mlsanityData to 14 Oct 2026

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

arXiv 2607.06585 ↗First posted 5 Jul 2026cs.CV cs.LGcited by 1

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Abstract (arXiv)

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. Accurate and scalable stress quantification is essential for precision agriculture, yet traditional manual assessments are labour-intensive and subjective. This paper proposes a unified deep learning pipeline integrating semantic segmentation, regression-based severity estimation, and disease classification. Stress severity is categorised into four levels (Low to Very High) based on the proportion of infected leaf area. Experiments on the Apple Tree Leaf Disease Segmentation dataset (1,641 samples, six classes) evaluate four models: U-Net (MobileNetV2), SegFormer, FCN, and PSPNet. U-Net with MobileNetV2 achieves the best performance with 98.20% pixel accuracy, 0.70 mIoU, and 99.41% detection accuracy at 14.7 ms per image, making it suitable for real-time use. SegFormer performs competitively (mIoU 0.66), while FCN and PSPNet show lower spatial accuracy (approximately 0.49 mIoU). The computed severity index strongly correlates with expert annotations (r = 0.968, R^2 = 0.937), demonstrating the system's reliability for automated crop monitoring and decision support.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 57 references that are in the map. First posted on arXiv 5 Jul 2026.

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19 of 57 references.

References · 19

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  7. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  8. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  9. 2016Pyramid Scene Parsing Networkcited by 2,479
  10. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  11. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  12. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
7 more
  1. 2017Tversky loss function for image segmentation using 3D fully convolutional deep networkscited by 228
  2. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 219
  3. 2018A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentationcited by 172
  4. 2015An open access repository of images on plant health to enable the development of mobile disease diagnosticscited by 135
  5. 2019CARAFE: Content-Aware ReAssembly of FEaturescited by 119
  6. 2019Crop Yield Prediction Using Deep Neural Networkscited by 29
  7. 2025Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithmscited by 2

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  1. 2026Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotypingcited by 0