Dynamic Monitoring and Counting for Lotus Flowers and Seedpods with UAV Based on Improved YOLOv7-tiny

The numbers of lotus flowers and seedpods are the important agronomic traits for lotus breeding and field management. However, the traditional measurement method is manual, subjective, inefficient and labor-intensive. Therefore, this study proposed a novel approach for dynamic monitoring and counting lotus flowers and seedpods with UAV based on improved YOLOv7-tiny. To improve YOLOv7-tiny, the fusion mechanism of SPD layer and convolutions was studied to construct novel SPD-Conv blocks which outperformed the traditional SPD-Conv. Besides, a small target detection layer was adopted to enhance the performance for the lotus flower and seedpod detection. Subsequently, a group of Convolutional Block Attention Modules were embedded in the neck of YOLOv7-tiny to optimize the utilization of channel-spatial information. The P, R, mAP@.5 and mAP@.5:.95 of the improved YOLOv7-tiny model achieved an increase of 0.6%, 4.0%, 3.5% and 3.3% comparing with YOLOv7-tiny. In addition, the MAE, RMSE, and R2 for flowers measurement were 1.90, 2.72, and 0.96 respectively, while 1.96, 2.78, and 0.97 for seedpods respectively. The results demonstrated that the improved YOLOv7-tiny model had satisfactory performance for lotus flowers and seedpods detection and counting. In addition, the specialized software base on the improved YOLOv7-tiny was developed, and a sliding window method was proposed to monitor the large-scale lotus field. This study provides an efficient and convenient measurement method for flowers and seedpods in lotus breeding and field management.