Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upret?, Kamal; S?ngh, Jaspreet; Alapatt, Bosco Paul Title A name given to the resource Smart Farming with Ensemble Learning: A Soil-Driven Crop Suggestion Model for Sustainable Agriculture Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Turkish Journal of Engineering;Volume;9;Issue;4;pp.801-810 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.31127/tuje.1688064" target="_blank" rel="noreferrer noopener">https://doi.org/10.31127/tuje.1688064</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019248029?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019248029?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upret? K., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India; S?ngh J., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India; Alapatt B.P., Christ University, Department of Computer Science, Delhi NCR Campus, Ghaziabad, India Description An account of the resource The use of machine learning (ML) in agriculture has paved new avenues to improve decision making, especially in crop choice. The current research offers a data-driven crop recommendation system using a machine learning approach based on key soil and environmental factorsi.e., nitrogen (N), phosphorus (P), potassium (K), pH, temperature, humidity, and rainfall. A dataset of 2,200 soil records was processed using exploratory data analysis (EDA), normalization, and model training with algorithms such as Random Forest, Logistic Regression, and Gradient Boosting. Of these, Random Forest provided the best test accuracy of 99.32%, with high predictive ability and interpretability via feature importance measures. Violin and boxplots showed distinct feature separability among crop types, particularly in variables such as rainfall, temperature, and NPK concentrations, confirming the model's classification effectiveness. The practicability of the system is in its possible incorporation in IoT-based soil monitoring devices and cell advisory apps, delivering real-time, location-specific crop advice. This strategy enables farmers to make informed decisions, minimizes fertilizer waste, and promotes sustainable farming practices. The suggested system not only showcases technical strength but also fits well within the overall vision of smart farming and precision agriculture. Author(s) 2025. Subject The topic of the resource Agriculture; Agronomic Insights; Crop Recommendation; Machine Learning; Soil Characteristics Publisher An entity responsible for making the resource available Murat Yakar Relation A related resource ISSN: 25871366; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online