Image Segmentation Algorithms for Banana Leaf Disease Diagnosis
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ORIGINAL CONTRIBUTION
Image Segmentation Algorithms for Banana Leaf Disease Diagnosis Suryaprabha Deenan1 • Satheeshkumar Janakiraman2 • Seenivasan Nagachandrabose3
Received: 21 August 2019 / Accepted: 5 June 2020 Ó The Institution of Engineers (India) 2020
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) & Seenivasan Nagachandrabose [email protected]; [email protected] 1
School of Post Graduate Studies, Tamil Nadu Agricultural University, Coimbatore 641003, Tamil Nadu, India
2
Department of Computer Applications, Bharathiar University, Coimbatore 641003, Tamil Nadu, India
3
Department of Plant Pathology, Agricultural College and Research Institute, Madurai 625104, Tamil Nadu, India
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. Keywords Image segmentation Edge detection Region growing Active contour Thresholding
Introduction Banana is a staple food that is consumed all over the world by more than millions of people. Due to its demand and consumption throughout the year, it provides major support for country’s economy. Healthier banana crop with better resistant ability to pest and diseases determines higher productivity of banana fruit [1]. Crops exhibit varied symptoms of infection for varied pests and diseases. Symptoms inflate as the severity of the infection increases. So it is essential to monitor, diagnose and identify pest and disease in the crop in its earlier stage itself so as to avoid any loss in production of banana fruit. Farmers are most of the time unaware about disease infection and severity level of infection in crop and seek advice f
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