Wavelet Aided Multi Task Transformer for Sugarcane Leaf Disease Prediction
Manjula V
Department of Computer Science and Engineering (AIML & IoT), Valluripalli Nageshwar Rao Vignana Jyothi Institute of Engineering and Technology, Bachupally, Telangana, India.
Accurate modeling of sugarcane leaf diseases pose several challenges, including the need for large and diverse datasets, difficulty in differentiating between visually similar disease symptoms, and the adverse effects of environmental variability on model accuracy. Additionally, real-time prediction remains computationally intensive and often lacks generalizability across different crop types and geographical regions. To address these limitations, this paper proposes a novel framework—Wavelet Prompt-Tuned Multi-Task Taxonomic Transformer with Hierarchical Auto-Associative Polynomial Network (WATT-Net)—applied to the Sugarcane Leaf Image Dataset for effective disease prediction. Image pre-processing is enhanced through Discrete Wavelet Transformation combined with Pre-Gaussian Filtering (DWT-PGF) to reduce noise and blur, thereby improving image clarity. Region deviation analysis is employed to localize disease-affected areas, followed by Prompt-Tuned Multi-Task Taxonomic Transformer (PTMT)-based segmentation, which ensures precise boundary delineation. The new architecture (proposed PTMT architecture) does not manually engineer prompts but instead, they are learnt using a data-driven approach during training. The learned prompts represent task specific contextual priors and are dynamically adjusted to perform multi-tasks and their segmentation and classification better via formulating attention mechanism of the transformer The Scale-Aware Hierarchical Auto-Associative Polynomial Network (SA-HAAPNet) further strengthens the framework by extracting discriminative features for accurate classification. Disease region identification is refined using the Walk-Spread Algorithm (WSA), which contributes to higher detection accuracy and reduced error rates. Experimental results using Python-based implementation demonstrate superior performance, achieving 99.9% accuracy and 99.8% sensitivity, significantly outperforming existing models. The proposed WATT-Net approach offers a robust and scalable solution for real-time sugarcane leaf disease detection, with strong potential for broader agricultural applications.
Keywords
Crop Leaf Disease, Discrete Wavelet Transformation, Prompt-Tuned Multi Task Taxonomic Transformer, Sugarcane Leaf Image Dataset, Walk Spread Algorithm.
Md. M. Islam et al., “DeepCrop: Deep learning-based crop disease prediction with web application,” Journal of Agriculture and Food Research,vol. 14, p. 100764, Dec. 2023, doi: 10.1016/j.jafr.2023.100764.
M. A. Patil and M. Manur, “Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features,” International Journal of System Assurance Engineering and Management, vol. 14, no. 6, pp. 2235–2266, Aug. 2023, doi: 10.1007/s13198-023-02066-0.
F. Arshad et al., “PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction,” Alexandria Engineering Journal, vol. 78, pp. 406–418, Sep. 2023, doi: 10.1016/j.aej.2023.07.076.
A. Pal and V. Kumar, “AgriDet: Plant Leaf Disease severity classification using agriculture detection framework,” Engineering Applications of Artificial Intelligence, vol. 119, p. 105754, Mar. 2023, doi: 10.1016/j.engappai.2022.105754.
V. Naralasetti and J. D. Bodapati, “Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning Approach,” Journal of The Institution of Engineers (India): Series B, vol. 105, no. 3, pp. 469–482, Feb. 2024, doi: 10.1007/s40031-023-00966-0.
S. Aggarwal et al., “Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business,” Scientific Programming, vol. 2022, pp. 1–13, Jun. 2022, doi: 10.1155/2022/1757888.
C. Ashwini and V. Sellam, “An optimal model for identification and classification of corn leaf disease using hybrid 3D-CNN and LSTM,” Biomedical Signal Processing and Control, vol. 92, p. 106089, Jun. 2024, doi: 10.1016/j.bspc.2024.106089.
V. Tanwar, S. Lamba, B. Sharma, and A. Sharma, “Red Rot Disease Prediction in Sugarcane Using the Deep Learning Approach,” 2023 2nd International Conference for Innovation in Technology (INOCON), Mar. 2023, doi: 10.1109/inocon57975.2023.10101147.
K. Thilagavathi, “Detection of Diseases in Sugarcane Using Image Processing Techniques,” Bioscience Biotechnology Research Communications, vol. 13, no. 11, pp. 109–115, Dec. 2020, doi: 10.21786/bbrc/13.11/24.
M. Mostafizur Rahman Komol, M. Sabid Hasan, and S. Ali, “Sugarcane Diseases Identification and Detection via Machine Learning,” Computer Vision and Machine Learning in Agriculture, Volume 3, pp. 37–51, 2023, doi: 10.1007/978-981-99-3754-7_3.
S. Thite, Y. Suryawanshi, K. Patil, and P. Chumchu, “Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications,” Data in Brief, vol. 53, p. 110268, Apr. 2024, doi: 10.1016/j.dib.2024.110268.
S. Thakur, D. Banerjee, and R. Sujitha, “Feature Extraction and Classification of Smut Disease in Sugarcane Leaves Using CNN and KNN,” 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 1967–1972, Feb. 2025, doi: 10.1109/idciot64235.2025.10915156.
