Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory
Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/3336 |
| _version_ | 1827747610547453952 |
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| author | Xinfa Wang Zhenwei Wu Meng Jia Tao Xu Canlin Pan Xuebin Qi Mingfu Zhao |
| author_facet | Xinfa Wang Zhenwei Wu Meng Jia Tao Xu Canlin Pan Xuebin Qi Mingfu Zhao |
| author_sort | Xinfa Wang |
| collection | DOAJ |
| description | Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories. |
| first_indexed | 2024-03-11T05:55:11Z |
| format | Article |
| id | doaj.art-434e502efab24626906354dc6c58f473 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:55:11Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-434e502efab24626906354dc6c58f4732023-11-17T13:49:17ZengMDPI AGSensors1424-82202023-03-01236333610.3390/s23063336Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant FactoryXinfa Wang0Zhenwei Wu1Meng Jia2Tao Xu3Canlin Pan4Xuebin Qi5Mingfu Zhao6School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaCollege of Mechanical and Electrical Engineering, Xinxiang University, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaInstitute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, ChinaDue to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories.https://www.mdpi.com/1424-8220/23/6/3336tomato detectionYOLOv5small-target detectionlightweight |
| spellingShingle | Xinfa Wang Zhenwei Wu Meng Jia Tao Xu Canlin Pan Xuebin Qi Mingfu Zhao Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory Sensors tomato detection YOLOv5 small-target detection lightweight |
| title | Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory |
| title_full | Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory |
| title_fullStr | Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory |
| title_full_unstemmed | Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory |
| title_short | Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory |
| title_sort | lightweight sm yolov5 tomato fruit detection algorithm for plant factory |
| topic | tomato detection YOLOv5 small-target detection lightweight |
| url | https://www.mdpi.com/1424-8220/23/6/3336 |
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