Recognition of terminal buds of densely-planted Chinese fir seedlings using improved YOLOv5 by integrating attention mechanism - Ye, Zhangxi; Guo, Qian; Wei, Jiahao; Zhang, Jian; Zhang, Houxi; Bian, Liming; Guo, Shijie; Zheng, Xueyan; Cao, Shijiang (Frontiers in Plant Science, 2022)
Accurate and timely information on the number of densely-planted Chinese fir seedlings is essential for their scientific cultivation and intelligent management. However, in the later stage of cultivation, the overlapping of lateral branches among individuals is too severe to identify the entire individual in the drone image. At the same time, in the high-density planting nursery, the terminal bud of each seedling has a distinctive characteristic of growing upward, which can be used as an identification feature. Still, due to the small size and dense distribution of the terminal buds, the existing recognition algorithm will have a significant error. Therefore, in this study, we proposed a model based on the improved network structure of the latest YOLOv5 algorithm for identifying the terminal bud of Chinese fir seedlings. Firstly, the micro-scale prediction head was added to the original prediction head to enhance the model’s ability to perceive small-sized terminal buds. Secondly, a multi-attention mechanism module composed of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA) was integrated into the neck of the network to enhance further the model’s ability to focus on key target objects in complex backgrounds. Finally, the methods including data augmentation, Test Time Augmentation (TTA) and Weighted Boxes Fusion (WBF) were used to improve the robustness and generalization of the model for the identification of terminal buds in different growth states. The results showed that, compared with the standard version of YOLOv5, the recognition accuracy of the improved YOLOv5 was significantly increased, with a precision of 95.55%, a recall of 95.84%, an F1-Score of 96.54%, and an mAP of 94.63%. Under the same experimental conditions, compared with other current mainstream algorithms (YOLOv3, Faster R-CNN, and PP-YOLO), the average precision and F1-Score of the improved YOLOv5 also increased by 9.51-28.19 percentage points and 15.92-32.94 percentage points, respectively. Overall, The improved YOLOv5 algorithm integrated with the attention network can accurately identify the terminal buds of densely-planted Chinese fir seedlings in UAV images and provide technical support for large-scale and automated counting and precision cultivation of Chinese fir seedlings.