ArticleFrontiers in plant science2021
Multiscale Parallel Algorithm for Early Detection of Tomato Gray Mold in a Complex Natural Environment.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images.Plants (Basel, Switzerland) · 2026Article
- A Multi-Modal Open Object Detection Model for Tomato Leaf Diseases with Strong Generalization Performance Using PDC-VLD.Plant phenomics (Washington, D.C.) · 2024Article
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- A Precise Image-Based Tomato Leaf Disease Detection Approach Using PLPNet.Plant phenomics (Washington, D.C.) · 2023Article
- A Review on Multiscale-Deep-Learning Applications.Sensors (Basel, Switzerland) · 2022Review
- Precision detection of crop diseases based on improved YOLOv5 model.Frontiers in plant science · 2022Article
- Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model.Frontiers in plant science · 2022Article
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2 authors.
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Abstract
Plant disease detection technology is an important part of the intelligent agricultural Internet of Things monitoring system. The real natural environment requires the plant disease detection system to have extremely high real time detection and accuracy. The lightweight network MobileNetv2-YOLOv3 model can meet the real-time detection, but the accuracy is not enough to meet the actual needs. This study proposed a multiscale parallel algorithm MP-YOLOv3 based on the MobileNetv2-YOLOv3 model. The proposed method put forward a multiscale feature fusion method, and an efficient channel attention mechanism was introduced into the detection layer of the network to achieve feature enhancement. The parallel detection algorithm was used to effectively improve the detection performance of multiscale tomato gray mold lesions while ensuring the real-time performance of the algorithm. The experimental results show that the proposed algorithm can accurately and real-time detect multiscale tomato gray mold lesions in a real natural environment. The F1 score and the average precision reached 95.6 and 93.4% on the self-built tomato gray mold detection dataset. The model size was only 16.9 MB, and the detection time of each image was 0.022 s.
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