CFP last date
22 April 2024
Reseach Article

A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning

by Nilay Ganatra, Atul Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 13
Year of Publication: 2018
Authors: Nilay Ganatra, Atul Patel
10.5120/ijca2018916249

Nilay Ganatra, Atul Patel . A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning. International Journal of Computer Applications. 180, 13 ( Jan 2018), 7-12. DOI=10.5120/ijca2018916249

@article{ 10.5120/ijca2018916249,
author = { Nilay Ganatra, Atul Patel },
title = { A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 180 },
number = { 13 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number13/28920-2018916249/ },
doi = { 10.5120/ijca2018916249 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:00:33.232707+05:30
%A Nilay Ganatra
%A Atul Patel
%T A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 13
%P 7-12
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Quality agriculture production is the essential trait for any nation’s economic growth. So, recognition of the deleterious regions of plants can be considered as the solution for saving the reduction of crops and productivity. The past traditional approach for disease detection and classification requires enormous amount of time, extreme amount of work and continues farm monitoring. In the last few years, advancement in the technology and researchers’ focus in this area makes it possible to obtain optimized solution for it. To identify and detect the disease on agriculture product various popular methods of the fields like machine learning, image processing and classification approaches have been utilized. This paper presents various existing techniques used to detect the disease of agriculture product. Also, paper surveys the mythologies utilized for disease detection, segmentation of the affected part and classification of the diseases. It also includes the summary of various feature extraction techniques, various segmentation techniques and various classifiers along with benefits and drawbacks.

References
  1. V. A. G. Ajay a. Gurjar, “disease detection on cotton leaves by eigenfeature regularization and extraction technique,” internation journal of electronics, communication & soft computing science and engineering(ijecscse), vol. I, no. I.
  2. H.-p. M. B. H. M.-x. L. Yan-cheng zhang, “features selection of cotton disease leaves image based on fuzzy feature selection techniques,” in ieee proceedings of the 2007 international conference on wavelet analysis and pattern recognition, beijing, china, 2007.
  3. G. Z. Libo liu, “extraction of the rice leaf disease image based on bp neural network,” in ieee, 2009.
  4. P. Sachin d. Khirade, “plant disease detection using image processing,” in ieee 2015 international conference on computing communication control and automation, pune, 2015.
  5. H. B. P. V. K. D. Jitesh p. Shah, “a survey on detection and classification of rice plant diseases,” in ieee international conference on current trends in advanced computing (icctac), bangalore, 2016.
  6. S. J. Shiv ram dubey, “detection and classification of apple fruit diseases using complete local binary patterns,” in ieee 2012 third international conference on computer and communication technology, 2012.
  7. K. B. Monika jhuria, “image processing for smart farming: detection of disease and fruit grading,” in proceedings of the 2013 ieee second international conference on image information processing , 2013.
  8. S. D. D. Bhavini j. Samajpati, “a survey on apple fruit diseases detection and classification,” international journal of computer applications, vol. 130, no. 13, pp. 25-32, november 2015.
  9. E. M. Godliver owomugisha, “machine learning for plant disease incidence and severity measurements from leaf images,” in 15th ieee international conference on machine learning and applications, 2015.
  10. P. K. K. A. A. Meunkaewjinda, “grape leaf disease detection from color imagery using hybrid intelligent system,” in ieee 2008 5th international conference on electrical engineering/electronics, computer, telecommunications and information technology, 2008.
  11. M. R. S. M. Ravindra naik, “plant leaf and disease detection by using hsv features and svm classifier,” international journal of engineering science and computing, vol. 6, no. 12, pp. 3794-3797, 2016.
  12. N. E. A. H. H. Suhaili beeran kutty, “classification of watermelon leaf diseases using neural network analysis,” ieee business engineering and industrial applications colloquium, 2013.
  13. P. L. R. P. S. P. N. K. Muthukannan, “classification of diseased plant leaves using neural network algorithms,” arpn journal of engineering and applied sciences , vol. 10, no. 4, 2015.
  14. R. M. U. R. Loyce selwyn pinto, “crop disease classification using texture analysis,” in ieee international conference on recent trends in electronics information communication technology, 2016.
  15. F. Encyclopedia, “image segmentation,” [online]. Available: https://en.wikipedia.org/wiki/image_segmentation.
  16. X. J. Y. S. J. W. H.d. Cheng, “color image segmentation: advances and prospects,” elsevier, 2001.
  17. T. F. E. Wikipedia, “feature extraction,” [online]. Available: https://en.wikipedia.org/wiki/feature_extraction.
  18. U. K. J. D. G. T. Uravashi solanki, “a survey on detection of disease and fruit grading,” international journal of innovative and emerging, vol. 2, no. 2, 2015.
  19. V. L. M.-f. D. O. Kleynen, “development of a multi-spectral vision system for the detection of defects on apples,” elsevier, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Classification image processing machine learning segmentation feature extraction pre-processing