Optimising crop recommendations: A comparative analysis of machine learning models in Indian precision agriculture

Agriculture is the backbone of global sustenance, but drastic climate changes have rendered traditional agricultural practices unsustainable, which makes it important to optimise farming strategies. In this pursuit, a comprehensive comparative analysis of predictive models tailored for precision agriculture is presented. This study undertakes a rigorous comparison of various machine learning algorithms such as Support Vector Classifier (SVC), Logistic Regression, Random Forest, K-Nearest Neighbours (K-NN), and Linear Discriminant Analysis (LDA). The main objective is to identify the most effective model for recommending optimal crops based on soil and climatic parameters. These findings reinforce the substantial potential of machine learning in furnishing precise predictions regarding optimal crop recommendation. The outcomes of this study hold significant implications for the sustainable future of Indian agriculture, offering a pathway to empower farmers with precise recommendations and boosting the resilience and productivity of farming systems.