基于堆叠集成学习的水稻品种分类精度研究
Precision in Rice Variety Classification using Stacking-Based Ensemble Learning
- Jahangirnagar University（贾汉吉尔纳加尔大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出堆叠集成模型，基于20个水稻品种的视觉特征实现100%分类准确率，并集成至移动应用，助力精准农业与防欺诈。
AI中文摘要：
水稻是全球相当一部分人口的主粮，其品种表现出显著的多样性，这给消费者、贸易商和农民准确识别带来了巨大挑战。这种复杂性往往助长了欺诈行为，例如未经授权混合不同种类的大米，从而损害了供应链中的质量和信任。尽管这一问题至关重要，但现有研究在基于颜色、大小和纹理等外部特征进行精确水稻品种分类方面，尚未提供稳健且高效的方法。为弥补这一空白，本研究引入了一个全面的水稻品种识别框架，旨在提升透明度和质量保障。我们开发了一个针对水稻品种分类量身定制的堆叠集成模型，并整理了一个包含20个水稻品种的综合数据集，每个品种都具有独特的视觉属性。所提出的方法实现了前所未有的100%分类准确率。此外，我们将该模型集成到一款移动应用程序中，使即使是新手用户也能通过智能手机摄像头拍摄的谷物图像轻松识别水稻品种。这些发现凸显了先进机器学习技术在减少欺诈行为和确保严格水稻质量控制方面的变革潜力。我们的工作对农业利益相关者具有重大意义，为自动化作物识别系统铺平了道路，并推动了精准农业实践的发展。
英文摘要：
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.
