Study on Real-Time Detection of Lightweight Tomato Plant Height Under Improved YOLOv5 and Visual Features

Tomato cultivation is relatively dense, and the main stem is easily submerged in a background environment with small color difference. The semi-enclosed planting space and fast growth cycle are both limitations that cannot be ignored in detection technology. The accuracy and real-time performance of plant height detection are of great practical significance. To this end, we are committed to improving YOLOv5 and proposing a lightweight real-time detection method for plant height by combining visual features of tomato main stems. Here, we improved the backbone, neck, head, and activation functions of YOLOv5, using CSP dark net53-s as the backbone structure and introducing a focus structure to reduce the number of GE modules. We replaced all CSP2_X structures in neck and head with GE modules, embedded interactive multi-head attention, and replaced YOLOv5’s framework function and attention activation function. We defined visual features such as the color of the main stem of tomato plants in the preprocessed image; input improved YOLOv5; and completed plant height detection through effective feature map fusion, main stem framing, and scale conversion. The experimental results show that the linear deviation between the plant height detection value and the actual value of the proposed method is always less than 3 cm, and the detection FPS can reach up to 67 frames per second, with superior timeliness, which can effectively achieve lightweight real-time detection.