A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions
Accurate detection of tea shoots in field conditions is a challenging task for production management and harvesting in tea plantations. Deep learning is well-suited for performing complex tasks due to its robust feature extraction capabilities. However, low-complexity models often suffer from poor d...
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MDPI AG
2025-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1122 |
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| author | Zhi Zhang Yongzong Lu Yun Peng Mengying Yang Yongguang Hu |
| author_facet | Zhi Zhang Yongzong Lu Yun Peng Mengying Yang Yongguang Hu |
| author_sort | Zhi Zhang |
| collection | DOAJ |
| description | Accurate detection of tea shoots in field conditions is a challenging task for production management and harvesting in tea plantations. Deep learning is well-suited for performing complex tasks due to its robust feature extraction capabilities. However, low-complexity models often suffer from poor detection performance, while high-complexity models are hindered by large size and high computational cost, making them unsuitable for deployment on resource-limited mobile devices. To address this issue, a lightweight and high-performance model was developed based on YOLOv5 for detecting tea shoots in field conditions. Initially, a dataset was constructed based on 1862 images of the tea canopy shoots acquired in field conditions, and the “one bud and one leaf” region in the images was labeled. Then, YOLOv5 was modified with a parallel-branch fusion downsampling block and a lightweight feature extraction block. The modified model was then further compressed using model pruning and knowledge distillation, which led to additional improvements in detection performance. Ultimately, the proposed lightweight and high-performance model for tea shoot detection achieved precision, recall, and average precision of 81.5%, 81.3%, and 87.8%, respectively, which were 0.4%, 0.6%, and 2.0% higher than the original YOLOv5. Additionally, the model size, number of parameters, and FLOPs were reduced to 8.9 MB, 4.2 M, and 15.8 G, representing decreases of 90.6%, 90.9%, and 85.3% compared to YOLOv5. Compared to other state-of-the-art detection models, the proposed model outperforms YOLOv3-SPP, YOLOv7, YOLOv8-X, and YOLOv9-E in detection performance while maintaining minimal dependency on computational and storage resources. The proposed model demonstrates the best performance in detecting tea shoots under field conditions, offering a key technology for intelligent tea production management. |
| format | Article |
| id | doaj-art-bd949d7b4443475c85e28c8f31ca671d |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-bd949d7b4443475c85e28c8f31ca671d2025-08-20T03:14:29ZengMDPI AGAgronomy2073-43952025-04-01155112210.3390/agronomy15051122A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field ConditionsZhi Zhang0Yongzong Lu1Yun Peng2Mengying Yang3Yongguang Hu4School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electronic Engineering, Changzhou College of Information Technology, Changzhou 213164, ChinaSchool of Material Science and Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaAccurate detection of tea shoots in field conditions is a challenging task for production management and harvesting in tea plantations. Deep learning is well-suited for performing complex tasks due to its robust feature extraction capabilities. However, low-complexity models often suffer from poor detection performance, while high-complexity models are hindered by large size and high computational cost, making them unsuitable for deployment on resource-limited mobile devices. To address this issue, a lightweight and high-performance model was developed based on YOLOv5 for detecting tea shoots in field conditions. Initially, a dataset was constructed based on 1862 images of the tea canopy shoots acquired in field conditions, and the “one bud and one leaf” region in the images was labeled. Then, YOLOv5 was modified with a parallel-branch fusion downsampling block and a lightweight feature extraction block. The modified model was then further compressed using model pruning and knowledge distillation, which led to additional improvements in detection performance. Ultimately, the proposed lightweight and high-performance model for tea shoot detection achieved precision, recall, and average precision of 81.5%, 81.3%, and 87.8%, respectively, which were 0.4%, 0.6%, and 2.0% higher than the original YOLOv5. Additionally, the model size, number of parameters, and FLOPs were reduced to 8.9 MB, 4.2 M, and 15.8 G, representing decreases of 90.6%, 90.9%, and 85.3% compared to YOLOv5. Compared to other state-of-the-art detection models, the proposed model outperforms YOLOv3-SPP, YOLOv7, YOLOv8-X, and YOLOv9-E in detection performance while maintaining minimal dependency on computational and storage resources. The proposed model demonstrates the best performance in detecting tea shoots under field conditions, offering a key technology for intelligent tea production management.https://www.mdpi.com/2073-4395/15/5/1122tea shoot detectiondeep learningdownsamplingmodel compression |
| spellingShingle | Zhi Zhang Yongzong Lu Yun Peng Mengying Yang Yongguang Hu A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions Agronomy tea shoot detection deep learning downsampling model compression |
| title | A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions |
| title_full | A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions |
| title_fullStr | A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions |
| title_full_unstemmed | A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions |
| title_short | A Lightweight and High-Performance YOLOv5-Based Model for Tea Shoot Detection in Field Conditions |
| title_sort | lightweight and high performance yolov5 based model for tea shoot detection in field conditions |
| topic | tea shoot detection deep learning downsampling model compression |
| url | https://www.mdpi.com/2073-4395/15/5/1122 |
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