TOMATO LEAF DISEASE DETECTION AND ADVISORY SYSTEM USING DEEP LEARNING AND LARGE LANGUAGE MODELS
DOI: 
SHARE THIS ARTICLE
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Agriculture is a cornerstone of food security and rural livelihood, yet crop diseases pose a persistent threat to yield and farmer income. This paper presents an intelligent system for tomato leaf disease detection and advisory generation using deep learning and Large Language Models (LLMs). A Convolutional Neural Network (CNN) model is trained on the PlantVillage dataset to classify ten tomato disease categories from leaf images. Upon disease identification, the system integrates Ollama, a locally hosted LLM, to generate comprehensive agricultural advisory outputs including disease causes, symptoms, preventive measures, and treatment recommendations. The backend is deployed using FastAPI, providing a responsive and scalable web-based interface. The proposed system bridges a critical gap in existing solutions by combining accurate computer vision-based classification with explainable, farmer-friendly advisory generation. Experimental results demonstrate strong classification accuracy across all ten classes, and the integrated advisory module delivers contextually relevant, actionable guidance supporting early-stage disease detection and informed decision-making in rural farming communities.
Keywords
Article Information
Article Metrics
HOW TO CITE
Copy
A, M. B. S. & P, V. (2026). Tomato Leaf Disease Detection and Advisory System using Deep Learning and Large Language Models. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.242
A, Muthu, and Vetrivel P. "Tomato Leaf Disease Detection and Advisory System using Deep Learning and Large Language Models." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.242.
A, Muthu, and Vetrivel P. "Tomato Leaf Disease Detection and Advisory System using Deep Learning and Large Language Models." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.242.
References
[2] M. Furqan, S. Rizvi, and P. Singh, "Plant Disease Diagnosis and Classification Using Deep Learning," International Journal of Pathology and Drugs, 2023, DOI: 10.54060/pd.2023.1.
[3] S. Kanakala and S. Ningappa, "Detection and Classification of Diseases in Multi-Crop Leaves using LSTM and CNN Models," arXiv preprint, 2021.
[4] P. D. Kumar, A. Suhasini, and D. Anand, "Crop Disease Detection Using 2D CNN Based Deep Learning Architecture," International Journal of Intelligent Systems and Applications in Engineering, 2020.
[5] N. Sulaiya and S. Banerjee, "Plant Leaf Disease Detection and Classification – A Review," International Journal of Advanced Research and Multidisciplinary Trends, 2021.
[6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems (NeurIPS), 2012.
[7] D. P. Hughes and M. Salathé, "An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics," arXiv preprint arXiv:1511.08060, 2015.
[8] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
[9] F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[10] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Ethics and Compliance
Indexed In
Similar Articles
Previous Article
