ArticleHeliyon2024
A novel method for identifying rice seed purity using hybrid machine learning algorithms.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Optimizing XGBoost via mSMA_plus: A Novel Meta-Heuristic Approach for High-Precision Multiclass Dry Bean Classification.Biomimetics (Basel, Switzerland) · 2026Article
- High-resolution RGB image dataset for wheat seed varietal identification and purity assessment.Data in brief · 2025Article
- Real-time segmentation and phenotypic analysis of rice seeds using YOLOv11-LA and RiceLCNN.Frontiers in plant science · 2025Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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