Phan Thi-Thu-Hong, Vo Quoc-Trinh, Nguyen Huu-Du
Artificial Intelligence Department, FPT University, Da Nang, 550000, Vietnam.
School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam.
Heliyon. 2024 Jul 6;10(14):e33941. doi: 10.1016/j.heliyon.2024.e33941. eCollection 2024 Jul 30.
In the grain industry, identifying seed purity is a crucial task because it is an important factor in evaluating seed quality. For rice seeds, this attribute enables the minimization of unexpected influences of other varieties on rice yield, nutrient composition, and price. However, in practice, they are often mixed with seeds from other varieties. This study proposes a novel method for automatically identifying the purity of a specific rice variety using hybrid machine learning algorithms. The core concept involves leveraging deep learning architectures to extract pertinent features from raw data, followed by the application of machine learning algorithms for classification. Several experiments are conducted to evaluate the performance of the proposed model through practical implementation. The results demonstrate that the novel method substantially outperformed the existing methods, demonstrating the potential for effective rice seed purity identification systems.
在粮食行业中，识别种子纯度是一项至关重要的任务，因为它是评估种子质量的一个重要因素。对于水稻种子而言，这一属性能够将其他品种对水稻产量、营养成分和价格的意外影响降至最低。然而，在实际操作中，它们常常与其他品种的种子混合在一起。本研究提出了一种使用混合机器学习算法自动识别特定水稻品种纯度的新方法。其核心概念是利用深度学习架构从原始数据中提取相关特征，随后应用机器学习算法进行分类。通过实际实施进行了多项实验，以评估所提出模型的性能。结果表明，该新方法显著优于现有方法，展现了有效水稻种子纯度识别系统的潜力。
