BENCHMARKING OF MACHINE LEARNING ALGORITHMS FOR FERTILIZER RECOMMENDATION IN PRECISION AGRICULTURE

Florin Daniel Militaru, Ramona Ciolac, Adrian Firu-Negoescu, Sebastian Moisa, Gabriela Popescu

Abstract


This paper benchmarks fourteen machine learning algorithms for fertilizer recommendation in precision agriculture using soil, crop, and environmental data. Ensemble-based models—particularly Gradient Boosting, Random Forest, and LightGBM—achieved the highest accuracy and robustness, effectively modeling nonlinear agronomic relationships. In contrast, linear and distance-based methods showed limited adaptability. The results confirm that ensemble learning offers the most reliable and efficient framework for data-driven fertilizer recommendation, contributing to sustainable and resource-efficient crop management.

Keywords


Machine Learning; Fertilizer Recommendation; Benchmarking; Precision Agriculture

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Copyright (c) 2026 Florin Daniel Militaru, Ramona Ciolac, Sebastian Moisa, Adrian Firu-Negoescu, Gabriela Popescu

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