P. Bhuyan, P. K. Singh, S. K. Das, and A. Kalla, “SE_SPnet: Rice leaf disease prediction using stacked parallel convolutional neural network with squeeze‐and‐excitation,” Expert Systems, vol. 40, no. 7, Apr. 2023, doi: 10.1111/exsy.13304.
M. Guijarro, I. Riomoros, G. Pajares, and P. Zitinski, “Discrete wavelets transform for improving greenness image segmentation in agricultural images,” Computers and Electronics in Agriculture, vol. 118, pp. 396–407, Oct. 2015, doi: 10.1016/j.compag.2015.09.011.
J. D. Pujari, Rajesh. Yakkundimath, and Abdulmunaf. Syedhusain. Byadgi, “Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops,” International Journal of Image, Graphics and Signal Processing, vol. 6, no. 1, pp. 24–31, Nov. 2013, doi: 10.5815/ijigsp.2014.01.04.
B. Mazumder, M. S. I. Khan, and K. M. Mohi Uddin, “Biorthogonal wavelet-based entropy feature extraction for identification of maize leaf diseases,” Journal of Agriculture and Food Research, vol. 14, p. 100756, Dec. 2023, doi: 10.1016/j.jafr.2023.100756.
Yanbo Huang, Yubin Lan, Steven J Thomson, Alex Fang, Wesley C Hoffmann, and Ron E Lacey, “Technical Development and Applications of Soft Computing in Agricultural and Biological Engineering,” 2009 Reno, Nevada, June 21 - June 24, 2009, 2009, doi: 10.13031/2013.27010.
S. Srinivasan, S. M. Prabin, S. K. Mathivanan, H. Rajadurai, S. Kulandaivelu, and M. A. Shah, “Sugarcane leaf disease classification using deep neural network approach,” BMC Plant Biology, vol. 25, no. 1, Mar. 2025, doi: 10.1186/s12870-025-06289-0.
P. Aakash Kumar, D. Nandhini, S. Amutha, and S. P. Syed Ibrahim, “Detection and identification of healthy and unhealthy sugarcane leaf using convolution neural network system,” Sādhanā, vol. 48, no. 4, Nov. 2023, doi: 10.1007/s12046-023-02309-7.
C. Sun, X. Zhou, M. Zhang, and A. Qin, “SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism,” Sensors, vol. 23, no. 20, p. 8529, Oct. 2023, doi: 10.3390/s23208529.
A. Kuppusamy, S. K. Sundaresan, and R. Cingaram, “Enhancing sugarcane leaf disease classification through a novel hybrid shifted-vision transformer approach: technical insights and methodological advancements,” Environmental Monitoring and Assessment, vol. 197, no. 1, Dec.2024, doi: 10.1007/s10661-024-13468-3.
S. B. Gupta, “A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering,” Indian Journal of Science and Technology, vol. 15, no. 43, pp. 2317–2324, Nov. 2022, doi: 10.17485/ijst/v15i43.1570.
R. Vasantha, N. Nguyen, and Y. Zhang, “Prompt-Tuned Muti-Task Taxonomic Transformer (PTMTTaxoFormer),” Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pp. 463–476, 2024, doi: 10.18653/v1/2024.emnlp-industry.35.
Chen, S., Ye, T., Liu, Y., & Chen, E. (2022). SnowFormer: Context interaction transformer with scale-awareness for single image desnowing. arXiv preprint arXiv:2208.09703.
P. Martell and V. Asari, “Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN),” Electronic Imaging, vol. 30, no. 10, pp. 338-1-338–6, Jan. 2018, doi: 10.2352/issn.2470-1173.2018.10.imawm-338.
P. D. Kusuma and A. L. Prasasti, “Walk-Spread Algorithm: A Fast and Superior Stochastic Optimization,” International Journal of Intelligent Engineering&Systems, vol.16, no.5,2.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Manjula V, Bhawna Sinha, Anupama Sharma, Sheela S, Kiranmai A V and Vetrithangam D;
Methodology: Manjula V, Bhawna Sinha and Anupama Sharma;
Writing- Original Draft Preparation: Manjula V, Bhawna Sinha, Anupama Sharma, Sheela S, Kiranmai A V and Vetrithangam D;
Visualization: Manjula V, Bhawna Sinha and Anupama Sharma;
Investigation: Sheela S, Kiranmai A V and Vetrithangam D;
Supervision: Manjula V, Bhawna Sinha and Anupama Sharma;
Validation: Sheela S, Kiranmai A V and Vetrithangam D;
Writing- Reviewing and Editing: Manjula V, Bhawna Sinha, Anupama Sharma, Sheela S, Kiranmai A V and Vetrithangam D; All authors reviewed the results and approved the final version of the manuscript.
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Vetrithangam D
Department of Computer Science & Engineering, Chandigarh University, Punjab, India.
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Cite this article
Manjula V, Bhawna Sinha, Anupama Sharma, Sheela S, Kiranmai A V and Vetrithangam D, “Wavelet Aided Multi Task Transformer for Sugarcane Leaf Disease Prediction”, Journal of Machine and Computing, vol.5, no.3, pp. 1725-1735, July 2025, doi: 10.53759/7669/jmc202505136.
