ArticleFrontiers in plant science2022
Identification of plant leaf diseases by deep learning based on channel attention and channel pruning.
Article in Frontiers in plant science, 2022. 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, 60 citations in OpenAlex.
- Article
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- Multi-class classification of plant leaf diseases using a hybrid deep neural transformer system and explainable AI techniques.Scientific reports · 2026Article
- A unified EfficientSwinB-based framework for medicinal plant recognition and safflower germplasm classification.Scientific reports · 2026Article
- Spectral image classification of asymptomatic peanut leaf diseases based on deep learning algorithms.Plant methods · 2025Article
- Robust CRW crops leaf disease detection and classification in agriculture using hybrid deep learning models.Plant methods · 2025Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity.Frontiers in plant science · 2024Article
- Compressing recognition network of cotton disease with spot-adaptive knowledge distillation.Frontiers in plant science · 2024Article
- Tea leaf disease and insect identification based on improved MobileNetV3.Frontiers in plant science · 2024Article
- A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity.Frontiers in plant science · 2024Article
- Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.PloS one · 2024Article
- Image-based crop disease detection with federated learning.Scientific reports · 2023Article
- MS-Net: a novel lightweight and precise model for plant disease identification.Frontiers in plant science · 2023Article
- MixSeg: a lightweight and accurate mix structure network for semantic segmentation of apple leaf disease in complex environments.Frontiers in plant science · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant diseases cause significant economic losses and food security in agriculture each year, with the critical path to reducing losses being accurate identification and timely diagnosis of plant diseases. Currently, deep neural networks have been extensively applied in plant disease identification, but such approaches still suffer from low identification accuracy and numerous parameters. Hence, this paper proposes a model combining channel attention and channel pruning called CACPNET, suitable for disease identification of common species. The channel attention mechanism adopts a local cross-channel strategy without dimensionality reduction, which is inserted into a ResNet-18-based model that combines global average pooling with global max pooling to effectively improve the features' extracting ability of plant leaf diseases. Based on the model's optimum feature extraction condition, unimportant channels are removed to reduce the model's parameters and complexity
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