Precision in Rice Variety Classification using Stacking-Based Ensemble Learning

• URL: http://arxiv.org/abs/2609.10524v1
• Date: Wed, 09 Sep 2026 17:53:50 GMT
• Title: Precision in Rice Variety Classification using Stacking-Based Ensemble Learning
• Abstract summary: This study introduces a comprehensive rice variety identification framework designed to enhance transparency and quality assurance.<n>We developed a stacked ensemble model tailored for rice variety classification and curated a dataset comprising 20 rice varieties.<n>The proposed approach achieved an unprecedented classification accuracy of 100%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rice, a staple food for a significant portion of the global population, exhibits remarkable diversity in its varieties, presenting substantial challenges for accurate identification by consumers, traders, and farmers. This complexity often facilitates fraudulent practices, such as the unauthorized mixing of rice types, which undermines quality and trust in the supply chain. Despite its critical importance, existing research falls short of providing robust and efficient methods for precise rice variety classification based on external characteristics like color, size, and texture. To address this gap, our study introduces a comprehensive rice variety identification framework designed to enhance transparency and quality assurance. We developed a stacked ensemble model tailored for rice variety classification and curated a comprehensive dataset comprising 20 rice varieties, each distinguished by unique visual attributes. The proposed approach achieved an unprecedented classification accuracy of 100%. Furthermore, we integrated our model into a mobile application, enabling even novice users to effortlessly identify rice varieties using grain images from a smartphone camera. These findings underscore the transformative potential of advanced machine learning techniques in mitigating fraudulent practices and ensuring stringent rice quality control. Our work holds significant implications for agricultural stakeholders, paving the way for automated crop identification systems and advancing precision agriculture practices.

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