Optimizing Crop Yields through Machine Learning-Based Prediction

Abstract

The application of machine learning techniques in agriculture, particularly in harvest forecasting, is gaining traction as a means of addressing this issue. The major project, "Optimizing Crop Yields through Machine Learning-Based Prediction," takes a comprehensive approach to this issue by considering a variety of parameters, including temperature, humidity, rainfall, and soil nutrient levels, to Figure out which crop is best to grow in those conditions. Naive Bayes, Random Forest, Support Vector Machines, Decision Trees, K-Nearest Neighbours, and Bagging, as well as feature selection methods like Synthetic Minority Oversampling Technique, Majority Weighted Minority Oversampling Technique, Random Over-Sampling Examples, and Recursive Feature Elimination, are used to accomplish this. High precision rates and improved forecast outcomes are the goals of these methods. Using machine learning techniques in crop forecasts, farmers can gain useful insights and make decisions based on data that increase crop production and overall agricultural productivity. This work demonstrates the potential of machine learning to address issues in agriculture and influence the sector's future.