Detection of tobacco plant numbers in large fields based on improved YOLOv8 and UAV remote sensing imagery
Accurate plant counting is crucial in precision agriculture,forming a critical foundation for monitoring crop growth and predicting yield. To address challenges such as densely packed,overlapping,and aerial small targets of tobacco plants during the maturity stage,a lightweight GEW-YOLOv8 tobacco plant counting algorithm was proposed. The algorithm utilizes the GhostC2f module to reduce the parameters and computational workload of the model and employs an efficient multi-scale attention mechanism to discern occluded tobacco plants. Additionally,the WIoU loss function is introduced to accelerate model convergence and improve accuracy. Experimental results show a significant improvement in efficiency and accuracy compared to the original model,with a 24.7％ reduction in FLOPs and a 26.7％ decrease in model size. The improved model tobacco plant detection accuracy AP0.5 and AP0.5～0.95 reached 99.1％ and 86.2％ respectively,which were increased by 0.8％ and 3.6％ respectively compared with the original YOLOv8n model. The improved model can more swiftly and accurately identify field tobacco plants,providing technical support for intelligent tobacco agriculture.
万方数据
维普
