Plant leaves disease detection using Image Processing and Machine learning techniques

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.926
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.
Volume 13, Special Issue 5, November 2022

##plugins.themes.academic_pro.article.main##

Pratibha Kokardekar
Aman Shah
Arjun Thakur
Shri Ramdeobaba College of Engineering and Management
Prachi Shahu
Rohan Raggad
Sudhanshu Keshaowar
Vineet Pashine

Abstract

Agriculture plays a very important role in strengthening the economy of a country. Disease in plants is the major
cause of production and economy loss which also reduced the quality and quantity of agriculture products. Farmers
face a lot of difficulty in detecting the diseases with naked eye which is the traditional and most used way. It is
an important and tedious task to detect disease on crops. It requires a lot of skilled labour and huge amount of
time. This paper compares the benefits and limitations of existing techniques for disease detections. Finally, it
will talk about a method for disease detection in plants using convolutional neural network (CNN).

##plugins.themes.academic_pro.article.details##

Creative Commons License

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

How to Cite
Pratibha Kokardekar, Aman Shah, Arjun Thakur, Prachi Shahu, Rohan Raggad, Sudhanshu Keshaowar, & Vineet Pashine. (2022). Plant leaves disease detection using Image Processing and Machine learning techniques. International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.926
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Bhange, M. and Hingoliwala, H. 2015. Smart farming: Pomegranate disease detection using image processing. Procedia computer science 58, 280–288. DOI: https://doi.org/10.1016/j.procs.2015.08.022
  2. Kulkarni, P., Karwande, A., Kolhe, T., Kamble, S., Joshi, A., and Wyawahare, M. 2021. Plant disease detection using image processing and machine learning. arXiv preprint arXiv:2106.10698 .
  3. Mohanty, S. P., Hughes, D. P., and Salath´e, 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
  4. Roopali, G. and Verma, T. 2020. Tomato leaf disease detection using back propagation neural network. International Journal of Innovative Technology and Exploring Engineering 9, 8, 529–538. DOI: https://doi.org/10.35940/ijitee.H6531.069820
  5. Shinde, P. and Manjrekar, A. 2013. Efficient classification of images using histogram based average distance computation algorithm extended with duplicate image detection elsevier, proc. of int. conf. On advances in Computer Sciences, AETACS, 102–110. DOI: https://doi.org/10.1109/I2CT.2014.7092036
  6. Singh, A. K., Sreenivasu, S., Mahalaxmi, U., Sharma, H., Patil, D. D., and Asenso, E. 2022. Hybrid feature-based disease detection in plant leaf using convolutional neural network, bayesian optimized svm, and random forest classifier. Journal of Food Quality 2022. DOI: https://doi.org/10.1155/2022/2845320
  7. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience 2016. DOI: https://doi.org/10.1155/2016/3289801