Integrated system and method for agriculture and forestry monitoring based on multi-source remote sensing data

Through multi-source remote sensing data fusion processing and linear planning prediction, the problem of inaccurate monitoring in traditional agricultural and forestry resource management is solved, accurate monitoring and prediction of agricultural and forestry resources is achieved, agricultural production efficiency is improved, and technical support is provided for sustainable development.

CN119227913BActive Publication Date: 2025-03-28AEROSPACE XINDE ZHITU (BEIJING) TECH CO LTD
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Patent Information

Application Number
CN202411757807.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2025-03-28
Estimated Expiration
2044-12-03

AI Technical Summary

Technical Problem

Traditional agricultural and forestry resource management methods have problems such as limited monitoring scope, untimely data updates, and incomplete information, resulting in inaccurate agricultural and forestry monitoring and prediction.

Method used

The integrated agricultural and forestry monitoring system and method based on multi-source remote sensing data are adopted to obtain multi-source images through multiple camera devices, and the fusion process generates panoramic images, detect pest and disease areas, acquire climate data and compare them with standard data, and linearly plan and predict agricultural and forestry yields based on pest and climate attributes.

Benefits of technology

Accurate monitoring and prediction of agricultural and forestry resources has been achieved, the efficiency and benefits of agricultural production have been improved, and technical support has been provided for the sustainable development of agriculture.

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Abstract

The present application provides an integrated agricultural and forestry monitoring system and method based on multi-source remote sensing data. A variety of camera devices acquire multi-source images of the monitoring area; based on the attribute information of the camera devices and the feature information of the multi-source images, the multi-source images are fused to generate a panoramic image of the monitoring area; the image features of the panoramic image are detected to determine the pest and disease area, and the corresponding pest attributes are determined according to the image features of the pest and disease area; the climate data of a preset period is acquired, and the climate data is compared with the standard data to determine the climate attributes; the agricultural and forestry yields of the monitoring area are predicted according to the pest attributes and climate attributes of each pest and disease area. The above process combines the attributes of the camera devices and the image features to generate a panoramic view of the monitoring area, so as to accurately detect the pest and disease area and its attributes. At the same time, the climate attributes are determined by comparing the current climate with the standard data, and variable factors such as pests and climate are comprehensively considered to achieve efficient and accurate yield prediction.
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