Optimizing Crop Yields through Machine Learning-Based Prediction

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