Machine Learning-Based Optimization of Site-Specific Fertilizer Recommendation

This paper presents a machine learning (ML) approach for generating site-specific fertilizer recommendations that maximize crop yield and nutrient efficiency while minimizing environmental impact. Using a rich agronomic dataset from the Al Moutmir program in Morocco, various ML models are trained (linear, tree-based, ensemble, and neural networks) to predict crop yield responses to nitrogen (N), phosphorus (P), and potassium (K) inputs under diverse soil and climate conditions. The best predictive model (an XGBoost regressor) achieved a Mean Absolute Percentage Error (MAPE) of ~8.9\%, substantially outperforming baseline approaches. The predictive model is then integrated with optimization algorithms (including Simulated Annealing and Particle Swarm Optimization) to identify the optimal N, P, K levels for each site. Simulated application of these recommendations indicates an average yield improvement of about 544 kg/ha over current practices, along with more efficient fertilizer use and low environmental impact. The importance of key features is analyzed in the recommendations, and an open analysis of the approach limitations is provided. All results are validated with statistical tests for significance. The proposed framework demonstrates how advanced ML and optimization techniques can enhance precision agriculture by tailoring fertilizer strategies to local needs.