Enhancing Sustainable Rice Grain Quality Analysis with Efficient SVM Optimization Using Genetic Algorithm

Abstract

The most extensively cultivated and indispensable crops in agriculture, ensuring sustainable rice cultivation practices is crucial, and accurately determining rice grain quality is a critical component of this effort. To achieve this goal, researchers have developed a novel technique that combines Support Vector Machine classification with Genetic Algorithm optimization. Using this technique, they were able to categorize rice grain quality with 92.81% accuracy, which improved to 93.31% after optimizing feature weights and classification configurations using the Genetic Algorithm. Compared to other classification algorithms, SVM showed the highest accuracy value, while k-NN had the lowest accuracy of 88.32%. The study’s results suggest that the combination of SVM classification with Genetic Algorithm optimization is an effective method for accurately analyzing rice grain quality. Furthermore, the SVM-based method outperformed other commonly used classification algorithms in terms of accuracy. This study’s findings could be valuable in promoting sustainable rice cultivation practices by improving the accuracy and efficiency of rice grain quality analysis and enhancing the overall productivity of the rice cultivation process. To further improve the classification accuracy of rice grain quality, the researchers employed the Genetic Algorithm optimization method to refine the feature weights and classification configurations.