[论文] Precision in Rice Variety Classification using Stacking-Based Ensemble Learning (arXiv:2609.10524)

论文概要

研究领域: CV
作者: Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Golam Moazzam, Mohammad Shorif Uddin
发布时间: 2026-09-09
arXiv: 2609.10524

中文摘要

水稻作为全球大量人口的主食，品种多样性给准确识别带来巨大挑战，也为掺假等欺诈行为提供了可乘之机。本文提出一种全面的水稻品种识别框架，采用基于堆叠的集成学习模型，在包含20个水稻品种的综合数据集上实现了前所未有的100%分类准确率。该模型已被集成到移动应用中，使新手用户也能通过智能手机相机轻松识别水稻品种。这项工作展示了先进机器学习技术在减少欺诈行为和确保严格质量控制方面的变革潜力。

原文摘要

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.

自动采集于 2026-09-11

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