Image Segmentation Algorithms for Banana Leaf Disease Diagnosis

Abstract

Identification and classification of leaf diseases in banana crop are an important requirement for farmers to diagnose and to get proper remedies for the pest and disease infection. Development of an automated system using image processing for leaf disease identification reduces time, cost and mainly supports to increase the productivity of banana fruit. In this process of automation, image segmentation is a key component that is required to analyze the image and to extract information from it. Image segmentation is a low-level module of image processing used to segregate the required object from an image for further analysis. The performance accuracy of image segmentation module determines the success of higher-level module of image processing. Therefore, to select an appropriate segmentation method for leaf analysis, different segmentation methods like adaptive thresholding, canny, color segmentation, fuzzy C-means, geodesic, global thresholding, K-means, log, multithresholding, Prewitt, region growing, Robert, Sobel and zero crossing are analyzed and compared in this paper. The quantitative matrices such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are considered to measure the performance of different segmentation methods. The results showed that geodesic method had significantly lower MSE value (6610), PSNR value (6608) and higher SSIM value (0.196) than all other methods. It is concluded that geodesic method is better for segmentation of banana leaf disease images.