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Deep learning frameworks compared for rice disease mapping

By PulseAugur Editorial · [3 sources] ·

Researchers compared various deep learning frameworks for mapping rice disease severity using UAV multispectral imagery. The study evaluated architectures like U-Net, U-Net++, DeepLabV3+, and SegFormer, testing them with different input configurations including vegetation indices. U-Net++ with EfficientNet-B3 demonstrated the highest performance with a 97.62% mIoU, suggesting that lightweight CNNs are more reliable for operational disease monitoring. AI

IMPACT Lightweight CNNs show promise for operational disease monitoring, potentially improving agricultural efficiency.

RANK_REASON The cluster contains a research paper detailing a comparison of deep learning models for a specific application.

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AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging

    In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet…

  2. arXiv cs.CV TIER_1 English(EN) · Yadav Raj Ghimire, Jagrati Talreja, Tewodros Syum Gebre, Timothy Agboada, Shikha V. Chandel, Leila Hashemi Beni ·

    Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging

    arXiv:2606.06359v1 Announce Type: new Abstract: In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with …

  3. arXiv cs.CV TIER_1 English(EN) · Leila Hashemi Beni ·

    Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging

    In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet…

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