Enhanced YOLO Architecture with Attention Mechanism for Accurate Tobacco Plant Counting from UAV Images

(1) Background: This study investigates the construction and optimization of the You Only Look Once (YOLO) deep learning model for high-precision identification of suitable tobacco leaves. (2) Methods: Using tobacco fields in Xiaoxin Street, Niulanjiang Town, Songming County, Kunming as the study area, a total of 1200 UAV images collected during the planting, growth, and harvesting stages were employed as the training dataset to train object detection models such as YOLO v3. After 200 training iterations, the recognition performance of each model was compared and analyzed. (3) Results: YOLO v5 and YOLO v7 were selected as baseline models, and a channel attention mechanism was integrated to develop the improved YOLO v5-EN model. Ablation experiments were conducted by incorporating the attention module, dynamic rectified linear unit (DReLu) activation function, and a feature refinement module. YOLO v7 en was designed as a backbone network, and metrics such as precision, recall, and accuracy were comprehensively evaluated to assess the performance of both the baseline and improved models in identifying the number of tobacco plants. Compared to the baseline, the improved YOLO v5 model demonstrated a 0.36% increase in precision and a 1.55% increase in recall, achieving an overall recognition accuracy of 91.41%. The improved YOLO v7 model achieved a precision of 99.16% and a mean average precision (map) of 95.86%. These results indicate that the enhanced YOLO v5 model with channel attention effectively addresses the issues of missed and false detections in tobacco plant recognition. Furthermore, the improved YOLO v7 model, integrated with collaborative optimization strategies and an enhanced backbone, significantly improves the performance and efficiency of the detection model, particularly in terms of accuracy and processing speed for complex visual tasks. (4) Conclusions: The improved YOLO models significantly enhance the accuracy of tobacco plant count recognition and offer a practical solution for efficient tobacco plant statistics, serving as a reference for intelligent agriculture.