ArticleFrontiers in plant science2023
Dual-branch collaborative learning network for crop disease identification.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- DFSNet: directional feature aggregation and shape-aware supervision for eggplant pest and disease detection.Frontiers in plant science · 2026Article
- Advancements in Agricultural Ground Robots for Specialty Crops: An Overview of Innovations, Challenges, and Prospects.Plants (Basel, Switzerland) · 2024Review
- A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds.Plants (Basel, Switzerland) · 2024Article
- Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: "Pivot-Shift Meter".Journal of personalized medicine · 2024Article
- Efficient residual network using hyperspectral images for corn variety identification.Frontiers in plant science · 2024Article
- SSATNet: Spectral-spatial attention transformer for hyperspectral corn image classification.Frontiers in plant science · 2024Article
- Parsing Netlists of Integrated Circuits from Images via Graph Attention Network.Sensors (Basel, Switzerland) · 2023Article
- SCGNet: efficient sparsely connected group convolution network for wheat grains classification.Frontiers in plant science · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture's efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.