RMP-UNet: An Efficient and Lightweight Model for Apple Leaf Disease Segmentation - Wenbo Zhao; Lijun Hu; Qi Wang; Hongxin Wu; Jiangbo Wang; Xu Li; Cuiyun Wu (Agronomy)
As an important and nutrient-rich economic crop, apple is significantly threatened by leaf diseases, which severely impact yield, making the timely and accurate diagnosis and segmentation of these diseases crucial. Traditional segmentation models face challenges such as low segmentation accuracy and excessive model size, limiting their applicability on resource-constrained devices. To address these issues, this study proposes RMP-UNet, an efficient and lightweight model for apple leaf disease segmentation. Based on the traditional UNet architecture, RMP-UNet incorporates an efficient multi-scale attention mechanism (EMA) along with innovative lightweight reparameterization modules (RepECA) and multi-scale feature fusion dynamic upsampling modules (PagDy), optimizing feature extraction and fusion processes to improve segmentation accuracy while reducing model complexity. The experimental results demonstrate that RMP-UNet achieves superior performance compared to mainstream models across multiple metrics, including a mean Intersection over Union (mIoU) of 83.27%, mean pixel accuracy of 89.84%, model size of 9.26 M, and computational complexity of 21.55 G FLOPs, making it suitable for deployment in resource-constrained environments and providing an efficient solution for real-time apple leaf disease diagnosis.