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Rice is the most essential food crop and contributes to agricultural economic development in Vietnam. With climate changes, accurate classification of rice types is essential to ensure food security and recognize the rice quality. Leveraging Fine-tuned Classical Machine Learning, this study focuses on two rice varieties: Osmancik and Cameo. The classification processing conducted data preprocessing and used GridSearchCV to indicate the best hyperparameters for some classical algorithms. Results showed that the SVM algorithm obtained 92.94% accuracy in classifying the above two rice varieties. Accurate identification of rice varieties through fine-tuning parameters ensures precision in rice assessment and grading, meets different consumer needs and promotes innovation in cultivation and rice processing. This research promotes the application of artificial intelligence problems in classifying agricultural products, supporting agricultural economic development, and classifying crops in agriculture.

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Rice Varieties Classification Combining Scalers and Fine-Tuned Classical Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Hai Thanh Nguyen,
  • Phat Tuan Hong Nguyen,
  • Anh Kim Su

摘要

Rice is the most essential food crop and contributes to agricultural economic development in Vietnam. With climate changes, accurate classification of rice types is essential to ensure food security and recognize the rice quality. Leveraging Fine-tuned Classical Machine Learning, this study focuses on two rice varieties: Osmancik and Cameo. The classification processing conducted data preprocessing and used GridSearchCV to indicate the best hyperparameters for some classical algorithms. Results showed that the SVM algorithm obtained 92.94% accuracy in classifying the above two rice varieties. Accurate identification of rice varieties through fine-tuning parameters ensures precision in rice assessment and grading, meets different consumer needs and promotes innovation in cultivation and rice processing. This research promotes the application of artificial intelligence problems in classifying agricultural products, supporting agricultural economic development, and classifying crops in agriculture.