Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images

Abstract

Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We propose a YOLOv4-based approach for the counting and location marking of rice seedlings from unmanned aerial vehicle (UAV) images. The detection of tiny objects is a crucial and challenging task in agricultural imagery. Therefore, we make modifications to the data augmentation and activation functions in the neural elements of the deep learning model to meet the requirements of rice seedling detection and counting. In the preprocessing stage, we segment the UAV images into different sizes for training. Mish activation is employed to enhance the accuracy of the YOLO one-stage detector. We utilize the dataset provided in the AIdea 2021 competition to evaluate the system, achieving an F1-score of 0.91. These results indicate the superiority of the proposed method over the baseline system. Furthermore, the outcomes affirm the potential for precise detection of rice seedlings in precision agriculture.