YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5
The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/7/1901 |
| _version_ | 1797590619237908480 |
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| author | Yaodi Li Jianxin Xue Mingyue Zhang Junyi Yin Yang Liu Xindan Qiao Decong Zheng Zezhen Li |
| author_facet | Yaodi Li Jianxin Xue Mingyue Zhang Junyi Yin Yang Liu Xindan Qiao Decong Zheng Zezhen Li |
| author_sort | Yaodi Li |
| collection | DOAJ |
| description | The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small automatic picking robot, etc. This research proposes a real-time multistage strawberry detection algorithm YOLOv5-ASFF based on improved YOLOv5. Through the introduction of the ASFF (adaptive spatial feature fusion) module into YOLOv5, the network can adaptively learn the fused spatial weights of strawberry feature maps at each scale as a way to fully obtain the image feature information of strawberries. To verify the superiority and availability of YOLOv5-ASFF, a strawberry dataset containing a variety of complex scenarios, including leaf shading, overlapping fruit, and dense fruit, was constructed in this experiment. The method achieved 91.86% and 88.03% for mAP and F1, respectively, and 98.77% for AP of mature-stage strawberries, showing strong robustness and generalization ability, better than SSD, YOLOv3, YOLOv4, and YOLOv5s. The YOLOv5-ASFF algorithm can overcome the influence of complex field environments and improve the detection of strawberries under dense distribution and shading conditions, and the method can provide technical support for monitoring yield estimation and harvest planning in intelligent strawberry field management. |
| first_indexed | 2024-03-11T01:24:02Z |
| format | Article |
| id | doaj.art-f17aa8875b9b44c989ea46402f6e3fcc |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T01:24:02Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-f17aa8875b9b44c989ea46402f6e3fcc2023-11-18T17:57:59ZengMDPI AGAgronomy2073-43952023-07-01137190110.3390/agronomy13071901YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5Yaodi Li0Jianxin Xue1Mingyue Zhang2Junyi Yin3Yang Liu4Xindan Qiao5Decong Zheng6Zezhen Li7College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaCollege of Food Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, ChinaThe smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small automatic picking robot, etc. This research proposes a real-time multistage strawberry detection algorithm YOLOv5-ASFF based on improved YOLOv5. Through the introduction of the ASFF (adaptive spatial feature fusion) module into YOLOv5, the network can adaptively learn the fused spatial weights of strawberry feature maps at each scale as a way to fully obtain the image feature information of strawberries. To verify the superiority and availability of YOLOv5-ASFF, a strawberry dataset containing a variety of complex scenarios, including leaf shading, overlapping fruit, and dense fruit, was constructed in this experiment. The method achieved 91.86% and 88.03% for mAP and F1, respectively, and 98.77% for AP of mature-stage strawberries, showing strong robustness and generalization ability, better than SSD, YOLOv3, YOLOv4, and YOLOv5s. The YOLOv5-ASFF algorithm can overcome the influence of complex field environments and improve the detection of strawberries under dense distribution and shading conditions, and the method can provide technical support for monitoring yield estimation and harvest planning in intelligent strawberry field management.https://www.mdpi.com/2073-4395/13/7/1901strawberryYOLOv5intelligent monitoringautomatic picking |
| spellingShingle | Yaodi Li Jianxin Xue Mingyue Zhang Junyi Yin Yang Liu Xindan Qiao Decong Zheng Zezhen Li YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5 Agronomy strawberry YOLOv5 intelligent monitoring automatic picking |
| title | YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5 |
| title_full | YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5 |
| title_fullStr | YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5 |
| title_full_unstemmed | YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5 |
| title_short | YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5 |
| title_sort | yolov5 asff a multistage strawberry detection algorithm based on improved yolov5 |
| topic | strawberry YOLOv5 intelligent monitoring automatic picking |
| url | https://www.mdpi.com/2073-4395/13/7/1901 |
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