A Systematic Literature Review: Crop Recommendation Using Machine Learning Algorithms

This paper provides a thorough examination and comparative assessment of approaches designed to improve crop recommendation and agricultural production in India. By analyzing multiple research publications, we investigate a wide range of methodologies, including machine learning-based models and precision agriculture techniques. The evaluation examines the accomplishments and constraints of each approach, providing insight into crucial issues such as precision, scalability, suitability, and hurdles in implementation. The results highlight the need of taking into account environmental, soil, and climatic factors in crop recommendation systems. They also stress the necessity of conducting field tests and ongoing monitoring to assure the long-term sustainability of these systems. In addition, the review examines the consequences of relying on technology, worries about the accuracy of data, and the impact of socioeconomic factors on the success and broad use of these approaches. In summary, this comparative analysis is a significant tool for researchers, policymakers, and stakeholders who are interested in tackling the issues that India’s agricultural industry is currently facing and enhancing crop output in a sustainable manner.