KrishiMitra – Development of a Hybrid Deep-Learning Algorithm for Detection of Diseases in Tree Leaves
Keywords:
Attention mechanism, CNN–transformer Architecture, ESP32-CAM edge computing, Explainable Artificial Intelligence, Hybrid deep learning, Leaf disease detection, Smart AgricultureAbstract
Plant diseases pose a major challenge to agricultural productivity in developing economies, where smallholder farmers often lack access to timely diagnostic support and expert guidance. Early disease identification through leaf image analysis offers an effective pathway for enabling timely intervention and reducing crop losses. This paper presents KrishiMitra, an Internet of Things (IoT)- enabled smart agriculture framework designed for practical, cost-effective plant leaf disease detection. The proposed system explores a hybrid deep learning architecture that integrates Convolutional Neural Networks for local feature extraction with attention-based mechanisms for enhanced feature representation, while transformer-based components are incorporated at the architectural level to support future contextual learning enhancements. The study focuses on system design, model development, and preprocessing strategies, supported by experiments conducted using the Plant Village dataset augmented to reflect real-world agricultural image variations. KrishiMitra is architected with edge deployment as a primary design objective, targeting low-cost devices such as the ESP32-CAM to enable decentralized, connectivity-independent operation. A web-based platform is developed to support image acquisition, disease prediction, and result visualization for farmer accessibility. While full-scale hardware optimization and extended field validation are ongoing, the presented work demonstrates the feasibility of deploying hybrid deep learning techniques within resource-constrained agricultural environments. This research establishes a foundation for scalable, farmer-centric disease detection systems and outlines clear directions for future enhancements, including edge-device optimization, planned explainable AI integration, and full-scale system deployment.
References
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, Sep. 2016.
A. Khattak, M. Asghar, U. Batool, “Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model,” IEEE Access, vol. 9, pp. 112942–112954, 2021.
Q. Zeng, X. Ma, B. Cheng, E. Zhou, and W. Pang, “GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning,” IEEE Access, vol. 8, pp. 172882–172891, 2020.
M. G. Lanjewar and J. S. Parab, “CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile,” Multimedia tools and Applications, vol. 83, no 11, pp. 31733–31758, Sep. 2023.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” openreview.net, Mar. 23, 2022.
Vaswani, N. Shazeer, “Attention Is All You Need,” Cornell University, Jun. 12, 2017. https://arxiv.org/abs/1706.03762
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Jun. 2016.
S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “CBAM: Convolutional Block Attention Module,” Computer Vision – ECCV 2018, pp. 3–19, 2018.
M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” arXiv.org, May 28, 2019.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer Normalization,” arXiv:1607.06450 [cs, stat], Jul. 2016.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2012.
N. Srivastava, G. Hinton, A. Krizhevsky, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014, Accessed: Jan. 23, 2026.
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, vol. 161, Mar. 2018.
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, vol. 2016, pp. 1–11, 2016.
Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Oct. 2021.
A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv.org, Apr. 17, 2017.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018.
Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, 2015, doi: https://doi.org/10.1109/cvpr.2015.7298594.
A. Krizhevsky, “One weird trick for parallelizing convolutional neural networks,” arXiv:1404.5997 [cs], Apr. 2014.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618–626, Oct. 2017.
M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, pp. 1135–1144, Aug. 2016.
S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” arXiv:1705.07874 [cs, stat], Nov. 2017.
N. K. Nawandar and V. R. Satpute, “IoT based low cost and intelligent module for smart irrigation system,” Computers and Electronics in Agriculture, vol. 162, pp. 979–990, Jul. 2019.
A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, Apr. 2018.
S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding,” arXiv:1510.00149, Feb. 2016.
L. Ruiz-Garcia, L. Lunadei, P. Barreiro, and I. Robla, “A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends,” Sensors, vol. 9, no. 6, pp. 4728–4750, Jun. 2009.
