Agricultural Data Analysis using Machine Learning: A Study on Dry Bean Classification

Abstract

The classification of Dry Beans using various techniques such as Support Vector Machine (SVM) classification, K-means clustering, Decision Trees and Random Forest (RF) classification using an ipython notebook. To refine the model, performance matrix graphs of Cross entropy vs Epoch number, True value vs Predictive value and Accuracy vs Epoch. This analysis is often used in agricultural practices for improved crop management, increasing yield, resource optimization, enhancing sustainability etc.