Benchmarking of Machine Learning Algorithms for Fertilizer Recommendation in Precision Agriculture
Year: 2025
Type: article
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.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W7215396392