期刊名称：Indian Journal of Innovations and Developments
印刷版ISSN：2277-5382
电子版ISSN：2277-5390
出版年度：2016
卷号：5
期号：7
页码：1-8
语种：English
出版社：Indian Society for Education and Environment
摘要：Objectives : The main objective of introducing SOM based clustering method is to improve the classification accuracy and detection of bacterial spot disease in tomato field. Methods : There are various image processing methods used to identify disease and severity of disease in plants. One of such methods uses visible spectrum Images for automatically detecting and classifying the severity of bacterial spot in tomato fields. Centroid-based K-means clustering was widely used for automatic segmentation. Findings : Plant diseases are one of the major responsibilities for economic degradation in the agricultural industry. So regular monitoring of plant health and early detection of disease causing pathogens are required for minimizing disease spread and assist effective management practices. Centroid-based K-means clustering for segmentation always does not chose centroids that provide best results and also different initial set of centroids affect the shape and effectiveness of the final cluster. Application/Improvements : To overcome the limitations of Centroid-based K-means clustering, Self-Organizing Maps (SOM) is introduced for achieving effective classification result and to improve the detection performance.
其他摘要：Objectives : The main objective of introducing SOM based clustering method is to improve the classification accuracy and detection of bacterial spot disease in tomato field. Methods : There are various image processing methods used to identify disease and severity of disease in plants. One of such methods uses visible spectrum Images for automatically detecting and classifying the severity of bacterial spot in tomato fields. Centroid-based K-means clustering was widely used for automatic segmentation. Findings : Plant diseases are one of the major responsibilities for economic degradation in the agricultural industry. So regular monitoring of plant health and early detection of disease causing pathogens are required for minimizing disease spread and assist effective management practices. Centroid-based K-means clustering for segmentation always does not chose centroids that provide best results and also different initial set of centroids affect the shape and effectiveness of the final cluster. Application/Improvements : To overcome the limitations of Centroid-based K-means clustering, Self-Organizing Maps (SOM) is introduced for achieving effective classification result and to improve the detection performance.
