ArticlePlant phenomics (Washington, D.C.)2023
Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Knowledge Distillation in Object Detection: A Survey from CNN to Transformer.Sensors (Basel, Switzerland) · 2026Review
- LMP-PM: a lightweight multi-path pruning method for plant leaf disease recognition.Frontiers in plant science · 2026Article
- A Detection Method of Pine Wilt Disease Based on Improved YOLOv11 With UAV Remote Sensing Images.Ecology and evolution · 2025Article
- An Improved YOLOv8-Based Method for Detecting Pests and Diseases on Cucumber Leaves in Natural Backgrounds.Sensors (Basel, Switzerland) · 2025Article
- PMJDM: a multi-task joint detection model for plant disease identification.Frontiers in plant science · 2025Article
- PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.Nucleic acids research · 2024Article
- CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.Plant phenomics (Washington, D.C.) · 2024Article
- A Multi-Modal Open Object Detection Model for Tomato Leaf Diseases with Strong Generalization Performance Using PDC-VLD.Plant phenomics (Washington, D.C.) · 2024Article
- AISOA-SSformer: An Effective Image Segmentation Method for Rice Leaf Disease Based on the Transformer Architecture.Plant phenomics (Washington, D.C.) · 2024Article
- PlanText: Gradually Masked Guidance to Align Image Phenotypes with Trait Descriptions for Plant Disease Texts.Plant phenomics (Washington, D.C.) · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition.Plant phenomics (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production, which benefits food production. Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases. However, existing methods are still limited to single crop disease diagnosis. More importantly, the existing model has a large number of parameters, which is not conducive to deploying it to agricultural mobile devices. Nonetheless, reducing the number of model parameters tends to cause a decrease in model accuracy. To solve these problems, we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops. In detail, we design 2 strategies to build 4 different lightweight models as student models: the YOLOR-Light-v1, YOLOR-Light-v2, Mobile-YOLOR-v1, and Mobile-YOLOR-v2 models, and adopt the YOLOR model as the teacher model. We develop a multistage knowledge distillation method to improve lightweight model performance, achieving 60.4%
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