YOLO-BSMamba: A YOLOv8s-Based Model for Tomato Leaf Disease Detection in Complex Backgrounds

The precise identification of diseases in tomato leaves is of great importance for precise target pesticide application in a complex background scenario. Existing models often have difficulty capturing long-range dependencies and fine-grained features in images, leading to poor recognition where there are complex backgrounds. To tackle this challenge, this study proposed using YOLO-BSMamba detection mode. We proposed that a Hybrid Convolutional Mamba module (HCMamba) is integrated within the neck network, with the aim of improving feature representation by leveraging the capture global contextual dependencies capabilities of the State Space Model (SSM) and discerning the localized spatial feature capabilities of convolution. Furthermore, we introduced the Similarity-Based Attention Mechanism into the C2f module to improve the model’s feature extraction capabilities by focusing on disease-indicative leaf areas and reducing background noise. The weighted bidirectional feature pyramid network (BiFPN) was utilized to replace the feature-fusion component of the network, thereby enhancing the model’s detection performance for lesions exhibiting heterogeneous symptomatic gradations and enabling the model to effectively integrate features from different scales. Research results showed that the YOLO-BSMamba achieved an F1 score, mAP@0.5, and mAP@0.5:0.95 of 81.9%, 86.7%, and 72.0%, respectively, which represents an improvement of 3.0%, 4.8%, and 4.3%, respectively, compared to YOLOv8s. Compared to other YOLO series models, it achieves the best mAP@0.5 and F1 score. This study provides a robust and reliable method for tomato leaf disease recognition, which is expected to improve target pesticide efficiency, and further enhance crop monitoring and management in precision agriculture.