Analysis of Late Blight Disease in Tomato Leaf Using Image Processing Techniques

Full Text (PDF, 595KB), PP.12-22

Views: 0 Downloads: 0


Megha P Arakeri 1 Malavika Arun 1 Padmini R K 1

1. M.S Ramaiah Institute of Technology, M.S.R Nagar, Bangalore 560-054, India

* Corresponding author.


Received: 14 Aug. 2015 / Revised: 17 Sep. 2015 / Accepted: 25 Oct. 2015 / Published: 8 Nov. 2015

Index Terms

Agriculture, tomato, late blight, segmentation, K- means clustering


Tomato (Lycopersicon esculentum L.) is one of the most widely grown crops in the world. This crop is easily prone to various diseases. One such disease is late blight, caused by the fungus Phytophthora infestans. The first symptoms of late blight on tomato leaves are irregularly shaped, water soaked lesions, which are typically found on the younger leaves of the plant canopy. During humid conditions, white cottony growth may be visible on the underside of affected leaves. As the disease progresses, lesions enlarge causing leaves to brown, shrivel and perish. Hence in the present paper, a novel computer vision system has been proposed for detection and analysis of late blight disease. The proposed system implements thresholding algorithm to classify the leaf as diseased or healthy. Later it uses K -means clustering algorithm for analyzing late blight disease. The experiment was carried out on leaves of tomato collected from various plantations. The accuracy, sensitivity and specificity of the developed system in analyzing the late blight disease are 84%, 85% and 80% respectively.

Cite This Paper

Megha P Arakeri, Malavika Arun, Padmini R K,"Analysis of Late Blight Disease in Tomato Leaf Using Image Processing Techniques", International Journal of Engineering and Manufacturing(IJEM), Vol.5, No.4, pp.12-22, 2015. DOI: 10.5815/ijem.2015.04.02


[1]Pinaki Mondal, Manisha Basu. Adoption of precision agriculture technologies in India and in some developing countries. Scope, present status and strategies, progress in Natural Science 2009;19:659-666.

[2]Md. Rokunuzzaman, H.P.W. Jayasuriya. Development of a low cost machine vision system for sorting of tomatoes. Agricultural Engineering International: CIGR Journal 2013;15(1):173-180.

[3]Late blight on tomato.[online], 2014,

[4]Jagadeesh Devdas Pujari, Rajesh Yakkundimath, Abdulmunaf Syedhusain Byadgi. Grading and Classification of Anthracnose Fungal Disease of Fruits based on Statistical Texture Features. International Journal of Advanced Science & Technology 2013;52:121-132.

[5]Minghua Zhang, Zhihao Qin, Xue Liu. Remote sensed spectral imagery to detect late blight in field tomatoes. Precision Agriculture, Springer 2005;6:489–508.

[6]S. Arivazghan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini. Detection of Unhealthy Region of Plant leaves and Classification of Plant Diseases using Texture Features. Agricultural Engineering International: CIGR Journal 2013;15(1):211-217.

[7]Niket Amoda, Bharat Jadhav, Smeeta Naikwadi. Detection and Classification of Plant Diseases using Image Processing. International Journal of Innovative Science, Engineering and Technology 2014;1(2): 1-7.

[8]Guili Xu, Fenling Zhang, Syed Ghafoor Shah, Yongqiang Ye, Hanping Mao. Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recognition Letters 2011;32(11):1584-1590.

[9]Sannakki SS, Rajpurohit VS, Nargund VB, Kumar A. Leaf Disease Grading by Machine Vision and Fuzzy Logic. International Journal. 2011;2(5):1709-1716.

[10]Al Bashish D, Braik M, BaniAhmad S. A framework for detection and classification of plant leaf and stem diseases. International conference on signal and image processing Chennai: IEEE 2010;113–118.

[11]Pugoy RADL, Mariano VY. Automated rice leaf disease detection using color image analysis. 3rd International conference on digital image processing 2011;8009:F1–F7.

[12]Wang H, Li G, Ma Z, Li X. Proceedings of the 2012. International Conference on Systems and Informatics (ICSAI) Yantai: IEEE; 2012. Application of neural networks to image recognition of plant diseases; 2159–2164.

[13]Zhang Yongqin, Chen Hui, Wang Ling, Xiao Yongjun, Huang Haibo. Color Image Segmentation Using Level Set Method with Initialization Mask in Multiple Color Spaces. International Journal of Engineering and Manufacturing 2011;4:70-76.

[14]Swati Sharma, Shipra Sharma and Rajesh Mehra. Image Restoration using Modified Lucy Richardson Algorithm in the Presence of Gaussian and Motion Blur. Advance in Electronic and Electric Engineering 2013;3(8):1063-1070.

[15]Vijay Jumb, Mandar Sohani, Avinash Shrivas. Color Image Segmentation Using K-means Clustering and Ostu's Adaptive Thresholding. International Journal of Innovative Technology and Exploring Engineering 2014;3(9):72-76.