World Applied Sciences Journal 23 (9): 1207-1211, 2013 ISSN 1818-4952 © IDOSI Publications, 2013 DOI: 10.5829/idosi.wasj.2013.23.09.1000 Corresponding Author: Mokhled S. Al-Tarawneh, Department of Computer Engineering, Faculty of Engineering, Mutah University, P.O. Box: (7), Mutah 61710, Jordan. Tel: +962799717222, Fax: +962 3 2375540. 1207 An Empirical Investigation of Olive Leave Spot Disease Using Auto-Cropping Segmentation and Fuzzy C-Means Classification Mokhled S. Al-Tarawneh Department of Computer Engineering, Faculty of Engineering, Mutah University, P.O. Box: (7), Mutah 61710, Jordan Submitted: Jun 8, 2013; Accepted: Jul 15, 2013; Published: Jul 20, 2013 Abstract: The objective of this research was to investigate an image analysis and classification techniques for detection and severity rating of olive leaf spot disease. Samples of olive leaves were collected from field and imaged under uncontrolled illumination. Images resolution were resized to 256×256 pixels and transformed from RGB to L*a*b* color space. The transformed images were then cropped polygonal to segment the region of interest and classified using fuzzy c-mean clustering for statistical usage to determine the defect and severity areas of plant leaves. Imaged enhancement was performed using median filtering. The severity percentage was calculated based on classification of detected diseased and total leaf areas. Comparative assessment of FCM and KCM was conducted with reference to mean opinion scoring of image data. The results showed a good agreement between FCM and manual scoring and by image analysis at an 86% accuracy rate comparing to KMC with 66%. Key words: Olive leaf spot disease Cropping Color transformation Segmentation and Image classification INTRODUCTION The olive is the most important fruit tree grown in Jordan. The total cultivated area with olives is about 130,000 ha representing 72% of the total planted area with fruit trees and 36% of the total cultivated area in Jordan[1]. One of the important foliar diseases affecting olive trees in Jordan and many other countries in the world is peacock spot disease also called olive leaf spot (OLS) and bird's-eye spot [2]. Twig death may occur in Fig. 1: Olive leaf spot, also known as peacock spot infected trees as a result of defoliation and productivity is eventually further reduced with great damage of conduct assess the infected leaves to define the severity plantation. Symptoms of disease start as sooty blotches of OLS [4]. In this work an image processing techniques on leaves develop into muddy green black circular spots were used to identify OLS, make correct diagnostics and 2.5 to 12 mm in diameter, there maybe a yellow halo analysis of such disease. Objective assessments were around the spot [3]. Spots quantities are considered the used to get more accurate system for identification and important units indicating the severity of diseases, recognition of this foliar disease. Figure 1. The relationship between proportions of olive leaves Previous Work: Plant disease was tackled using image diseased and the amount of affected tissue is a valuable processing techniques for the sake of disease detection, tool for disease assessment and management. Although identification and recognition. Building vision system the area of infected tissue is the most commonly used for disease detection to find defected area by color, measurement of severity, that why this work aimed to shape and textures need to be determined feasibly based