction on the collected multi-source remote sensing data to eliminate data errors caused by sensor differences, terrain undulations, atmospheric interference and other factors, unify data formats and coordinate systems, and improve data quality and availability.

[0007] Further preferably, the AI ​​data analysis module: constructs a farmland ecological environment analysis model based on a deep learning algorithm, such as a convolutional neural network (CNN) for crop disease and pest identification, a semantic segmentation model for land use classification, and a recurrent neural network (RNN) for predicting crop growth trends. The preprocessed data is input into the model to realize intelligent identification, classification and prediction of farmland ecological environment elements, and analyze information such as crop growth status, soil moisture conditions, and the risk of disease and pest occurrence.

[0008] Further preferably, the decision support module: combines the results output by the AI ​​data analysis module, as well as the preset farmland ecological environment standards and crop growth requirements, to formulate targeted farmland ecological environment regulation strategies. For example, when the soil moisture is insufficient, an irrigation plan is generated; when the risk of pests and diseases is predicted, pest and disease prevention measures are formulated.

[0009] Further preferably, the precise control execution module includes intelligent irrigation equipment, fertilizing machinery, pest control drones, etc. According to the control strategy generated by the decision support module, the precise control execution module automatically performs corresponding operations, such as intelligent irrigation equipment realizes precise irrigation based on soil moisture data, and fertilizing machinery performs variable fertilization according to soil nutrient conditions.

[0010] Further preferably, the user interaction module provides web and mobile applications to facilitate agricultural managers, farmers and other users to view farmland ecological environment monitoring data, AI analysis results and control strategy execution progress in real time. Users can also manually adjust control parameters through this module, or submit monitoring and control requirements for specific farmland areas.

[0011] The method for intelligent monitoring and precise control of farmland ecological environment based on multi-source remote sensing and AI includes the following steps:

[0012] S1. Multi-source remote sensing data acquisition: According to the set time and space plan, the multi-source remote sensing data acquisition module is used to obtain farmland ecological environment related data from satellites, drones and ground equipment;

[0013] S2, data preprocessing, transmits the collected data to the data preprocessing module, performs radiation correction, geometric correction, atmospheric correction and other processing, and generates a standardized multi-source remote sensing data set;

[0014] S3, AI data analysis: input the preprocessed data set into the deep learning model of the AI ​​data analysis module. Through model training and inference, it realizes intelligent analysis and prediction of farmland ecological environment factors;

[0015] S4, Decision-making: The decision support module formulates scientific and reasonable farmland ecological environment control decisions based on AI analysis results, combined with farmland ecological environment standards and crop growth requirements;

[0016] S5, precise control execution: the precise control execution module receives the control decision instruction, automatically starts the corresponding equipment, and precisely controls the farmland ecological environment;

[0017] S6, feedback and optimization, real-time monitoring of control effects, and transmission of feedback data to the system for optimizing AI analysis models and control strategies to form closed-loop management.

[0018] The embodiment of the present invention adopts the above technical solution, which has the following advantages:

[0019] 1. The multi-source remote sensing data acquisition module of the present invention integrates satellite, UAV and ground remote sensing data to achieve comprehensive monitoring of farmland ecological environment from macro to micro with high temporal and spatial resolution, thereby improving the accuracy and integrity of monitoring data.

[0020] 2. The AI ​​data analysis module of the present invention uses a deep learning algorithm to quickly and accurately identify and predict changes in the ecological environment of farmland, greatly improving efficiency and accuracy compared to traditional analysis methods.

[0021] 3. The present invention adopts a control strategy formulated based on the results of intelligent analysis, combined with a precise control execution module, to achieve precise utilization of farmland resources, avoid resource waste, improve crop yield and quality, and protect the farmland ecological environment.

[0022] 4. The user interaction module of the present invention provides a friendly interactive interface, which facilitates users to understand the farmland status in real time and participate in regulatory decision-making, thereby enhancing the practicality and flexibility of the system.

[0023] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0025] Fig. 1 is a system flow chart of the present invention;

[0026] Fig. 2 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0027] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] like Figs. 1-2 As shown, an embodiment of the present invention provides an intelligent monitoring and precise control system for farmland ecological environment based on multi-source remote sensing and AI, including the following modules: a multi-source remote sensing data acquisition module, a data preprocessing module, an AI data analysis module, a decision support module, a precise control execution module and a user interaction module.

