ArticleFrontiers in plant science2024
Efficient and accurate identification of maize rust disease using deep learning model.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Accelerating iron biofortification in millets: progress, challenges, and future prospects.Planta · 2026Review
- Spectrally optimised YOLOv10s-SeqOpt framework for real-time UAV-based early detection of avocado foliar diseases in indian orchards.Scientific reports · 2026Article
- mIT-CMCA: a cross-modal category alignment framework for robust maize disease identification.Scientific reports · 2026Article
- Molecular networks and signaling pathways governing abiotic stress tolerance in cotton: advances and perspectives.Functional & integrative genomics · 2026Review
- Lead tolerance and siderophore production by native Pseudomonas spp. isolated from lead-contaminated environments in Colombia.Folia microbiologica · 2025Article
- Mobile based deep CNN model for maize leaf disease detection and classification.Plant methods · 2025Article
- Proteomic insights into fruit-pathogen interactions: managing biotic stress in fruit.Plant cell reports · 2025Review
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Authors and funding
9 authors.
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Abstract
Common corn rust and southern corn rust, two typical maize diseases during growth stages, require accurate differentiation to understand their occurrence patterns and pathogenic risks. To address this, a specialized Maize-Rust model integrating a SimAM module in the YOLOv8s backbone and a BiFPN for scale fusion, along with a DWConv for streamlined detection, was developed. The model achieved an accuracy of 94.6%, average accuracy of 91.6%, recall rate of 85.4%, and F1 value of 0.823, outperforming Faster-RCNN and SSD models by 16.35% and 12.49% in classification accuracy, respectively, and detecting a single rust image at 16.18 frames per second. Deployed on mobile phones, the model enables real-time data collection and analysis, supporting effective detection and management of large-scale outbreaks of rust in the field.
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Registered trials
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