LeafScan AI: Dual Self-Attention Residual Network for Plant Disease Detection

@article{1717330,
      author = {Harshitha S, Ishwarya S, Janani I},
      title = {LeafScan AI: Dual Self-Attention Residual Network for Plant Disease Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {1032-1036},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717330.pdf},
      abstract = {Agriculture plays a vital role in global food security, yet plant diseases continue to cause significant losses in crop yield and quality. This paper presents LeafScan AI, an intelligent plant disease detection system based on a Dual Self-Attention Residual Network (DSRN). The proposed model integrates deep residual learning with spatial and channel attention mechanisms to enhance feature extraction from plant leaf images. Experimental results demonstrate 93.40% accuracy, 91.20% sensitivity, 94.10% specificity, and 92.85% AUC, outperforming existing CNN-based baselines.},
      keywords = {Plant Disease Detection, Deep Learning, Residual Network, Self-Attention, Dual Attention Mechanism, Leaf Image Classification, PlantVillage Dataset},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717330}
  }