Machine Learning Techniques for Crop Recommendation Systems Based on Productivity

Abstract

Crop productivity is a critical factor in ensuring food security and economic stability in the agricultural sector. Traditional methods of crop recommendations often rely on expert knowledge and historical data, which may not fully capture the complex relationships between various factors influencing crop productivity. In recent years, machine learning techniques have emerged as powerful tools for analyzing large-scale agricultural data and making accurate crop recommendations. This survey paper aims to provide a comprehensive review of the state-of-the-art machine learning algorithms and methodologies used for agriculture crop recommendations based on productivity. The paper begins with a discussion on the importance of crop recommendations and the challenges associated with traditional methods. It then delves into the various machine learning techniques employed in this domain, including regression models, support vector machines, and neural networks. The paper also explores the preprocessing steps required for handling agricultural data, such as feature selection and engineering.