Plants Disease Segmentation using Image Processing

Full Text (PDF, 267KB), PP.24-32

Views: 0 Downloads: 0


Rabia Masood 1,* S.A. Khan 1 M. N. A. Khan 1

1. Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan.

* Corresponding author.


Received: 12 Sep. 2015 / Revised: 26 Oct. 2015 / Accepted: 20 Nov. 2015 / Published: 8 Jan. 2016

Index Terms

Plant disease segmentation, morphological characteristics, features extraction, neural Networks, Fuzzy Logic


The image segmentation performs a significant role in the field of image processing because of its wide range of applications in the agricultural fields to identify plants diseases by classifying the different diseases. Classification is a technique to classify the plants diseases on different morphological characteristics. Different classifiers are used to classify such as SVM (Support Vector Machine), K- nearest neighbor classifiers, Artificial Neural Networks, Fuzzy Logic, etc. This paper presents different image processing techniques used for the early detection of different Plants diseases by different authors with different techniques. The main focus of our work is on the critical analysis of different plants disease segmentation techniques. The strengths and limitations of different techniques are discussed in the comparative evaluation of current classification techniques. This study also presents several areas of future research in the domain of plants disease segmentation. Our focus is to analyze the best classification techniques and then fuse certain best techniques to overcome the flaws of different techniques, in the future.

Cite This Paper

Rabia Masood, S.A. Khan, M.N.A. Khan,"Plants Disease Segmentation using Image Processing", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.1, pp.24-32, 2016.DOI: 10.5815/ijmecs.2016.01.04


[1] H.Al-Hiary, S. Bani-Ahmad, M.Reyalat, M.Braik & Z.AlRahamneh, “Fast and Accurate Detection and Classification of Plant Diseases”, International Journal of Computer Applications, Vol. 17, No.1, pp. 31-38.March 2011.

[2] Y. Tian, L. Wang and Q. Zhou , “Grading method of Crop disease based on Image Processing”, Computer and computing technologies in agriculture 427-433, 2011.

[3] R.G. Mundada, Dr. V.V. Gohokar, “Detection and classification of Pests in Green House using Image Processing”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) Vol. 5, Issue 6, PP 57-63, 2013.

[4] S. Ananthi & S.V. Varthini, “Detection and Classification of Plant Leaf Diseases”, International Journal of Research in Engineering & Applied Sciences, Vol. 2, Issue 2, pp.763-773, February 2012.

[5] F. Fina, P. Birch, R. Young, J. Obu, B. Faithpraise and C. Chatwin, “Automatic Plant Pest detection and recognition using k-means clustering algorithm and correspondence filters”, International Journal of Advanced Biotechnology and Research, Vol. 4, Issue 2, pp 189-199, 2013.

[6] J. D.Pujari, R. Yakkundimath & A.S.Byadgi, “Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits’ Images”, International Journal of Intelligent Systems & Applications in Engineering Advanced Technology & Science, vol.1 no.4, pp.60–67, 12th Dec2013.

[7] E. Hitimana & O. Gwun, “Automatic estimation of live Coffee leaf infection based on Image processing techniques”, Second International Conference on Signal, Image Processing and Pattern Recognition, Sydney, Australia, pp. 255–266, 2014.

[8] S. Phadikar, J. Sil, and A. K. Das, “Classification of Rice Leaf Diseases Based on Morphological Changes”, International Journal of Information and Electronics Engineering, Vol. 2, No. 3, pp. 460-463, May 2012.

[9] J. N. Kapur, P. K. Sahoo & A.K.C. Wong, “A New Method for Gray Level Picture Threshold Using the Entropy of the Histogram,” Graphical Models and Image Processing, 29, pp. 273-285. 1985.

[10] T. Touanf and R. C. Gonzalez, “In: Pattern Recognition Principles. London,” UK, Addition Wesley Publishing Company. pp. 110-1543, 1974.

