Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Detection of Healthy and Defected Diseased Leaf of Rice Crop using K-Means Clustering Technique

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Prabira Kumar Sethy, Baishalee Negi, Nilamani Bhoi

Prabira Kumar Sethy, Baishalee Negi and Nilamani Bhoi. Detection of Healthy and Defected Diseased Leaf of Rice Crop using K-Means Clustering Technique. International Journal of Computer Applications 157(1):24-27, January 2017. BibTeX

	author = {Prabira Kumar Sethy and Baishalee Negi and Nilamani Bhoi},
	title = {Detection of Healthy and Defected Diseased Leaf of Rice Crop using K-Means Clustering Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {1},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {24-27},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017912601},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Rice covers about 69 percent of the cultivated area and is the major crop covering about 63 percent of total area under the food grains [1]. It is a staple food of almost entire population of Odisha; therefore state economy has directly affected the production of rice in the state [1]. The common disorder found in the rice crop and usually appears at the tillering and panicle initiation stage which shows on the leaves of rice crop [2]. The disorder is due to mineral deficiency and infection caused by the pest. The disorders are visualized by discoloration and dead spots on leaves. It may beneficial for detecting defected diseased leaf by the symptoms that found on the surface of leaves [3]. In this paper, a novel approach to identify defected diseased leaf by using K-Means clustering or 3-Means clustering method is proposed. The experimental outcomes demonstrate that the proposed method is an impressive technique for the detection of defected part present on the leaves of the rice crop.


  1. S.R. Das, Department of Plant Breeding and Genetics, “Current status of the rice crop in Odisha”, University of Odisha Agriculture and Technology, BBSR.
  2. Karen Moldenhauer et al., “Development and Growth of rice crop”, the University Of Arkansas Division Of Agriculture.
  3. Prashant Jain, Jimita Baghel, “Leaf Disease Detection by using K-Means Based Segmentation”. Int. Journal of Engineering Research and Application, ISSN: 2248-9622, Issue 3, (Part -5), Vol. 6, March 2016.
  4. Khaled Alsabti, Sanjay Ranka, Vineet Singh, “An Effective K-Means Clustering Method”, ITL, Hitachi America, Ltd.
  5. Prof. T.M. Dudhane, Prof. Dr. Sanjay B. Patil, “Leaf disease detection by the help of K-Means Segmentation Method and CBIR system”, IRJET, e-ISSN: 2395 -0056, p-ISSN: 23950072, Dec-2015.
  6. Pushkar Dixit et al, “Diseased Fruit Area Detected by using K- Means Clustering Segmentation Method”, ijimai.2013.229, Vol. 2, NO. 2.
  7. Niket Amoda, Smita Naikwadi, “DETECTION OF PLANT DISEASES USING IMAGE PROCESSING TECHNIQUE”, IJAIEM, Volume 2, Issue 11, ISSN 2319 – 4847.
  8. Malik Braik, Dheeb Al Bashish, Sulieman Bani-Ahmad, “Leaf Disease Detection and Classification by using K- means algorithm and Neural-networks classification”, Information Technology Journal, ISSN 1812-5638.
  9. A. B. Patil, Sachin D. Khirade, “Detection of Plant Disease Using Advance Image Processing Technique”, International Conference on Computing Communication Control and Automation, IEEE, 978-1-4799-6892-3/15, 2015.
  10. Tapas Kanungo and David M. Mount, “Analysis andImplementation of An Efficient K-Means Clustering Method”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No.7.


Defected leaf disease detection; K-Means clustering; rice crop; defected segmentation.