Automatic Monitoring System for Seed Germination Test Based on Deep Learning
Germination test is an irreplaceable step in seed selection and breeding. The current traditional germination test method must rely on experienced professional technicians to repeatedly classify and count the germination status of seeds and count the germination rate at different moments during the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4678316 |
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| author | Qi Peng Lifen Tu Yunyun Wu Zhenyu Yu Gerui Tang Wei Song |
| author_facet | Qi Peng Lifen Tu Yunyun Wu Zhenyu Yu Gerui Tang Wei Song |
| author_sort | Qi Peng |
| collection | DOAJ |
| description | Germination test is an irreplaceable step in seed selection and breeding. The current traditional germination test method must rely on experienced professional technicians to repeatedly classify and count the germination status of seeds and count the germination rate at different moments during the whole test process (usually takes 2 to 10 days). Currently, only the German seed germination detection system (Germination Scanalyzer) can solve this problem, but it is so expensive that it has not been practically promoted. In order to improve breeding efficiency, an automatic monitoring system for seed germination tests based on deep learning was designed. It includes a modified germination thermostat, connected with a three-dimensional movable camera bin with built-in camera; a multifunctional software system capable of online, offline, and sentinel mode monitoring; a dense distributed small target detection algorithm (DDST-CenterNet) for seed germination monitoring systems. The system test results show that the seed germination test automatic monitoring system is low cost, does not depend on the seed background, light, camera model, and other usage environments, and has high scalability. The DDST-CenterNet algorithm proposed in this paper can still maintain high accuracy and good stability in the process of seed target detection and classification as the number and density of seeds increase, which is suitable for a special application scenario of seed germination test. In addition, the algorithm has high computational efficiency and can give detection results at a frame rate of not less than 10fps, which can be used in practical applications. |
| format | Article |
| id | doaj-art-7187017c97ea4c85a61c94457f3ddf2f |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-7187017c97ea4c85a61c94457f3ddf2f2025-02-03T01:24:29ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/4678316Automatic Monitoring System for Seed Germination Test Based on Deep LearningQi Peng0Lifen Tu1Yunyun Wu2Zhenyu Yu3Gerui Tang4Wei Song5School of Physics and Electronic Information EngineeringSchool of Physics and Electronic Information EngineeringSchool of Computer and Information ScienceSchool of Physics and Electronic Information EngineeringSchool of Physics and Electronic Information EngineeringSchool of Physics and Electronic Information EngineeringGermination test is an irreplaceable step in seed selection and breeding. The current traditional germination test method must rely on experienced professional technicians to repeatedly classify and count the germination status of seeds and count the germination rate at different moments during the whole test process (usually takes 2 to 10 days). Currently, only the German seed germination detection system (Germination Scanalyzer) can solve this problem, but it is so expensive that it has not been practically promoted. In order to improve breeding efficiency, an automatic monitoring system for seed germination tests based on deep learning was designed. It includes a modified germination thermostat, connected with a three-dimensional movable camera bin with built-in camera; a multifunctional software system capable of online, offline, and sentinel mode monitoring; a dense distributed small target detection algorithm (DDST-CenterNet) for seed germination monitoring systems. The system test results show that the seed germination test automatic monitoring system is low cost, does not depend on the seed background, light, camera model, and other usage environments, and has high scalability. The DDST-CenterNet algorithm proposed in this paper can still maintain high accuracy and good stability in the process of seed target detection and classification as the number and density of seeds increase, which is suitable for a special application scenario of seed germination test. In addition, the algorithm has high computational efficiency and can give detection results at a frame rate of not less than 10fps, which can be used in practical applications.http://dx.doi.org/10.1155/2022/4678316 |
| spellingShingle | Qi Peng Lifen Tu Yunyun Wu Zhenyu Yu Gerui Tang Wei Song Automatic Monitoring System for Seed Germination Test Based on Deep Learning Journal of Electrical and Computer Engineering |
| title | Automatic Monitoring System for Seed Germination Test Based on Deep Learning |
| title_full | Automatic Monitoring System for Seed Germination Test Based on Deep Learning |
| title_fullStr | Automatic Monitoring System for Seed Germination Test Based on Deep Learning |
| title_full_unstemmed | Automatic Monitoring System for Seed Germination Test Based on Deep Learning |
| title_short | Automatic Monitoring System for Seed Germination Test Based on Deep Learning |
| title_sort | automatic monitoring system for seed germination test based on deep learning |
| url | http://dx.doi.org/10.1155/2022/4678316 |
| work_keys_str_mv | AT qipeng automaticmonitoringsystemforseedgerminationtestbasedondeeplearning AT lifentu automaticmonitoringsystemforseedgerminationtestbasedondeeplearning AT yunyunwu automaticmonitoringsystemforseedgerminationtestbasedondeeplearning AT zhenyuyu automaticmonitoringsystemforseedgerminationtestbasedondeeplearning AT geruitang automaticmonitoringsystemforseedgerminationtestbasedondeeplearning AT weisong automaticmonitoringsystemforseedgerminationtestbasedondeeplearning |