FSM-YOLO: Apple leaf disease detection network based on adaptive feature capture and spatial context awareness

Abstract

Apple leaf disease is a key factor affecting apple yield. Detecting apple leaf diseases in unstructured environments presents a significant challenge due to the diverse early forms and varying scales of the diseases, as well as the similarity between the diseased areas and the background. To address these challenges, this paper proposes an improved convolutional neural network FSM-YOLO with adaptive feature capture and spatial context awareness. Firstly, to address the lack of feature extraction due to the complex texture structure of disease features, AFEM (Adaptive Feature Enhancement Module) with the ability of contextual information fusion and channel information modulation is proposed, which enhances the feature extraction capability for multiple disease types. Secondly, SCAA (Spatial Context-aware Attention) module with spatial relationship capture and adaptive receptive field adjustment was designed to enhance the network's ability to spatial relationship modeling and its ability to focus on disease characteristics to distinguish between disease targets and background information. Finally, MKMC (Multi-kernel mixed Convolution) is proposed to enhance multi-scale feature extraction capability by efficiently capturing and integrating information at multiple spatial resolutions to cope with different scales and shape variations of early leaf disease types. Experiments were conducted on an apple leaf disease dataset covering eight different disease types with 15,159 disease instances, and the experimental results show that compared with the baseline model YOLOv8s, FSM-YOLO improves mAP@0.5 by 2.7%, precision by 2.0%, and recall by 4.0%. Meanwhile, experimental results on the open-source apple leaf disease dataset ALDOD and plant leaf disease dataset PlantDoc show that FSM-YOLO outperforms the state-of-the-art algorithms, which validates the versatility of FSM-YOLO and confirms its excellent detection performance in various plant disease scenarios.