Abstract

This purpose of the review investigates the integration of machine learning (ML) techniques into crop
recommendation systems in the context of precision agriculture. The study addresses the research question: How
effective are ML-based systems in improving crop selection, productivity, and sustainability by leveraging
environmental, soil, and climatic factors. The Methods involved review synthesizes of recent literature on ML
applications in crop recommendation, focusing on both traditional and advanced methods. Techniques such as
Logistic Regression, Random Forest, Gradient Boosting, and XGBoost are evaluated. Additionally, the review explores
the use of data sources including soil health data to train and validate these models. Special emphasis is placed on
the emerging application of Ensemble Algorithms which model complex spatial and relational data. The Results
showing Ensemble models, particularly XGBoost, achieved exceptional predictive accuracy, with precision scores
exceeding 99.5%. Graph-based models effectively captured localized interactions and demonstrated improved
recommendation outcomes. Integrating diverse datasets was shown to enhance model robustness and
generalizability across different agricultural settings. The Conclusions that can be drawn from the study is that
ML-based crop recommendation systems show significant potential in promoting efficient resource utilization,
reducing environmental impacts, and increasing agricultural sustainability. With proper field validation and adaptive
monitoring, these systems can lead to transformative outcomes in food security and the economic resilience of
farming communities.