StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training
Maize (<i>Zea mays</i> L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To ad...
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MDPI AG
2025-07-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/13/2070 |
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| author | Ziqi Yang Yiran Liao Ziao Chen Zhenzhen Lin Wenyuan Huang Yanxi Liu Yuling Liu Yamin Fan Jie Xu Lijia Xu Jiong Mu |
| author_facet | Ziqi Yang Yiran Liao Ziao Chen Zhenzhen Lin Wenyuan Huang Yanxi Liu Yuling Liu Yamin Fan Jie Xu Lijia Xu Jiong Mu |
| author_sort | Ziqi Yang |
| collection | DOAJ |
| description | Maize (<i>Zea mays</i> L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements. |
| format | Article |
| id | doaj-art-1c8fe63d8c2f48feb5cc68d921d53884 |
| institution | DOAJ |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-1c8fe63d8c2f48feb5cc68d921d538842025-08-20T03:17:52ZengMDPI AGPlants2223-77472025-07-011413207010.3390/plants14132070StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task TrainingZiqi Yang0Yiran Liao1Ziao Chen2Zhenzhen Lin3Wenyuan Huang4Yanxi Liu5Yuling Liu6Yamin Fan7Jie Xu8Lijia Xu9Jiong Mu10College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaMaize Research Institute, Sichuan Agricultural University, Chengdu 611130, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaMaize Research Institute, Sichuan Agricultural University, Chengdu 611130, ChinaMaize Research Institute, Sichuan Agricultural University, Chengdu 611130, ChinaMaize Research Institute, Sichuan Agricultural University, Chengdu 611130, ChinaMaize Research Institute, Sichuan Agricultural University, Chengdu 611130, ChinaCollege of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaMaize (<i>Zea mays</i> L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements.https://www.mdpi.com/2223-7747/14/13/2070maize stomaYOLOmulti-task trainingprecision agriculture |
| spellingShingle | Ziqi Yang Yiran Liao Ziao Chen Zhenzhen Lin Wenyuan Huang Yanxi Liu Yuling Liu Yamin Fan Jie Xu Lijia Xu Jiong Mu StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training Plants maize stoma YOLO multi-task training precision agriculture |
| title | StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training |
| title_full | StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training |
| title_fullStr | StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training |
| title_full_unstemmed | StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training |
| title_short | StomaYOLO: A Lightweight Maize Phenotypic Stomatal Cell Detector Based on Multi-Task Training |
| title_sort | stomayolo a lightweight maize phenotypic stomatal cell detector based on multi task training |
| topic | maize stoma YOLO multi-task training precision agriculture |
| url | https://www.mdpi.com/2223-7747/14/13/2070 |
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