ArticleFrontiers in plant science2024
TomatoGuard-YOLO: a novel efficient tomato disease detection method.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
- YOLOv8n-DSLW: A Deployment-Oriented AI-Enabled Vision-Sensing Model for Tiny Strawberry Disease and Pest Detection in Greenhouse Images.Sensors (Basel, Switzerland) · 2026Article
- Research on Lightweight Rose Disease Detection Based on Transferable Feature Representation.Plants (Basel, Switzerland) · 2026Article
- YOLO-FLBM: a lightweight and high-performance model for tomato ripeness detection in complex greenhouse environments.Frontiers in plant science · 2026Article
- Article
- DGS-Yolov7-Tiny: a lightweight pest and disease target detection model suitable for edge computing environments.Scientific reports · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- SSD-YOLO: a lightweight network for rice leaf disease detection.Frontiers in plant science · 2025Article
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2 authors.
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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.
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