Comparative Analysis of Climate-Smart Agriculture for Marathwada Region Using Machine Learning Algorithms

Abstract

The economy of the Indian region known as Marathwada is mostly dependent on agriculture. But as smart agricultural technology advances, it becomes more and more necessary to use machine learning algorithms to enhance decision-making and maximize resource allocation. This study conducts a comprehensive comparative analysis of various machine learning classification algorithms to identify the most suitable approach for addressing agricultural challenges in the Marathwada region, and improve the crop production. The dataset includes historical yield statistics, crop type, soil, weather, and other agricultural characteristics. Six popular classification algorithms are used for Climate-Smart Agriculture such as Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree with 91%, 96%, 93%, 89%, and 98 % accuracy respectively Results indicate that Random Forest (96%) and Decision Tree (98%) consistently outperform other algorithms across various metrics. Decision trees are quite good at predicting agricultural production because of its ensemble structure and ability to handle complex interactions within the data. The study highlights how important feature selection and preprocessing are to improving the efficiency of machine learning algorithms. This comparison study offers insightful information about which machine learning classification algorithms are most suited for maximizing smart agricultural techniques in the Marathwada area.