Intelligent Agriculture

Article

AI‑Powered Deep Learning Web Application for Automated Plant Disease Diagnosis with Rich Visual Analytics

Downloads

https://doi.org/10.54963/ia.v1i2.1665
Volume 1 Issue 2 (2025)
Copyright (c) 2025 Jaydish John kennedy, Gurusamy Chelladurai
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Intelligent Agriculture (IA) publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by Intelligent Agriculture (IA) agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source is properly cited, anyone may copy, redistribute, reuse and transform the content.

Authors

  • Jaydish John kennedy

    Department of Botany, St. Joseph’s College (Autonomous), Trichirappalli, Tamil Nadu 620002, India
  • Gurusamy Chelladurai

    Department of Botany, St. Joseph’s College (Autonomous), Trichirappalli, Tamil Nadu 620002, India

Received: 23 July 2025 | Revised: 12 September 2025 | Accepted: 18 September 2025 | Published Online: 2 October 2025

This work presents an AI system powered by artificial intelligence and based on deep learning for diagnosing and detecting plant diseases. Using a CNN that has been trained and optimized on the Plant Village dataset, major crops such as tomatoes, potatoes, and bell peppers can have their illnesses properly classified. The method provides comprehensive diagnostic data, including taxonomy, organisms responsible for the disease, nutritional deficit mimics, and external symptoms, in addition to illness class predictions. Innovatively, the system incorporates the Rich Python library, which enables a graphical, colour-coded command-line interface. Because of this, users can receive detailed, interactive feedback within the terminal itself. The programme was designed with easy use in mind and is intended for use by researchers, educators, and farmers in real-world agricultural settings. Facilitating the detection and understanding of plant health issues in real time aids in learning and practical decision-making. This study demonstrates how integrating AI with agricultural diagnostics can enhance interpretability, usefulness, and overall impact. Finally, it stresses how technology based on deep learning could revolutionize crop health monitoring and agricultural education.

Keywords:

Plant Disease Detection Deep Learning Image Classification Convolutional Neural Network (CNN)

