TomatoGuard-YOLO: a novel efficient tomato disease detection method

Abstract

Tomatoes are highly susceptible to numerous diseases that significantly reduce their yield and quality, posing critical challenges to global food security and sustainable agricultural practices. To address the shortcomings of existing detection methods in accuracy, computational efficiency, and scalability, this study propose TomatoGuard-YOLO, an advanced, lightweight, and highly efficient detection framework based on an improved YOLOv10 architecture. The framework introduces two key innovations: the Multi-Path Inverted Residual Unit (MPIRU), which enhances multi-scale feature extraction and fusion, and the Dynamic Focusing Attention Framework (DFAF), which adaptively focuses on disease-relevant regions, substantially improving detection robustness. Additionally, the incorporation of the Focal-EIoU loss function refines bounding box matching accuracy and mitigates class imbalance. Experimental evaluations on a dedicated tomato disease detection dataset demonstrate that TomatoGuard-YOLO achieves an outstanding mAP50 of 94.23%, an inference speed of 129.64 FPS, and an ultra-compact model size of just 2.65 MB. These results establish TomatoGuard-YOLO as a transformative solution for intelligent plant disease management systems, offering unprecedented advancements in detection accuracy, speed, and model efficiency.