DFR-YOLOv12n: A Lightweight Detection Method for Tomato Leaf Diseases in Natural Environments via Detail-Preserving Downsampling, Feature Fusion Enhancement, and Regression Optimization

Tomato leaf disease detection in natural environments is challenged by subtle early-stage symptoms, complex backgrounds, leaf occlusion, and scale variation, which can lead to missed detections, false detections, and unstable leaf localization. Meanwhile, practical agricultural applications impose higher requirements on model lightweightness and edge-deployment capability. To address these issues, this study proposes DFR-YOLOv12n, a lightweight tomato leaf disease detection model based on YOLOv12n that integrates detail-preserving downsampling, feature enhancement, and regression optimization. First, a multi-source dataset collected in natural environments was constructed and curated, covering eight categories: bacterial spot, early blight, late blight, leaf mold, mosaic virus disease, septoria leaf spot, yellow leaf curl virus disease, and healthy leaves. Second, SPDConv was introduced into key downsampling layers to preserve fine-grained disease-related visual cues. The A2C2f_DEConv module was incorporated into the P3 feature fusion branch to enhance leaf texture and disease-related appearance features under complex backgrounds. In addition, MPDIoU was adopted to optimize bounding box regression and improve whole-leaf localization under occlusion and background interference. The optimal model configuration was determined through insertion-position, module comparison, and ablation experiments. Compared with the baseline model, DFR-YOLOv12n increased Precision, Recall, and mAP@0.5 from 86.8%, 76.9%, and 86.5% to 88.1%, 81.7%, and 88.6%, respectively. Meanwhile, FLOPs decreased from 5.83 G to 5.27 G, the parameter count decreased from 2.51 M to 2.25 M, and the model size decreased from 5.22 MB to 4.71 MB. Furthermore, the model was successfully deployed and validated on the Jetson Nano platform, demonstrating its potential for edge applications. The results indicate that DFR-YOLOv12n achieves a favorable balance among detection accuracy, model complexity, and deployment feasibility, providing a reference for intelligent tomato leaf disease detection in natural environments.