Enhancing rice seed purity recognition accuracy based on optimal feature selection
This study proposes a robust and accurate approach for classifying rice variety purity to meet stringent agricultural standards. To achieve this, we construct a comprehensive dataset by leveraging diverse types of features encompassing morphological properties, overall image structure, texture infor...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000536 |
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| author | Thi-Thu-Hong Phan Le Huu Bao Nguyen |
| author_facet | Thi-Thu-Hong Phan Le Huu Bao Nguyen |
| author_sort | Thi-Thu-Hong Phan |
| collection | DOAJ |
| description | This study proposes a robust and accurate approach for classifying rice variety purity to meet stringent agricultural standards. To achieve this, we construct a comprehensive dataset by leveraging diverse types of features encompassing morphological properties, overall image structure, texture information, and color distribution from rice seeds. Subsequently, we employ advanced feature selection techniques, including filter methods (Correlation, Chi-square, ANOVA), wrapper methods (Recursive Feature Elimination — RFE), and embedded methods (Random Forest, Decision Trees), to identify the most significant features. Through rigorous experimentation with eight machine learning algorithms, we find that using Random Forest for feature selection, in combination with the SVM classifier, yields the best performance. Specifically, Random Forest reduces the feature set by more than half, from 172 to 80, remarkably enhancing classification accuracy from 94.73% to 96.11%. This paper highlights the potential of the proposed method to offer a robust and efficient solution for rice seed purity identification in agricultural applications, while also opening up new horizons for similar studies. |
| format | Article |
| id | doaj-art-a91dc02832424aa7bcb36ea59cf3fc87 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-a91dc02832424aa7bcb36ea59cf3fc872025-02-05T04:31:23ZengElsevierEcological Informatics1574-95412025-05-0186103044Enhancing rice seed purity recognition accuracy based on optimal feature selectionThi-Thu-Hong Phan0Le Huu Bao Nguyen1Corresponding author.; Department of Artificial Intelligence, FPT University, FPT City, Da Nang, 550000, Viet NamDepartment of Artificial Intelligence, FPT University, FPT City, Da Nang, 550000, Viet NamThis study proposes a robust and accurate approach for classifying rice variety purity to meet stringent agricultural standards. To achieve this, we construct a comprehensive dataset by leveraging diverse types of features encompassing morphological properties, overall image structure, texture information, and color distribution from rice seeds. Subsequently, we employ advanced feature selection techniques, including filter methods (Correlation, Chi-square, ANOVA), wrapper methods (Recursive Feature Elimination — RFE), and embedded methods (Random Forest, Decision Trees), to identify the most significant features. Through rigorous experimentation with eight machine learning algorithms, we find that using Random Forest for feature selection, in combination with the SVM classifier, yields the best performance. Specifically, Random Forest reduces the feature set by more than half, from 172 to 80, remarkably enhancing classification accuracy from 94.73% to 96.11%. This paper highlights the potential of the proposed method to offer a robust and efficient solution for rice seed purity identification in agricultural applications, while also opening up new horizons for similar studies.http://www.sciencedirect.com/science/article/pii/S1574954125000536Feature selectionMachine learning methodsSynergized featuresImproved performanceRice seed purity identification |
| spellingShingle | Thi-Thu-Hong Phan Le Huu Bao Nguyen Enhancing rice seed purity recognition accuracy based on optimal feature selection Ecological Informatics Feature selection Machine learning methods Synergized features Improved performance Rice seed purity identification |
| title | Enhancing rice seed purity recognition accuracy based on optimal feature selection |
| title_full | Enhancing rice seed purity recognition accuracy based on optimal feature selection |
| title_fullStr | Enhancing rice seed purity recognition accuracy based on optimal feature selection |
| title_full_unstemmed | Enhancing rice seed purity recognition accuracy based on optimal feature selection |
| title_short | Enhancing rice seed purity recognition accuracy based on optimal feature selection |
| title_sort | enhancing rice seed purity recognition accuracy based on optimal feature selection |
| topic | Feature selection Machine learning methods Synergized features Improved performance Rice seed purity identification |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000536 |
| work_keys_str_mv | AT thithuhongphan enhancingriceseedpurityrecognitionaccuracybasedonoptimalfeatureselection AT lehuubaonguyen enhancingriceseedpurityrecognitionaccuracybasedonoptimalfeatureselection |