Tobacco Plant Counting Based on Improved YOLOv8 and UAV Remote Sensing Images

Abstract

The tobacco plant counting is an important aspect in tobacco production management, traditional manual methods are time-consuming, labor-intensive and inaccurate, failing to meet the efficiency demands of modern agriculture. To enhance the accuracy and efficiency of tobacco plant counting in the field environment, this study utilizes high-resolution remote sensing imagery collected by drones to construct a sample dataset and proposes an improved YOLOv8-based target detection model (YOLOv8-CSD). YOLOv8-CSD model, based on YOLOv8, incorporates a coordinate attention mechanism (CA) to improve the extraction ability of the model to tobacco plant features. It also optimizes the feature pyramid network (FPN) and adds a small target detection layer to enhance the detection ability for the small target tobacco plants. Additionally, the shape intersection over ratio (SIoU) loss function is used to accelerate model convergence, and the slice-assisted hyper inference (SAHI) strategy is introduced to improve the accuracy and inference efficiency of small target detection by slicing high-resolution images. The experimental results show that the YOLOv8-CSD model achieves a precision of 97.96%, a recall rate of 97.93%, and an average accurate mean (mAP0.5) of 99.32%, significantly outperforming the original YOLOv8 and other 5 commonly used target recognition models. In addition, the efficiency of YOLOv8-CSD model is only lower than YOlOv11, indicating good overall performance. The YOLOv8-CSD model has good adaptability and robustness in tobacco plant detection at different growth stages, with a low missed detection rate, and the YOLOv8-CSD model can effectively meet the requirements of tobacco plant counting in complex field scenarios.