Agriculture plays a vital role in India’s economy, and accurate crop yield prediction is essential for ensuring food security, efficient resource allocation, and policy formulation. Traditional prediction methods often fail to account for the complex interactions between climatic, soil, and management factors. This paper presents a comprehensive survey on the use of machine learning (ML) techniques for predicting Indian crop yield. Various supervised and unsupervised learning algorithms—such as Linear Regression, Random Forest, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks (ANN)—have been analyzed for their performance in forecasting yield across diverse agro-climatic regions. The survey highlights the importance of feature selection, data preprocessing, and the integration of remote sensing and weather data for improving prediction accuracy. Additionally, it discusses challenges such as data inconsistency, regional variability, and model generalization. The study concludes that machine learning-based models offer a promising approach for building intelligent, data-driven agricultural systems that support sustainable farming and policy planning in India.
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