Agricultural non-point source in-river pollution flux real-time monitoring method

Through multispectral cameras and machine learning technology, agricultural non-point source pollution can be monitored in real time, solving the problems of insufficient timeliness and accuracy in existing technologies. It realizes efficient and intelligent agricultural non-point source pollution monitoring, can quickly identify pollution sources and paths, and generate early warning information.

CN120668587APending Publication Date: 2025-09-19BEIJING NORMAL UNIV AT ZHUHAI +1
3 Cites -1 Cited by

Patent Information

Application Number
CN202511157237.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-19
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

Existing technologies in agricultural non-point source pollution monitoring have problems such as poor timeliness, insufficient representativeness, large human errors, and imperfect integration of remote sensing data and field monitoring data, which lead to inaccurate monitoring results.

Method used

A multispectral camera is used to acquire high-resolution remote sensing images, combined with machine learning and neural network models to identify crop areas and river inflow areas, monitor crop growth stages, predict nitrogen content, calculate pollution parameters, generate early warning information, and realize real-time monitoring of agricultural non-point source pollution.

Benefits of technology

It has achieved precise monitoring of agricultural non-point source pollution, reduced human intervention, improved monitoring efficiency and accuracy, can quickly process large amounts of remote sensing images, adapt to the monitoring needs of large-scale agricultural areas, and promptly discover pollution trends and potential risks.

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Abstract

The invention relates to the technical field of pollution monitoring, and discloses a real-time monitoring method for agricultural non-point source in-river pollution flux. The method comprises the following steps: shooting an agricultural area to be monitored, generating a plurality of area remote sensing images, and respectively extracting remote sensing data and geographical distribution images; identifying crop regions and types, obtaining crop output concentrations, establishing a crop non-point source output library, calculating runoff volume, and outputting non-point source loads of different crop regions; comparing and analyzing the pollution load with a pollution safety standard, and outputting a first pollution result of the crop area; calculating confluence time, generating mixed surface source load capacity of different water systems and sections, calculating unit pollution data of pollutants within monitoring time, and generating pollution parameters corresponding to the river entering area; and calculating a first difference value between the pollution parameter and the pollution safety standard, setting the first difference value as a river pollution amount, and generating early warning information. According to the invention, the efficiency and accuracy of agricultural non-point source in-river pollution flux real-time monitoring are improved.
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Citation Information

Patent Citations

  • Monitoring method and system for determining drainage basin agricultural non-point source pollution load

    CN111912947A

  • River entry coefficient test method and total pollution calculation method for agricultural non-point source pollution

    CN118409061A

  • Remote sensing monitoring and early warning method and system for agricultural non-point source pollution

    CN116401557A

Cited By

  • Regional total nitrogen non-point source pollution intensity analysis method

    CN120870494A