Crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring

Through multi-spectral UAV monitoring and intelligent algorithms, a high-precision crop growth status prediction system was built, solving the problems of different fertility stages and the impact of moisture and pests, and achieving accurate crop growth prediction.

CN120508784AActive Publication Date: 2025-08-19JIANGSU SANSSAN INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511007431.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-22
Publication Date
2025-08-19
Estimated Expiration
2045-07-22

AI Technical Summary

Technical Problem

The prior art fails to effectively consider the impact of different fertility stages and moisture, pests and diseases on NDVI in crop growth prediction, resulting in low accuracy of prediction results.

Method used

Through multi-spectral UAV monitoring, the growth status data of crops in staged growth periods under standard growth environments are configured, multi-dimensional data is collected, and the contribution of vegetation index is determined using factor analysis and principal component analysis algorithms. Combined with LiDAR data and simulation, reinforcement learning model is constructed for prediction.

Benefits of technology

Accurate prediction of crop growth status is achieved, the prediction accuracy and adaptability are significantly improved, and the impact of moisture and pests and diseases is dynamically reflected.

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Abstract

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.
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