[11] Q. He, B. Ma, D. Qu, Q. Zhang, X. Hou, J. Zhao, “Cotton Pests and Diseases Detection based on Image Processing”, TELKOMNIKA, Indonesian Journal of electrical engineering, Vol. 11, No. 6, pp. 3445 ~ 3450, June 2013.

[12] W. Abudullakasim & J. Unartngam, “Assessment of the Severity of Brown Leaf Spot Disease in Cassava using Image Analysis”, The International conference of the Thai Society of Agricultural Engineering, 2012.

[13] Gudiño, J. Gudiño-Bazaldúa, J. L. Rojas-Rentería, V. Rodríguez-Hernández and V.M. Castaño, “Color image segmentation using perceptual spaces through applets for determining and preventing diseases in chili peppers”, African Journal of Biotechnology Vol. 12,no.7, pp. 679-688, 2013.

[14] F. Ortiz, F. Torres, E. Juan & N. Cuenca, “Colour mathematical morphology for neural image analysis”, Real-Time Imaging 8, pp. 455-465, (2002).

[15] J.Yang, C. Liu & L.Zhang, “Polar space normalization: Enhancing the discriminating power of polar spaces for face recognition”, Pattern Recognit. 43, pp. 1454-1466, (2010).

[16] S. S. Sannakki, V. S. Rajpurohit, V. B. Nargund, A.R. Kumar & P. S. Yallur, “ Leaf Disease Grading by Machine Vision and Fuzzy Logic”, International Journal Computer Technology Applications, vol.2 ,no.5, pp. 1709-1716. 2011.

[17] D. Zhihua, W. Huan, S. Yinmao & W. Yunpeng,” Image segmentation method for cotton mite disease based on color features and area thresholding”, Journal of Theoretical and Applied Information Technology, Vol. 48, No.1. 2013.

[18] P. Chaudhary, A. K. Chaudhari, Dr. A. N. Cheeran & S. Godara, “Color Transform Based Approach for Disease Spot Detection on Plant Leaf”, International Journal of Computer Science and Telecommunications, Vol. 3, Issue 6, June 2012.

[19] S. D. Bauer , F. Korc, W. Fo¨rstner, “the potential of automatic methods of classification to identify leaf diseases from multispectral images”, Precision Agriculture, vol. 12, No.3, pp.361-377, 26 January 2011

[20] S. D. Bauer , F. Korc, W. Fo¨rstner, “Investigation into the classification of diseases of sugar beet leaves using multispectral image”, In E. J. van Henten, D. Goense, & C. Lokhorst (Eds.), Precision agriculture ‘09 (pp. 229–238). Wageningen: Wageningen Academic Press, 2009.

[21] Khan, MNA., Khalid M., ulHaq S., Review of Requirements Management Issues in Software Development. International Journal of Modern Education & Computer Science, 5(1), (2013).

[22] Abbasi, A. A., Khan, M. N. A., & Khan, S. A. (2013). A Critical Survey of Iris Based Recognition Systems. Middle-East Journal of Scientific Research, 15(5), 663- 668.

[23] Zia, A., & Khan, M. N. A. (2012). Identifying key challenges in performance issues in cloud computing. International Journal of Modern Education and Computer Science (IJMECS), 4(10), 59.

[24] Ul Haq, S., Raza, M., Zia, A., & Khan, M. N. A. (2011). Issues in global software development: A critical review. An Appraisal of Off-line Signature Verification Techniques 75 Copyright © 2015 MECS I.J. Modern Education and Computer Science, 2015, 4, 67-75 Journal of Software Engineering and Applications, 4(10), 590.

[25] Zia, A., & Khan, M. N. A. (2013). A Scheme to Reduce Response Time in Cloud Computing Environment. International Journal of Modern Education and Computer Science (IJMECS), 5(6), 56.

[26] Masood, M. A., & Khan, M. N. A. (2015). Clustering Techniques in Bioinformatics. I.J. Modern Education and Computer Science, 2015, 1, 38-46.