Automated seed counting using image processing and deep learning
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| Publicat a: | Frontiers in Plant Science vol. 16 (Aug 2025), p. 1659781-1659797 |
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| Autor principal: | |
| Altres autors: | , , , , , , , , |
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Frontiers Media SA
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| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 3273795074 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1664-462X | ||
| 024 | 7 | |a 10.3389/fpls.2025.1659781 |2 doi | |
| 035 | |a 3273795074 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 100 | 1 | |a Zu, Qiuyu |u School of Agriculture, Jilin Agricultural Science and Technology College, Jilin City, China, Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 245 | 1 | |a Automated seed counting using image processing and deep learning | |
| 260 | |b Frontiers Media SA |c Aug 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a IntroductionAccurate seed counting is an essential task in agricultural research and farming, supporting activities such as crop breeding, yield prediction, and weed management. Traditional manual seed counting, while accurate, is time-consuming, labor-intensive, and prone to human error, particularly for large quantities of micro-sized seeds.MethodsThis study developed two automated computer vision approaches integrated into a mobile application (app) for seed counting: one utilizing image processing (IP) and the other based on deep learning (DL). These methods aim to address the limitations of traditional manual counting by providing automated, efficient alternatives.ResultsThe IP-based method demonstrated high accuracy comparable to manual counting and offered substantial time savings. However, its reliance on controlled environmental conditions, such as uniform lighting, limits its versatility for field apps. The DL-based method excelled in speed and scalability, processing counts in as little as 0.33 seconds per image, but its accuracy was inconsistent for visually complex or densely clustered seeds.DiscussionBoth automated methods significantly enhance the efficiency of seed counting, providing a practical and accessible solution for various agricultural contexts. The integration of these methods into a mobile app streamlines seed counting for laboratory research, field studies, seed production, and breeding trials, offering a transformative approach to modernizing seed counting practices while reducing time and labor requirements. | |
| 651 | 4 | |a United States--US | |
| 651 | 4 | |a Kentucky | |
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Plant breeding | ||
| 653 | |a Applications programs | ||
| 653 | |a Agricultural research | ||
| 653 | |a Weed control | ||
| 653 | |a Corn | ||
| 653 | |a Mobile computing | ||
| 653 | |a Environmental conditions | ||
| 653 | |a Crops | ||
| 653 | |a Image processing | ||
| 653 | |a Computer vision | ||
| 653 | |a Automation | ||
| 653 | |a Efficiency | ||
| 653 | |a Seeds | ||
| 653 | |a Modernization | ||
| 653 | |a Lighting | ||
| 653 | |a Labor | ||
| 653 | |a Methods | ||
| 653 | |a Human error | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Liu, Teng |u Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 700 | 1 | |a Zhu, Wenpeng |u Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 700 | 1 | |a Pan, Yan |u Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 700 | 1 | |a Wang, Jinxu |u Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 700 | 1 | |a Song, Xinru |u Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 700 | 1 | |a Yu, Jialin |u Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, China | |
| 700 | 1 | |a Dang, Shu |u School of Agriculture, Jilin Agricultural Science and Technology College, Jilin City, China | |
| 700 | 1 | |a Yu, Xiaoming |u School of Agriculture, Jilin Agricultural Science and Technology College, Jilin City, China | |
| 700 | 1 | |a Zhang, Zhenyu |u School of Agriculture, Jilin Agricultural Science and Technology College, Jilin City, China | |
| 773 | 0 | |t Frontiers in Plant Science |g vol. 16 (Aug 2025), p. 1659781-1659797 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3273795074/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3273795074/fulltext/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3273795074/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |