Experimental Selection of Machine learning Techniques and Image features to Detect “Cactus” Diseases

Abstract

Image is a very important data in machine learning. In order to select better features, feature extraction techniques and classifiers, intensive experiments are taken place using data. In this work, best feature, feature extraction technique and machine learning classifier are selected experimentally. Hence, bag of features were the best features experimentally out of color, texture and bag of features. Of color histogram, bag of features and GLCM (Gray-level co-occurrence matrix), bag of features extraction technique is found to be the best one experimentally. Of the machine learning classifiers shown in the scatter plot and confusion matrix, linear support vector machine is selected and the achieved accuracy is 97.2%.