Optimizing Agricultural Productivity: Developing a Smart Crop Recommendation System Using Machine Learning

Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preprocessing techniques, grid search optimization and the pipeline class are applied in conjunction with the different machine learning algorithm for the crop recommendation. The accuracy and efficiency of the recommendation process enhancement is aimed by applying techniques including data standardization, relative density normalization, and grid search optimization. Leveraging Machine Learning algorithms within a unified pipeline framework allows for systematic evaluation and comparison of models, ultimately providing farmers with reliable recommendations for crop selection. Machine Learning algorithms Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector Machine, Logistic Regression and Naïve Bayes were applied to predict and optimize agricultural productivity based on the dataset collected from Nagaland state. Among these algorithms, Random Forest outperformed the others, achieving an impressive accuracy of 99.54% in predicting agricultural productivity.