TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection.

Abstract

Introduction: Tomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. Effective detection of these diseases is essential for enhancing agricultural productivity and mitigating economic losses. Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference.

Methods: The multi-scale tomato leaf disease detection model Tomato Focus-Diffusion Network (TomaFDNet) was proposed to solve the above problems. The model utilizes a multi-scale focus-diffusion network (MSFDNet) alongside an efficient parallel multi-scale convolutional module (EPMSC) to significantly enhance the extraction of multi-scale features. This combination particularly strengthens the model's capability to detect small targets amidst complex backgrounds.

Results and discussion: Experimental results show that TomaFDNet reaches a mean average precision (mAP) of 83.1% in detecting Early_blight, Late_blight, and Leaf_Mold on tomato leaves, outperforming classical object detection algorithms, including Faster R-CNN (mAP = 68.2%) and You Only Look Once (YOLO) series (v5: mAP = 75.5%, v7: mAP = 78.3%, v8: mAP = 78.9%, v9: mAP = 79%, v10: mAP = 77.5%, v11: mAP = 79.2%). Compared to the baseline YOLOv8 model, TomaFDNet achieves a 4.2% improvement in mAP, which is statistically significant (P < 0.01). These findings indicate that TomaFDNet offers a valid solution to the precise detection of tomato leaf diseases.