ArticleHeliyon2024
Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Improved multiscale attention based deep learning approach for automated sugarcane leaf disease detection using BSRI data.Scientific reports · 2025Article
- Sugarcane leaf disease classification using deep neural network approach.BMC plant biology · 2025Article
- Enhancing leaf disease classification using GAT-GCN hybrid model.Frontiers in plant science · 2025Article
- Deep learning-based phenotyping of lettuce diseases using Efficient-FBM-FRMNet for precision agriculture.Frontiers in plant science · 2025Article
- Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration.Heliyon · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With an emphasis on classifying diseases of sugarcane leaves, this research suggests an attention-based multilevel deep learning architecture for reliably classifying plant diseases. The suggested architecture comprises spatial and channel attention for saliency detection and blends features from lower to higher levels. On a self-created database, the model outperformed cutting-edge models like VGG19, ResNet50, XceptionNet, and EfficientNet_B7 with an accuracy of 86.53%. The findings show how essential all-level characteristics are for categorizing images and how they can improve efficiency even with tiny databases. The suggested architecture has the potential to support the early detection and diagnosis of plant diseases, enabling fast crop damage mitigation. Additionally, the implementation of the proposed AMRCNN model in the Android phone-based application gives an opportunity for the widespread use of mobile phones in the classification of sugarcane diseases.
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Registered trials
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.