Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Detection of Plant Leaf Disease Employing Image Processing and Gaussian Smoothing Approach

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Isaac Kofi Nti, Gyamfi Eric, Yeboah Samuel Jonas
10.5120/ijca2017913260

Isaac Kofi Nti, Gyamfi Eric and Yeboah Samuel Jonas. Detection of Plant Leaf Disease Employing Image Processing and Gaussian Smoothing Approach. International Journal of Computer Applications 162(2):20-25, March 2017. BibTeX

@article{10.5120/ijca2017913260,
	author = {Isaac Kofi Nti and Gyamfi Eric and Yeboah Samuel Jonas},
	title = {Detection of Plant Leaf Disease Employing Image Processing and Gaussian Smoothing Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {2},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {20-25},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume162/number2/27214-2017913260},
	doi = {10.5120/ijca2017913260},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A study of plant observation is critical to regulate the unfold of illness in plants, but its value could be higher and as a result, the producers of agricultural products often skip important preventive procedures to keep their production cost at low value. The detection of plant leaf is a vital factor to forestall serious natural event. Most plant diseases are caused by bacteria, fungi, and viruses. An automatic detection of plant disease is a necessary analytical topic. Computer vision techniques are used to uncover the affected spots from the image through an image processing technique capable of recognizing the plant lesion options is delineated in this paper. The achieved accuracy of the overall system is 90.96%, in line with the experimental results.

References

  1. J. G. A. Barbedo, “Digital image processing techniques for detecting, quantifying and classifying plant diseases,” Springer Open Journal, pp. 10-12, 2013.
  2. N. Petrellis, “Plant Disease Diagnosis Based on Image Processing, Appropriate for Mobile Phone Implementation,” International Conference on Information & Communication Technologies in Agriculture, Food and Environment, pp. 238-246, 2015.
  3. D. Tilman, K. Cassman, P. Matson, R. Naylor and S. Polasky, “Agricultural sustainability and intensive production practices,” Nature, vol. 418, no. 6898, pp. 671-677, 2002.
  4. E. E. Hawkson and T. Ngnenb, “Graphic.com.gh,” 2015. [Online]. Available: http://www.graphic.com.gh/news/general-news/ghana-loses-30-per-cent-of-crop-yields-to-pests-diseases.html. [Accessed 2 March 2016].
  5. S. R. Kamlapurkar, “Detection of Plant Leaf Disease Using Image Processing Approach,” International Journal of Scientific and Research Publications, vol. 6, no. 2, pp. 73-76, 2016.
  6. NCSA, “illinois.edu: Image Processing Techniques,” 2000. [Online]. Available: http://www.ncsa.illinois.edu/People/kindr/phd/PART1.PDF. [Accessed 10 August 2015].
  7. K. R. Gokulakrishnan and Kapilya, “Detecting the Plant Diseases and Issues by Image Processing Technique and Broadcasting,” International Journal of Science and Research, vol. 3, no. 5, pp. 1016-1018, 2014.
  8. A. Martin and S. Tosunoglu, “Image Processing Techniqoes for Machine Vision,” Florida International University Department of Mechanical Engineering 10555 West Flagler Street, Miami, Florida 33174, 2000.
  9. G. X. Ritter and J. N. Wilson, Handbook of Computer Vision Algorithms in Image Algebra, CRC Press, 1996.
  10. G. Anthonys and N. Wickramarachchi, “An image recognition system for crop disease identification of paddy fields in Sri Lanka,” International Conference on Industrial and Information Systems (ICIIS) Sri Lanka: IEEE, pp. 403-407, 2009.
  11. D. J. Sena, F. Pinto, D. Queiroz and P. Viana, “Fall armyworm damaged maize plant identification using digital images.,” Biosyst Eng, vol. 85, no. 4, p. 449–454, 2003.
  12. D. Al Bashish, M. Braik and S. Bani-Ahmad, “A framework for detection and classification of plant leaf and stem diseases.,” 2010 international conference on signal and image processing. IEEE, Chennai, p. 113–118, 2010.
  13. Y. Sanjana and S. S. J. Ashwath, “Plant Disease Detection Using Image Processing Techniques,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 6, pp. 295-301, 2015.
  14. M. Zhang and Q. Meng, “Citrus canker detection based on leaf images analysis,” In Information Science and Engineering (ICISE), 2010 2nd International Conference. IEEE, pp. 3584-3587, 2010.
  15. S. B. Dhaygude and N. P. Kumbhar, “Agricultural plant Leaf Disease Detection Using Image Processing,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, no. 1, pp. 599-602, 2013.

Keywords

Digital-Pictures, Matlab, Image-Processing, Segmentation, Plant-Leaf-Diseases, agricultural-production