APP4FARM, a project for the sustainable use of resources in agriculture

Efficient nitrogen management remains a major challenge in modern agriculture due to the low nitrogen use efficiency (NUE) of cropping systems and the associated environmental impacts of reactive nitrogen emissions. APP4FARM proposes an integrated cyber-physical framework combining distributed gas sensing, soil microbial functional analysis, satellite data, and artificial intelligence to enable continuous monitoring and short-term forecasting of nitrogen-related dynamics in agricultural systems. The system integrates low-cost metal oxide (MOX) gas sensors with reference photonic sensors, in-field meteorological measurements, Copernicus satellite products, and soil molecular characterization based on quantitative PCR targeting nitrogen cycling functional genes. Biological validation in High Yield (HY) and Low Yield (LY) zones demonstrated significant differences in nitrification and denitrification gene abundance, supporting mechanistic interpretation of nitrogen transformation processes. Artificial intelligence models, including ARX, ANN, CNN, and Hybrid CNN (HCNN), were implemented for pointwise and spatial forecasting. In the German pilot, pointwise models achieved correlation coefficients up to 0.9967 for soil-related variables. For satellite-based prediction, HCNN models reached correlations above 0.95 for Soil Water Index at a 3-day forecast horizon, while ANN models provided robust predictions of NDVI and NDMI. The results demonstrate the feasibility of integrating multi-source sensing and AI-based forecasting within a Decision Support System capable of supporting environmentally sustainable fertilizer management. APP4FARM represents a scalable approach for linking real-time emission monitoring, biological soil processes, and predictive analytics to improve nitrogen efficiency and reduce greenhouse gas emissions in agroecosystems.