Wheat Kernel Classification Using Machine Learning

Abstract

Machine learning is mainly divided into two sections, specifically supervised and unsupervised learning. Supervised learning is additionally divided into two fundamental parts, that is, the regression type and classification type. Unsupervised learning additionally has its types like clustering, association, PCA, and so on. Here, we consider the use case from the supervised machine learning approach. We consider the dataset of the wheat kernel, used to classify 3 types of wheat, specifically Kama, Rosa, and Canadian, consisting of properties of the wheat kernel like area, compactness, perimeter, and so on. So, as this dataset comprises 3 classes or 3 distinct results, it is called multi-class data, which is multiple classes. Various machine learning algorithms can assist us in solving this multi-class classification problem. Some of the algorithms from Bagging [Ensemble Technique] are Random Forest, Boosting [Ensemble Technique] are LightGBM, XGBoost, Gradient Boosting, Support Vector Machine Classifier, Decision Tree Classifier, Logistic Regression, and so on. The above-mentioned algorithms work in different ways to find the solution to multi-class classification problems. In a large number of problems, we use an SVM classifier to solve the wheat kernel identifier use case. The SVM classifier is essential in solving binary classification problems. Nonetheless, here, in the wheat kernel identification case, multiple classes are three types of classes of items in the original dataset. This work focuses on learning the approaches for improving the results by using the support vector classifier and applying legitimate hyper-parameter tuning to get generalized output.