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Image Analysis for Ethiopian Coffee Plant Diseases Identification
Abrham Debasu Mengistu, Seffi Gebeyehu Mengistu , Dagnachew Melesew Alemayeh
Pages - 1 - 11     |    Revised - 30-04-2016     |    Published - 01-06-2016
Volume - 10   Issue - 1    |    Publication Date - June 2016  Table of Contents
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KEYWORDS
Otsu, FCM, K-means, Gaussian Distribution.
ABSTRACT
Diseases in coffee plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Now a day’s coffee plant diseases detection has received increasing attention in monitoring large field of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of coffee plant diseases. This paper presents an automatic identification of Ethiopian coffee plant diseases which occurs on the leaf part and also provides suitable segmentation technique regarding the identifications of the three types of Ethiopian coffee diseases. In this paper different classifiers are used to classify such as artificial neural network (ANN), k-Nearest Neighbors (KNN), Naïve and a hybrid of self organizing map (SOM) and Radial basis function (RBF) .We also used five different types of segmentation techniques i.e. Otsu, FCM, K-means, Gaussian distribution and the combinations of K-means and Gaussian distribution. We conduct an experiment for each segmentation technique to find the suitable one. In general, the overall result showed that the combined segmentation technique is better than Otsu, FCM, K-means and Gaussian distribution and the performance of the combined classifiers of RBF (Radial basis function) and SOM (Self organizing map) together with a combination of k-means and Gaussian distribution is 92.10%.
1 CiteSeerX 
2 Scribd 
3 SlideShare 
4 PdfSR 
1 Atsbaha GebreSilasie and Tesema Bekele, “A review of Ethiopian agriculture: Roles, policy and Small scale farming systems”, KOPIN, 2010.
2 Savita N. Ghaiwat and Parul Arora, “Detection and Classification of Coffee diseases Using Image processing Techniques”: A Review, Volume-2, Issue - 3, 2014.
3 Xinshen Diao, “Economic Importance of Agriculture for sustainable development and poverty reduction: the case study of Ethiopia. Paris, 2010.
4 Joel Iscaro, “The Impact of Climate Change on Coffee Production in Colombia and Ethiopia”, Vol. 5, No. 1, 2014.
5 Berhanu Aebissa, “Developing A knowledge based system for coffee diseases diagnosis and treatment” Addis Ababa University, 2012.
6 P.Revathi, M.Hemalatha, “Classification of cotton leaf spot diseases using image processing edge detection techniques” IEEE, 2010.
7 Dheeb Al Bashish, Malik Braik and Sulieman Bani-Ahmad “A framework for detection and classification of plant leaf and stem diseases” IEEE, 2010.
8 Elham Omrani, et al, “Potential of radial basis function-based support vector regression for apple disease detection” ELSEVIER, 2014.
9 Sanjay B. Dhaygude and Nitin P.Kumbhar,, “Agricultural Coffee diseases Disease Detection Using Image Processing”, Vol. 2, Issue 1, January 2013.
10 URL:http://www.apsnet.org/edcenter/intropp/topics/Pages/PlantDiseaseDiagnosis.aspx.
11 URL:http://www.gardeningknowhow.com/plant-problems/disease/plant-leaf-spots.htm.
12 Arti N. Rathod, Bhavesh Tanawal and Vatsal Shah, “Image Processing Techniques for Detection of Leaf Disease”, Volume 3, Issue 11, November 2013.
13 Elham Omrani, Benyamin Khoshnevisan, Shahaboddin Shamshirband, Hadi Saboohi, Nor Badrul Anuar and Mohd Hairul Nizam Md Nasir, “Potential of radial basis function based support vector regression for apple disease detection”, ELSEVIER, Measurement 55, 2014.
14 Prakash M. Mainkar, Shreekant Ghorpade and Mayur Adawadkar, “Coffee diseases Disease Detection and Classification Using Image Processing Techniques, Volume 2, Issue 4, 2015.
15 Premalatha.V, Valarmathy.S and Sumithra.M.G, “Disease Identification in Cotton Plants Using Spatial FCM & PNN Classifier”, Volume 3, Issue 4, April 2015.
16 Nikita Rishi1 and Jagbir Singh Gill, “An Overview on Detection and Classification of Plant Diseases in Image Processing”, 2014.
17 Jayme Garcia Arnal Barbedo, “Digital image processing techniques for detecting, quantifying and classifying plant diseases”, Barbedo SpringerPlus 2013.
18 Haiguang Wang, Guanlin Li, Zhanhong Ma and Xiaolong Li, “Image Recognition of Plant Diseases Based on Backpropagation Networks” : IEEE, 2015.
19 Khushal Khairnar and Rahul Dagade. “Disease Detection and Diagnosis on Plant using Image Processing”: A Review, Volume 108 – No. 13, December 2014.
20 Tinku Acharya and Ajoy K. Ray, “Image Processing Principles and Applications” , Jhon Wiley, 2005.
21 William K. Pratt: “Digital image processing, PIKS Scientific inside”, John Wiley, 4th Edition, 2007.
22 Phadikar, S, “Rice disease identification using pattern recognition techniques", pp. 420 - 423, 2008.
23 S. Phadikar, J. Sil, and A. K. Das, “Classification of Rice Leaf Diseases Based on morphological Changes”, Breeding Science, pp. 93-96, 2008.
24 Habtamu Minasie “Image analysis for Ethiopian coffee classification”, Addis Ababa University , 2008.
25 Ali Salem , Bin Samma and Rosalina Abdul Salam, ”Adaptation of K-Means Algorithm for Image Segmentation”, CiteSeerx.
26 Anesh Bhadane, Sapana Sharma and Vijay B. Nerkar, “Early Pest Identification in Agricultural Crops using Image Processing Techniques”, volume 2, 2013.
27 Niket Amoda, Bharat Jadhav andSmeeta Naikwadi, “Detection and Classification of plant Diseass by Image processing”, Vol. 1 Issue 2, April 2014.
28 Jayamala K. Patil , Raj Kumar,” Advances In Image Processing For Detection Of Plant Diseases”, Vol 2, Issue 2, June-2011.
29 Jadadeesh D. Rajesh Yakkundimath and Abdulmunaf S.Byadgi,”Image processing based detection of fungal diseases in plant leaf”, Science direct, 2015.
30 Jayme Garcia Arnal Barbedo,” Digital image processing techniques for detecting, quantifying and classifying plant diseases”, SpringerPlus 2013 a review.
Mr. Abrham Debasu Mengistu
Bahir Dar University - Ethiopia
abrhamd@bdu.edu.et
Mr. Seffi Gebeyehu Mengistu
Bahir Dar University - Ethiopia
Mr. Dagnachew Melesew Alemayeh
Bahir Dar University - Ethiopia