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

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

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

	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 = {},
	doi = {10.5120/ijca2017913260},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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