Fertilizer Management

Optimizing fertilizer management aims to maximize crop yields while minimizing environmental impact and economic costs. Current research focuses on developing precise, data-driven approaches, employing machine learning models like convolutional neural networks and transformers to analyze diverse data sources (e.g., drone imagery, soil sensors, weather data) for predicting optimal fertilizer application timing and amounts. These advancements leverage techniques such as representation learning and counterfactual analysis to improve the accuracy and explainability of fertilizer recommendations, ultimately leading to more sustainable and efficient agricultural practices.