Modern Agriculture: A Review of Emerging Crop Yield Prediction Models Using Machine Learning

Accurately predicting crop yields is a very critical component of global food safety and agricultural efficiency. Such is a multifaceted approach toward crop yield prediction explored in the present study. Anchoring on the Green Revolution and subsequent impact on crop diversity and resilience, it reviews how far a wide range of yield prediction models has evolved in terms of effectiveness, including regression models, machine learning, and hybrid models. Such methods would require advanced machine learning techniques, statistical methods, and integration of data sources like satellite imagery and meteorological data to assure the accuracy of yield predictions. This study analyzes strengths and limitations of the models in order to provide a fuller understanding of the current situation and identify opportunities for technological advances and improved implementation strategies in crop yield forecasting.