A method and system for classifying japonica rice seeds based on support vector machines

By integrating elite optimization strategies and hidden mechanisms to improve the rime ice optimization algorithm and optimizing support vector machine parameters, the problem of reduced classification accuracy of rice and japonica rice seeds was solved, achieving higher classification accuracy and robustness.

CN121505461BActive Publication Date: 2026-03-24JILIN AGRICULTURAL UNIV
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Patent Information

Application Number
CN202610036751.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-13
Publication Date
2026-03-24
Estimated Expiration
2046-01-13

AI Technical Summary

Technical Problem

In existing technologies, the reliance on experience to select support vector machine parameters leads to a decrease in the accuracy of rice and japonica rice seed classification, making it impossible to find the optimal solution.

Method used

An improved rime optimization algorithm is adopted by combining elite optimization strategy and hidden mechanism. The penalty coefficient and kernel function parameters of support vector machine are optimized, and a mapping law is designed to perform iterative optimization to generate the optimal solution.

Benefits of technology

It improves the accuracy and robustness of rice and japonica rice seed classification, enhances the algorithm's global exploration and adaptive adjustment capabilities, and avoids the blindness of manual parameter tuning.

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Abstract

A rice japonica seed classification method and system based on a support vector machine belong to the technical field of computing intelligence and optimization algorithm, and solve the technical problem that the existing technology depends on experience to select support vector machine parameters, thus missing the optimal solution to the problem, resulting in reduced rice japonica seed classification accuracy. High spectral image data of rice japonica seeds is collected; based on a fusion elite optimization strategy, a fog optimization algorithm is initialized to generate an initial solution; a hidden mechanism is used to improve the greedy strategy for population updating; the improved fog optimization algorithm is used to optimize the penalty coefficient and kernel function parameters in the support vector machine, and a mapping rule is designed to iteratively optimize the kernel function in the support vector machine to obtain an optimal solution; the optimal solution is set as the support vector machine parameters, a rice japonica seed variety classification model is constructed, and rice japonica seed variety classification is realized. The present application is used to realize efficient and accurate rice japonica seed classification.
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