Crop Prediction using Random Forest Algorithm

Abstract

Abstract—India's population depends heavily on agriculture. A lot of farmers in India follow their intuition when deciding which crop to plant in a particular season. The most common issue that farmers face is when they do not select the appropriate crop according to the needs of their property and its surroundings. Consequently, production is impacted. Researchers have been trying to agricultural production forecasting utilizing multiple approaches and methodologies and have comparative analysis on such algorithms here using decision tree, logistic regression and random forest classifier. Machine learning approaches, in particular Random Forest classifiers, have become beneficial tools for precise and reliable crop prediction. To create a predictive model, this study analyzes historical data on soil characteristics and climate trends. As a reliable foundation for crop prediction, the Random Forest method is used to effectively record complicated linkages and interactions among different data. The outcomes show the potential for crop prediction powered by machine learning to improve agricultural output, optimize resource allocation, and support ethical farming methods.