ArticleFrontiers in plant science2023
A lightweight model for efficient identification of plant diseases and pests based on deep learning.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Transformer augmented hybrid deep learning for explainable multi class pest classification.Scientific reports · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- EfficientNet-CBAM-prototype: an attention-guided EfficientNet framework with dynamic prototype representation for tomato disease classification.Frontiers in plant science · 2026Article
- Article
- MobileNet-WDD: a lightweight deep learning image classification method for identifying defects in wheat grains.Frontiers in plant science · 2026Article
- AdaptPest-Net: A Task-Adaptive Network with Graph-Mamba Fusion for Multi-Scale Agricultural Pest Recognition.Entropy (Basel, Switzerland) · 2025Article
- EDDet: efficient deep-fusion and dynamic optimization for small target detection in eggplant diseases.BMC plant biology · 2025Article
- Design of a lightweight recognition network for adult locusts and grasshoppers based on deep learning.iScience · 2025Article
- Deep learning empowers genomic selection of pest-resistant grapevine.Horticulture research · 2025Article
- DWTFormer: a frequency-spatial features fusion model for tomato leaf disease identification.Plant methods · 2025Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors.Frontiers in oncology · 2024Article
- Deep learning and explainable AI for classification of potato leaf diseases.Frontiers in artificial intelligence · 2024Article
- Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.Frontiers in plant science · 2024Article
- A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.Frontiers in plant science · 2023Article
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Authors and funding
6 authors.
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Abstract
Plant diseases and pests have always been major contributors to losses that occur in agriculture. Currently, the use of deep learning-based convolutional neural network models allows for the accurate identification of different types of plant diseases and pests. To enable more efficient identification of plant diseases and pests, we design a novel network architecture called Dise-Efficient based on the EfficientNetV2 model. Our experiments demonstrate that training this model using a dynamic learning rate decay strategy can improve the accuracy of plant disease and pest identification. Furthermore, to improve the model's generalization ability, transfer learning is incorporated into the training process. Experimental results indicate that the Dise-Efficient model boasts a compact size of 13.3 MB. After being trained using the dynamic learning rate decay strategy, the model achieves an accuracy of 99.80% on the Plant Village plant disease and pest dataset. Moreover, through transfer learning on the IP102 dataset, which represents real-world environmental conditions, the Dise-Efficient model achieves a recognition accuracy of 64.40% for plant disease and pest identification. In light of these results, the proposed Dise-Efficient model holds great potential as a valuable reference for the deployment of automatic plant disease and pest identification applications on mobile and embedded devices in the future.
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