Machine Learning Techniques for Crop Yield Forecasting

A machine learning-based methodology for precisely forecasting crop yields is presented in this research, providing farmers with beneficial knowledge for improving their farming methods. To accurately anticipate crop yields, the model uses Decision Trees, Random Forest, and Gradient Boosting algorithms to analyze historical weather data, soil quality, and crop-specific factors. Farmers can utilize the system's real-time integration into agricultural monitoring platforms to get timely information that enables them to make appropriate choices about resource allocation, planting, and harvesting. This forecasting method assists in improving resistance to climate change and other agricultural difficulties, as well as production and food security.