arXiv Machine Learning July 9, 2026 By Raunak Kumar, Soumyashree Kar

Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops

Read the original on arXiv Machine Learning →

arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

computer-vision

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Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift

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fine-tuning benchmarks

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CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification

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safety

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An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification

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Hugging Face Trending Papers

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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-B3 and EfficientNetB7, DeepLabV3+, and SegFormer, all trained under a common pipeline with three input configurations (multispectral only, multispectral+NDVI, and multispectral+NDRE).

llms computer-vision efficiency

arXiv Machine Learning

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Improved Classification of Nitrogen Stress Severity in Plants Under Combined Stress Conditions Using Spatio-Temporal Deep Learning Framework

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By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Mohammad Shafiul Alam, Ahmed Al Wase, Md. Rabius Sani, Khan Md Hasib

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