International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Signify: An ML Based Plant Disease Detection System

Neha BagulStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaSayali ChorgheStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaRiya MajiStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaDiptimai SahooStudent, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, IndiaManisha HatkarProfessor, Dept. of AI&ML, Smt. Indira Gandhi College of Engineering, Ghansoli, Navi Mumbai, Maharashtra, India

Vol 9 No 4 (2025): Volume 9, Issue 4, April 2025 | Pages: 119-126

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 23-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.904018

pdf iconFull Text PDF
Abstract

Agriculture remains the backbone of many economies, and plant health is essential for food security and high yields. Traditional methods of plant disease identification are slow, inconsistent, and inaccessible for many farmers. To address these challenges, we propose a deep learning-based Plant Disease Detection System that identifies plant diseases through image recognition. Users can upload images of diseased leaves to receive fast, accurate diagnoses and tailored treatments. Utilizing transfer learning, our system fine-tunes the VGG-16 Convolutional Neural Network (CNN) on the Plant Village dataset. The web-based interface, built using Flask, enables easy interaction and disease management. This paper discusses the development and implementation of the system, highlighting its potential to revolutionize plant disease management and support sustainable agriculture. The approach is validated through rigorous performance metrics, and future enhancements are explored.

Keywords

Leaf Disease Detection, Machine Learning, VGG-16, Transfer Learning, Image Classification, Deep Learning, Smart Agriculture, Plant Village Dataset, Precision Farming, Convolutional Neural Networks


Citation of this Article

Neha Bagul, Sayali Chorghe, Riya Maji, Diptimai Sahoo, & Manisha Hatkar. (2025). Signify: An ML Based Plant Disease Detection System. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(4), 119-126. Article DOI https://doi.org/10.47001/IRJIET/2025.904018
Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. A.H. Kulkarni, A.R.K. Patil, "Applying image processing technique to detect plant diseases," International Journal of Modern Engineering Research, vol. 2, no. 5, pp. 3661–3664, 2012.
  2. T.H. Jaware, R.D. Badgujar, P.G. Patil, "Leaf disease detection using image segmentation," National Conference on Advances in Communication and Computing, pp. 190–194, 2012.
  3. S.B. Dhaygude, N.P. Kumbhar, "Agricultural Plant Leaf Disease Detection Using Image Processing," IJAREEIE, vol. 2, no. 1, pp. 599–602, 2013.
  4. K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2015.
  5. D.P. Hughes, M. Salathe, "An open access repository of images on plant health to enable the development of mobile disease diagnostics," arXiv preprint arXiv:1511.08060, 2015.
  6. Mohanty, S.P., Hughes, D.P., & Salathé, M. (2016).Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
  7. Ferentinos, K.P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
  8. Barbedo, J.G.A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1), 660.
  9. 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.
  10. Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based leaf disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290.
  11. Zhang, S., Wu, X., & You, Z. (2017).Leaf image based cucumber disease recognition using sparse representation classification. Computers and Electronics in Agriculture, 134, 135–141.
  12. Amara, J., Bouaziz, B., & Algergawy, A.(2017). A deep learning-based approach for banana leaf diseases classification. In Datenbanksysteme für Business, Technologie und Web (BTW 2017) – Workshopband (pp. 79–88).
  13. 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.

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.