Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 6; p. 3336
• Main Authors: Wang, Xinfa, Wu, Zhenwei, Jia, Meng, Xu, Tao, Pan, Canlin, Qi, Xuebin, Zhao, Mingfu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.03.2023; MDPI
• Subjects: Accuracy; Agriculture; Algorithms; Analysis; Apples; Cell Movement; Factories; Fruit; Fruits; Harvest; lightweight; Methods; Neural networks; Robots; small-target detection; Solanum lycopersicum; tomato detection; Tomatoes; Vegetable industry; YOLOv5; tomato detection; YOLOv5; small-target detection; lightweight
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23063336

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.