Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN

• Main Authors: Mat Said, Nur Sholehah, Madzin, Hizmawati, Ali, Siti Khadijah, Ng, Seng Beng
• Format: Article
• Published: Institute of Advanced Engineering and Science (IAES) 2021
• Online Access: http://psasir.upm.edu.my/id/eprint/96465/; https://ijeecs.iaescore.com/index.php/IJEECS/article/view/26341

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.