Literature Review on Agricultural Yield Prediction and Soil Data Analysis Using Machine Learning

Abstract

This paper presents a comprehensive review of current research on agricultural yield prediction and soil data analysis, emphasizing the application of machine learning techniques. It discusses how soil characteristics, weather variables, and fertilizer management influence crop productivity. The review explores various machine learning models, including decision trees, random forests, support vector machines, and deep learning architectures, highlighting their effectiveness in improving yield forecasts and optimizing resource use. Additionally, the integration of Internet of Things (IoT) technologies with ML models for real-time monitoring and precision agriculture is examined. Key research gaps identified include the need for integrated fertilizer recommendation systems, real-time IoT data incorporation, regional specificity in models, and accessible farmer-centric decision support tools. Addressing these gaps offers significant potential to enhance agricultural sustainability, productivity, and economic viability.