Machine Learning Based Crop Yield Prediction on Geographical and Climatic Data

Agriculture is essential to life on a fundamental level, and machine learning (ML) presents a noteworthy method for solving problems related to crop productivity. Results from current techniques, such as manual counting, climate-smart pest control, and satellite photography, are frequently erroneous. This research uses a variety of machine learning approaches to forecast agricultural productivity. Random Forest, Naïve Bayes, and Logistic Regression are the classifiers used; Random Forest has the best accuracy. By taking into account variables like temperature, rainfall, and area, these machine learning forecasts help farmers choose which crops to produce for the best yield, closing the gap between technology and the agricultural industry.