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

References

[1] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, p. 1419, 2016.

[2] K. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018.

[3] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE CVPR, Las Vegas, NV, USA, 2016, pp. 770–778.

[4] A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition,” Sensors, vol. 17, no. 9, p. 2022, 2017.

[5] X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local Means Networks,” in Proc. IEEE CVPR, 2018, pp. 7794–803.

[6] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “CBAM: Convolutional Block Attention Module,” in Proc. ECCV, 2018, pp. 3–19.

[7] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, vol. 115, pp. 211–252, 2015.

[8] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in Proc. IEEE CVPR, 2009, pp. 248–258.

[9] G. Huang, Z. Liu, L. Van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proc. IEEE CVPR, 2017, pp. 4700–4708.

[10] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Proc. ICLR, 2015.

[11] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proc. NeurIPS, 2012, pp. 1097–1105.

[12] C. Szegedy et al., “Going Deeper with Convolutions,” in Proc. IEEE CVPR, 2015, pp. 1–9.

[13] M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proc. ICML, 2019, pp. 6105–6114.

@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}
  }