Authors : Mohd Zaid; Mohd Suhail Khan; Dr. Velayudham Sathiyasuntharam

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/3865vf7b

Scribd : https://tinyurl.com/mrpxum28

DOI : https://doi.org/10.38124/ijisrt/25nov542

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Abstract : Crop diseases cause large yield losses worldwide and represent a serious threat to food security. Traditional detection methods rely on manual inspection, which is time-consuming and error-prone. The AI-driven Crop Disease Detection and Management System presented in this paper combines environmental data analytics utilizing Random Forest regression for disease risk predictions with Convolutional Neural Networks (CNNs) for image-based disease identification. A carefully selected portion of the PlantVillage dataset, with an emphasis on the crops maize, tomato, and potato, is used to train the model. The hybrid approach leverages temperature, humidity, and rainfall data to increase prediction reliability. When compared to traditional CNN-only methods, experimental evaluation shows an accuracy of 94.33% and enhanced early disease prediction skills. The system, which offers real-time disease monitoring, is implemented as a mobile application and web platform. detection, forecasting, and treatment suggestions. This hybrid approach promotes sustainable agriculture through proactive disease management and optimized resource use.

Keywords : Crop Disease Prediction, Convolutional Neural Network (CNN), Deep Learning, Plant Village Dataset, Disease Risk Assessment, Precision Agriculture, AI in Agriculture, Sustainable Farming, Real-Time Disease Detection.

References :

1. Mohanty, S. P., Hughes, D. P., & Salathé, M. 2016. Using deep learning for image-based plant-disease-detection. Frontiers-in-Plant-Science,7,1419. https://doi.org/10.3389/fpls.2016.01419
2. Ferentinos, K. P. 2018. Deep learning models for plant disease detection and diagnosis. Computers-and-Electronics-in-Agriculture,145,311–318. https://doi.org/10.1016/j.compag.2018.01.009
3. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. 2016. Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 1–11. https://doi.org/10.1155/2016/3289801
4. Brahimi, M., Boukhalfa, K., & Moussaoui, A. 2017. Deep learning for tomato diseases: Classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299–315. https://doi.org/10.1080/08839514.2017.1315516
5. Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272–279. https://doi.org/10.1016/j.compag.2018.03.032
6. Fuentes, A. F., Yoon, S., Kim, S. C., & Park, D. S. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022. https://doi.org/10.3390/s17092022
7. Picon, A., Seitz, M., Alvarez-Gila, A., Mohnke, P., Ortiz-Barredo, A., & Echazarra, J. 2019. Crop disease classification in the wild with deep learning: An in-field domain adaptation approach. Computers and Electronics in Agriculture, 162, 351–358. https://doi.org/10.1016/j.compag.2019.04.020
8. Selvaraj, M. G., Vergara, A., Ruiz, H., Safari, N., Elayabalan, S., Ocimati, W., & Blomme, G. 2020. AI-powered banana diseases and pest detection using mobile phone images. Plant Methods, 16(1), 92. https://doi.org/10.1186/s13007-020-00622-9
9. Lu, J., Hu, J., Zhao, G., Mei, F., & Zhang, C. 2021. An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 191, 106523. https://doi.org/10.1016/j.compag.2021.106523
10. Zhang, S., Zhang, X., & Wang, Q. 2022. Plant disease detection based on deep learning: A review. Plant Phenomics, 2022, 1–20. https://doi.org/10.34133/2022/8561541
11. Singh, U., Jain, V., & Arora, A. 2023. Transfer learning approaches for robust crop disease classification in real-world scenarios. IEEE Access, 11, 52641–52653. https://doi.org/10.1109/ACCESS.2023.3264852
12. Abbas, A., Jain, S., Gour, M., & Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics-in-Agriculture,187,106279. https://doi.org/10.1016/j.compag.2021.106279
13. Uddin Chowdhury, M. J., Islam Mou, Z., Afrin, R., & Kibria, S. (2025). Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh’s Perspective. arXiv preprint. https://arxiv.org/abs/2501.03305

14. Zhang, R., Wang, M., Liu, P., Zhu, T., Qu, X., Chen, X., et al. (2024). Enhancing plant disease detection through deep learning. Frontiers in Plant Science, 2024. https://doi.org/10.3389/fpls.2024.1505857