Precision Agriculture: A Machine Learning Approach to Crop Recommendation

Crop recommendation systems are essential in contemporary agriculture because they help farmers choose the best crops. The main aim of these systems is to advise the farmers which crop is best in a particular area based on soil characteristics, conditions of climate, and environmental factors. In this paper, a methodology for crop recommendation is proposed. Authors have assessed the effectiveness of four models developed using NB (Naive Bayes), RF (Random Forest), SVM (Support Vector Machine), and KNN (K-Nearest Neighbors) classifiers. Dataset acquired from Kaggle is used to evaluate the models. All four classifiers NB, SVM, RF, and KNN achieved remarkably high accuracy of 99.50%, 98.55%, 99.64% and 97.95% respectively. These results illustrate the effectiveness of machine learning in crop selection.