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%.
Highlights
The main purpose of this work is to experimentally select machine learning and feature extraction techniques to correctly classify whether the cactus image is healthy or unhealthy
In the model creation phase, data is acquired, images are enhanced, important features are extracted and the model is created by training it by the extracted features
The results of the confusion matrix are expressed in a tabular form
Summary
The main purpose of this work is to experimentally select machine learning and feature extraction techniques to correctly classify whether the cactus image is healthy or unhealthy. The intention of this work is to propose a machine learning model that detects cactus plants as healthy or unhealthy to maintain the quality and quantity of the plant to get the usual benefits. In the model creation phase, data (cactus image) is acquired, images are enhanced, important features are extracted and the model is created by training it by the extracted features. The same activities that are done in the model creation phase are done except the training step In this phase, the extracted features of the new input image are compared with the features in which the model is created and classification is done if there is matching of the features
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