Comparisons of classification algorithms on seeds dataset using machine learning algorithm

Abstract

In history, agriculture has been the backbone of the economy. These agricultural activities remain undeveloped due to different factors. The current seed classification analysis is inefficient and has no validation mechanism. In this research, we have made an effort to present a predictive model to predict seed classes using machine learning algorithms. For this development, machine learning algorithm is used to learn from data which can be used to make predictions, to make real-world simulations. The developed model is experimented using seed dataset and then seed classes are predicted using the developed model. . The main machine learning methods used in this research is Logistic Regression (LR), Linear Discriminant Analysis (LDA), K Neighbors Classifier (KNN), Decision Tree Classifier (CART), Gaussian NB (NB), and Support Vector Machine (SVM). This is a good combination of simple linear (LR and LDA), nonlinear (KNN, CART, NB and SVM) algorithm. In this research we are trying to present distinctive machine learning approaches, for classification of various seeds which provide opportunity to individuals or agriculturist to identify various seeds.