Eucalyptus canopy leaf nutrition content inversion method and system based on multi-feature fusion

By using multi-feature fusion and random forest regression algorithms, the problem of insufficient accuracy in monitoring the nutrient content of eucalyptus leaves in UAV remote sensing technology is solved, realizing efficient and comprehensive nutrient content monitoring and management, and supporting precision fertilization.

CN122313129APending Publication Date: 2026-06-30GUILIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202610366719.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-24
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

In the monitoring of nutrient content in eucalyptus leaves using existing UAV remote sensing technology, the use of single vegetation indices or texture features leads to insufficient accuracy and stability of the inversion model, making it difficult to meet the needs of precise nutrient management. Furthermore, the traditional model has insufficient fitting ability.

Method used

A multi-feature fusion method, combining vegetation index and texture features, is employed. Features are selected using a mutual information algorithm, and a random forest regression model is constructed. Feature selection and parameter optimization are then performed to retrieve the nitrogen, phosphorus, and potassium content of eucalyptus leaves. This method includes UAV multispectral image data acquisition, preprocessing, feature extraction, fusion, model construction, and optimization.

Benefits of technology

It significantly improves the accuracy and stability of nutrient content inversion in eucalyptus leaves, enabling rapid, comprehensive, and non-contact monitoring, reducing costs, providing spatial distribution maps of nutrient content, and supporting precision fertilization and nutrient management.

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Abstract

The invention discloses a eucalyptus canopy leaf nutrition content inversion method and system based on multi-feature fusion, and belongs to the technical field of unmanned aerial vehicle remote sensing and forestry information. The method comprises the following steps: acquiring and preprocessing multi-spectral image data of an unmanned aerial vehicle; extracting vegetation index features and texture features of the eucalyptus canopies; screening fusion features based on mutual information, and constructing a multi-feature fusion data set; utilizing a random forest regression algorithm to construct an inversion model of the nitrogen, phosphorus and potassium contents of the eucalyptus leaves; and carrying out precision verification and optimization on the inversion model. According to the method, the spectral information and the spatial structure information are fused, so that the information gap of a single feature is effectively made up, the inversion precision of the nutrient content of the eucalyptus canopy leaves is remarkably improved, and reliable technical support is provided for precise nutrient management of a eucalyptus man-made forest.
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