[0030] In one embodiment, a multi-source remote sensing data acquisition module integrates satellite remote sensing, UAV remote sensing, and ground remote sensing equipment. Satellite remote sensing uses high-resolution optical satellites and multispectral satellites to obtain macro data such as land use and vegetation coverage in large-scale farmland areas; UAV remote sensing is equipped with hyperspectral cameras, thermal infrared cameras, etc., which can collect high-temporal and spatial resolution data of specific farmland areas at low altitudes to obtain detailed information such as crop growth conditions, pests and diseases; ground remote sensing equipment, such as portable spectrometers and soil moisture sensors, is used to collect local soil physical and chemical properties of farmland, near-ground meteorological data, etc.

[0031] In one embodiment, the data preprocessing module performs radiation correction, geometric correction, atmospheric correction and other preprocessing operations on the collected multi-source remote sensing data to eliminate data errors caused by sensor differences, terrain undulations, atmospheric interference and other factors, unify the data format and coordinate system, and improve data quality and availability.

[0032] In one embodiment, the AI ​​data analysis module: constructs a farmland ecological environment analysis model based on a deep learning algorithm, such as a convolutional neural network (CNN) for crop disease and pest identification, a semantic segmentation model for land use classification, and a recurrent neural network (RNN) for predicting crop growth trends. The preprocessed data is input into the model to realize intelligent identification, classification and prediction of farmland ecological environment elements, and analyze information such as crop growth status, soil moisture conditions, and the risk of disease and pest occurrence.

[0033] In one embodiment, the decision support module: combines the results output by the AI ​​data analysis module with the preset farmland ecological environment standards and crop growth requirements to formulate targeted farmland ecological environment regulation strategies. For example, when the soil moisture is insufficient, an irrigation plan is generated; when the risk of pests and diseases is predicted, pest and disease prevention measures are formulated.

[0034] In one embodiment, the precise control execution module includes intelligent irrigation equipment, fertilizing machinery, pest control drones, etc. According to the control strategy generated by the decision support module, the precise control execution module automatically performs corresponding operations. For example, the intelligent irrigation equipment realizes precise irrigation based on soil moisture data, and the fertilizing machinery performs variable fertilization according to the soil nutrient status.

[0035] In one embodiment, the user interaction module provides web and mobile applications to facilitate agricultural managers, farmers and other users to view farmland ecological environment monitoring data, AI analysis results and control strategy execution progress in real time. Users can also manually adjust control parameters through this module, or submit monitoring and control requirements for specific farmland areas.

[0036] The method for intelligent monitoring and precise control of farmland ecological environment based on multi-source remote sensing and AI includes the following steps:

[0037] S1. Multi-source remote sensing data acquisition: According to the set time and space plan, the multi-source remote sensing data acquisition module is used to obtain farmland ecological environment related data from satellites, drones and ground equipment;

[0038] S2, data preprocessing, transmits the collected data to the data preprocessing module, performs radiation correction, geometric correction, atmospheric correction and other processing, and generates a standardized multi-source remote sensing data set;

[0039] S3, AI data analysis: input the preprocessed data set into the deep learning model of the AI ​​data analysis module. Through model training and inference, it realizes intelligent analysis and prediction of farmland ecological environment factors;

[0040] S4, Decision-making: The decision support module formulates scientific and reasonable farmland ecological environment control decisions based on AI analysis results, combined with farmland ecological environment standards and crop growth requirements;

[0041] S5, precise control execution: the precise control execution module receives the control decision instruction, automatically starts the corresponding equipment, and precisely controls the farmland ecological environment;

[0042] S6, feedback and optimization, real-time monitoring of control effects, and transmission of feedback data to the system for optimizing AI analysis models and control strategies to form closed-loop management.

[0043] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various modifications and substitutions within the technical scope disclosed in the present invention, and such modifications and substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. An intelligent monitoring and precise control system for farmland ecological environment based on multi-source remote sensing and AI, characterized by: It includes the following modules: multi-source remote sensing data acquisition module, data preprocessing module, AI data analysis module, decision support module, precise control execution module and user interaction module.

