Research on Feature Optimization Algorithm for Rice Crops Under Mountainous Conditions

Yuyao Wang

doi:10.54097/5w6t9v29

Authors

Yuyao Wang

DOI:

https://doi.org/10.54097/5w6t9v29

Keywords:

Gray Wolf Optimization, Feature Selection, Random Forest, Relief Algorithm, Rice Recognition.

Abstract

To improve the accuracy and efficiency of rice recognition in mountainous areas, this paper proposes a feature selection method combining the Gray Wolf Optimization (GWO) algorithm and the Random Forest classifier. First, the Relief algorithm is applied to further filter the initial feature set by calculating feature weights and setting appropriate thresholds, optimizing the feature subset's dimensionality and classification accuracy. Then, the GWO algorithm is used to further optimize the feature subset, reducing feature redundancy and correlation, and improving the quality of the final feature set. The GWO algorithm simulates the hunting behavior of gray wolf packs, dynamically adjusting the positions of the wolves to gradually approach the optimal solution. During the optimization process, the Random Forest classifier serves as the fitness function to evaluate the classification accuracy of each feature subset. Through an iterative update process, the GWO_RF algorithm successfully selects the optimal feature subset, achieving high recognition accuracy with reduced computational complexity. Experimental results show that the proposed method significantly improves recognition accuracy and effectively reduces computation time, providing a new approach for the efficient analysis of large-scale remote sensing images.

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