A Review on Machine Learning Techniques for Crop Yield Prediction

Abstract: Crop yield prediction plays a crucial role in ensuring food security, efficient resource management, and sustainable agricultural planning. With the rapid advancement of artificial intelligence, machine learning (ML) techniques have emerged as powerful tools for predicting crop productivity using diverse datasets such as weather conditions, soil characteristics, and remote sensing data. This paper presents a comprehensive review of machine learning techniques applied to crop yield prediction. It analyzes commonly used algorithms, including linear regression, decision trees, random forests, support vector machines, and deep learning models such as artificial neural networks and convolutional neural networks. Studies show that environmental factors like temperature, rainfall, and soil type are the most significant features influencing prediction accuracy.

Keywords: Crop Yield Prediction, Machine Learning, Precision Agriculture, Remote Sensing, Predictive Modeling