2. The farmland ecological environment intelligent monitoring and precise control system based on multi-source remote sensing and AI according to claim 1 is characterized by: The multi-source remote sensing data acquisition module integrates satellite remote sensing, UAV remote sensing and ground remote sensing equipment. Satellite remote sensing uses high-resolution optical satellites and multispectral satellites to obtain macro data such as land use and vegetation coverage in large-scale farmland areas; UAV remote sensing is equipped with hyperspectral cameras, thermal infrared cameras, etc., which can collect high-temporal and spatial resolution data of specific farmland areas at low altitudes to obtain detailed information such as crop growth conditions, diseases and pests; ground remote sensing equipment, such as portable spectrometers, soil moisture sensors, etc., is used to collect local soil physical and chemical properties of farmland, near-ground meteorological data, etc.

3. The farmland ecological environment intelligent monitoring and precise control system based on multi-source remote sensing and AI according to claim 1 is characterized by: The data preprocessing module performs preprocessing operations such as radiation correction, geometric correction, and atmospheric correction on the collected multi-source remote sensing data to eliminate data errors caused by factors such as sensor differences, terrain undulations, and atmospheric interference, unify data formats and coordinate systems, and improve data quality and availability.

4. The farmland ecological environment intelligent monitoring and precise control system based on multi-source remote sensing and AI according to claim 1 is characterized by: The AI ​​data analysis module constructs a farmland ecological environment analysis model based on deep learning algorithms, such as convolutional neural networks (CNN) for crop disease and pest identification, semantic segmentation models for land use classification, and recurrent neural networks (RNN) for predicting crop growth trends. The preprocessed data is input into the model to achieve intelligent identification, classification, and prediction of farmland ecological environment elements, and analyze information such as crop growth status, soil moisture conditions, and the risk of disease and pest occurrence.

5. The farmland ecological environment intelligent monitoring and precise control system based on multi-source remote sensing and AI according to claim 1 is characterized by: The decision support module combines the output of the AI ​​data analysis module with the preset farmland ecological environment standards and crop growth requirements to formulate targeted farmland ecological environment regulation strategies. For example, when soil moisture is insufficient, an irrigation plan is generated; when the risk of pests and diseases is predicted, pest and disease prevention measures are formulated.

6. The farmland ecological environment intelligent monitoring and precise control system based on multi-source remote sensing and AI according to claim 1 is characterized by: The precise control execution module includes intelligent irrigation equipment, fertilizing machinery, pest control drones, etc. According to the control strategy generated by the decision support module, the precise control execution module automatically performs corresponding operations. For example, intelligent irrigation equipment realizes precise irrigation based on soil moisture data, and fertilizing machinery performs variable fertilization according to soil nutrient conditions.

7. The farmland ecological environment intelligent monitoring and precise control system based on multi-source remote sensing and AI according to claim 1 is characterized by: The user interaction module provides web and mobile applications to facilitate agricultural managers, farmers and other users to view farmland ecological environment monitoring data, AI analysis results and control strategy execution progress in real time. Users can also manually adjust control parameters through this module, or submit monitoring and control requirements for specific farmland areas.

8. A method for intelligent monitoring and precise control of farmland ecological environment based on multi-source remote sensing and AI, in conjunction with the intelligent monitoring and precise control system for farmland ecological environment based on multi-source remote sensing and AI as described in any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Multi-source remote sensing data acquisition: According to the set time and space plan, the multi-source remote sensing data acquisition module is used to obtain farmland ecological environment related data from satellites, drones and ground equipment; S2, data preprocessing, transmits the collected data to the data preprocessing module, performs radiation correction, geometric correction, atmospheric correction and other processing, and generates a standardized multi-source remote sensing data set; S3, AI data analysis, inputs the preprocessed data set into the deep learning model of the AI ​​data analysis module. Through model training and inference, it realizes intelligent analysis and prediction of farmland ecological environment factors; S4, Decision-making: The decision support module formulates scientific and reasonable farmland ecological environment control decisions based on AI analysis results, combined with farmland ecological environment standards and crop growth requirements; S5, precise control execution: the precise control execution module receives the control decision instruction, automatically starts the corresponding equipment, and precisely controls the farmland ecological environment; S6. Feedback and optimization: monitor the control effect in real time and transmit feedback data to the system to optimize the AI ​​analysis model and control strategy to form a closed-loop management.

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