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Student Project | Computer Science & Engineering | India | Volume 12 Issue 10, October 2023 | Rating: 5.9 / 10
Machine Learning Techniques for Agriculture Crop Recommendations Based on Productivity: A Survey
Savitha N [2] | Ravikumar K
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.
Keywords: machine learning, agriculture, crop recommendations, productivity, survey paper, data preprocessing, regression models, support vector machines, neural networks, feature selection, feature engineering, evaluation metrics, case studies, challenges, future research directions, sustainable agriculture
Edition: Volume 12 Issue 10, October 2023,
Pages: 111 - 116