Comparative Analysis of ML Models on Crop Yield Predictıon

India, a nation largely dependent on agriculture, relies on crop prediction to ensure food security and sustainable farming practices. The emergence of contemporary technology and the accessibility of vast amounts of agricultural data have led to the rise of machine learning models as effective instruments for crop forecasting. The goal of this research project is to find the best accurate and efficient machine crop forecast learning model in the Indian agricultural setting by performing a thorough comparative analysis of several models. The study will make use of a large and varied dataset that includes historical data on agricultural yields, weather patterns, soil characteristics, and socioeconomic variables in several Indian regions. Through the integration of data from many sources and the use of diverse machine learning techniques, the research aims to evaluate the predictive capacity of the models for major crops like rice, wheat, and cotton. The results of this study will help build reliable and regional crop prediction models that will help stakeholders, governments, and farmers in India make decisions about food security, resource allocation, and crop planning. It will also highlight how machine learning may transform Indian agriculture and open the door to more effective and sustainable farming methods.