A novel method for identifying rice seed purity using hybrid machine learning algorithms.

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.