Machine Learning-Based Dual Agricultural Decision Support System: An Integrated Approach for Crop and Fertilizer Recommendations
Year: 2026
Type: article
Abstract: This study proposes a comprehensive dual-module agricultural decision support system that integrates machine learning algorithms for both crop and fertilizer recommendations. The system addresses critical challenges in modern agriculture by providing evidence-based recommendations to optimize productivity while maintaining sustainability. Four machine learning algorithms (Random Forest, Support Vector Machine, XGBoost, and K-Nearest Neighbors) were implemented and evaluated using comprehensive agricultural datasets containing 2,200 crop samples and 3,100 fertilizer samples. The crop recommendation module achieved 99.32% accuracy using Random Forest, while the fertilizer recommendation module attained 98.75% accuracy. The system incorporates advanced techniques including SMOTE for handling class imbalance, GridSearchCV for hyperparameter optimization, and SHAP analysis for model interpretability. Comparative analysis with existing literature demonstrates competitive performance while offering enhanced explainability and dual functionality. The developed framework provides a foundation for region-specific implementations and represents a practical contribution to intelligent agricultural decision support systems.
Authors: Hatice Bülbül, Enes Güler, Burcu Çarklı Yavuz
Institution: Sakarya University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $25
OpenAlex ID: W7122487428