References

  1. Liakos, K.G., Busato, P., Moshou, D., et al., 2018. Machine Learning in Agriculture: A Review. Sensors. 18(8), 2674. DOI: https://doi.org/10.3390/s18082674
  2. Foysal, M.A.H., Ahmed, F., Haque, M.Z., 2024. Multi-Class Plant Leaf Disease Detection: A CNN-Based Approach With Mobile App Integration. arXiv preprint. arXiv.2408.15289. DOI: https://doi.org/10.48550/arXiv.2408.15289
  3. Pacal, I., Kunduracioglu, I., Alma, M.H., et al., 2024. A Systematic Review of Deep Learning Techniques for Plant Diseases. Artificial Intelligence Review. 57, 304. DOI: https://doi.org/10.1007/s10462-024-10944-7
  4. Wang, S., Xu, D., Liang, H., et al., 2025. Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review. Remote Sensing. 17(4), 698. DOI: https://doi.org/10.3390/rs17040698
  5. Ahmed, T., Jannat, S., Islam, M.F., et al., 2025. Involution-Infused DenseNet With Two-Step Compression for Resource-Efficient Plant Disease Classification. arXiv preprint. arXiv.2506.00735. DOI: https://doi.org/10.48550/arXiv.2506.00735
  6. Too, E.C., Yujian, L., Njuki, S., et al., 2019. A Comparative Study of Fine-Tuning Deep Learning Models for Plant Disease Identification. Computers and Electronics in Agriculture. 161, 272–279. DOI: https://doi.org/10.1016/j.compag.2018.03.032
  7. Ayyappan, A.B., Gobinath, T., Kumar, M., et al., 2025. Rice Plant Disease Detection Using Convolutional Neural Networks. Discover Artificial Intelligence. 5, 50. DOI: https://doi.org/10.1007/s44163-025-00277-x
  8. Steinke, J., van Etten, J., Müller, A., et al., 2022. Tapping the Full Potential of the Digital Revolution for Agricultural Extension: An Emerging Innovation Agenda. International Journal of Agricultural Sustainability. 19(5–6), 549–565. DOI: https://doi.org/10.1080/14735903.2020.1738754
  9. Shoaib, M., Sadeghi-Niaraki, A., Ali, F., et al., 2025. Leveraging Deep Learning for Plant Disease and Pest Detection: A Comprehensive Review and Future Directions. Frontiers in Plant Science. 16, 1538163. DOI: https://doi.org/10.3389/fpls.2025.1538163
  10. Darshan, J.S., Krishna, K.V., Bhushan, K.S., et al., 2025. Plant Disease Detection Using Deep Learning. International Journal of Interpreting Enigma Engineers. 2(2), 24–29. DOI: https://doi.org/10.62674/ijiee.2025.v2i02.003
  11. Albahli, S., 2025. AgriFusionNet: A Lightweight Deep Learning Model for Multisource Plant Disease Diagnosis. Agriculture. 15(14), 1523. DOI: https://doi.org/10.3390/agriculture15141523
  12. Benfenati, A., Causin, P., Oberti, R., et al., 2021. Unsupervised Deep Learning Techniques for Powdery Mildew Recognition Based on Multispectral Imaging. arXiv preprint. arXiv.2112.11242. DOI: https://doi.org/10.48550/arXiv.2112.11242
  13. Sujatha, R., Krishnan, S., Chatterjee, J.M., et al., 2025. Advancing Plant Leaf Disease Detection Integrating Machine Learning and Deep Learning. Scientific Reports. 15, 11552. DOI: https://doi.org/10.1038/s41598-024-72197-2
  14. Khanna, A., Kaur, S., 2019. Evolution of Internet of Things (IoT) and Its Significant Impact in the Field of Precision Agriculture. Computers and Electronics in Agriculture. 157, 218–231. DOI: https://doi.org/10.1016/j.compag.2018.12.039
  15. Gohil, M.K., Bhattacharjee, A., Rana, R., et al., 2024. A Hybrid Technique for Plant Disease Identification and Localisation in Real-Time. arXiv preprint. arXiv.2412.19682. DOI: https://doi.org/10.48550/arXiv.2412.19682
  16. Sinamenye, J.H., Chatterjee, A., Shrestha, R., 2025. Potato Plant Disease Detection: Leveraging Hybrid Deep Learning Models. BMC Plant Biology. 25, 647. DOI: https://doi.org/10.1186/s12870-025-06679-4
  17. Tonmoy, M.R., Hossain, M.M., Dey, N., et al., 2025. MobilePlantViT: A Mobile-Friendly Hybrid ViT for Generalized Plant Disease Classification. arXiv preprint. arXiv.2503.16628. DOI: https://doi.org/10.48550/arXiv.2503.16628
  18. Liu, J., Wang, X., 2024. Multisource Information Fusion Method for Vegetable Disease Detection. BMC Plant Biology. 24, 738. DOI: https://doi.org/10.1186/s12870-024-05346-4
  19. Ashurov, A.Y., Al-Gaashani, M.S.A.M., Samee, N.A., et al., 2025. Enhancing Plant Disease Detection Through Deep Learning: A Depthwise CNN with Squeeze and Excitation Integration and Residual Skip Connections. Frontiers in Plant Science. 15, 1505857. DOI: https://doi.org/10.3389/fpls.2024.1505857
  20. Ferentinos, K.P., 2018. Deep Learning Models for Plant Disease Detection and Diagnosis. Computers and Electronics in Agriculture. 145, 311–318. DOI: https://doi.org/10.1016/j.compag.2018.01.009
  21. Mohanty, S.P., Hughes, D.P., Salathé, M., 2016. Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science. 7, 1419. DOI: https://doi.org/10.3389/fpls.2016.01419
  22. Cap, Q.H., Uga, H., Kagiwada, S., et al., 2020. LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis. arXiv preprint. arXiv.2002.10100. DOI: https://doi.org/10.48550/arXiv.2002.10100
  23. Thakur, P.S., Khanna, P., Sheorey, T., et al., 2022. Explainable Vision Transformer Enabled Convolutional Neural Network for Plant Disease Identification: PlantXViT. arXiv preprint. arXiv.2207.07919. DOI: https://doi.org/10.48550/arXiv.2207.07919
  24. Kanakala, S., Ningappa, S., 2025. Detection and Classification of Diseases in Multi-Crop Leaves Using LSTM and CNN Models. arXiv preprint. arXiv.2505.00741. DOI: https://doi.org/10.48550/arXiv.2505.00741
  25. Baiju, B.V., Kirupanithi, N., Srinivasan, S., et al., 2025. Robust CRW Crops Leaf Disease Detection and Classification in Agriculture Using Hybrid Deep Learning Models. Plant Methods. 21, 18. DOI: https://doi.org/10.1186/s13007-025-01332-5
  26. kaggle, n.d. PlantVillage Dataset. Available from: https://www.kaggle.com/datasets/emmarex/plantdisease (cited 20 July 2025).
  27. Asghar, M., Khan, Z.F., Ramzan, M., et al., 2025. A Lightweight Hybrid Model for Scalable and Robust Plant Leaf Disease Classification. Scientific Reports. 15, 32353. DOI: https://doi.org/10.1038/s41598-025-08788-4
  28. Kamilaris, A., Prenafeta-Boldú, F.X., 2018. Deep Learning in Agriculture: A Survey. Computers and Electronics in Agriculture. 147, 70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016
  29. Ahmad, A., Saraswat, D., El Gamal, A., 2023. A Survey on Using Deep Learning Techniques for Plant Disease Diagnosis and Recommendations for Development of Appropriate Tools. Smart Agricultural Technology. 3, 100083. DOI: https://doi.org/10.1016/j.atech.2022.100083
  30. Sundhar, S., Sharma, R., Maheshwari, P., et al., 2025. Enhancing Leaf Disease Classification Using GAT-GCN Hybrid Model. arXiv preprint. arXiv.2504.04764. DOI: https://doi.org/10.48550/arXiv.2504.04764
  31. Barbedo, J.G.A., 2019. Factors Influencing the Use of Deep Learning for Plant Disease Recognition. Biosystems Engineering. 172, 84–91. DOI: https://doi.org/10.1016/j.biosystemseng.2018.05.013
  32. Singh, A., Ganapathysubramanian, B., Singh, A.K., et al., 2016. Machine Learning for High-Throughput Stress Phenotyping in Plants. Trends in Plant Science. 21(2), 110–124. DOI: https://doi.org/10.1016/j.tplants.2015.10.015
  33. Pantazi, X.E., Moshou, D., Tamouridou, A.A., 2017. Automated Leaf Disease Detection in Different Crop Species Through Image Features Analysis and Machine Learning. Computers and Electronics in Agriculture. 156, 96–104. DOI: https://doi.org/10.1016/j.compag.2018.11.005
  34. Lowe, A., Harrison, N., French, A.P., 2017. Hyperspectral Image Analysis Techniques for the Detection and Classification of the Early Onset of Plant Disease and Stress. Plant Methods. 13, 80. DOI: https://doi.org/10.1186/s13007-017-0233-z