YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor
IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germ...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1257947/full |
| _version_ | 1827804854977822720 |
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| author | Haoyu Jiang Fei Hu Xiuqing Fu Cairong Chen Chen Wang Luxu Tian Yuran Shi |
| author_facet | Haoyu Jiang Fei Hu Xiuqing Fu Cairong Chen Chen Wang Luxu Tian Yuran Shi |
| author_sort | Haoyu Jiang |
| collection | DOAJ |
| description | IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea. |
| first_indexed | 2024-03-11T21:16:26Z |
| format | Article |
| id | doaj.art-e768f59c414d45718d1212f3e5a27658 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-11T21:16:26Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-e768f59c414d45718d1212f3e5a276582023-09-29T04:20:09ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-09-011410.3389/fpls.2023.12579471257947YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigorHaoyu Jiang0Fei Hu1Xiuqing Fu2Cairong Chen3Chen Wang4Luxu Tian5Yuran Shi6College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, ChinaCollege of Artificial Intelligence, Nanjing Agricultural University, Nanjing, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, ChinaCollege of Information Management, Nanjing Agricultural University, Nanjing, ChinaIntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.https://www.frontiersin.org/articles/10.3389/fpls.2023.1257947/fullpea seedseed vitalitydrought tolerancelightweightYOLOv8 |
| spellingShingle | Haoyu Jiang Fei Hu Xiuqing Fu Cairong Chen Chen Wang Luxu Tian Yuran Shi YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor Frontiers in Plant Science pea seed seed vitality drought tolerance lightweight YOLOv8 |
| title | YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor |
| title_full | YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor |
| title_fullStr | YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor |
| title_full_unstemmed | YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor |
| title_short | YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor |
| title_sort | yolov8 peas a lightweight drought tolerance method for peas based on seed germination vigor |
| topic | pea seed seed vitality drought tolerance lightweight YOLOv8 |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1257947/full |
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