Monitoring digestate application on agricultural crops using Sentinel-2 Satellite imagery

2025-04-28

AI Summary

This study addresses environmental risks associated with agricultural application of digestate—including soil health degradation, microplastic contamination, and nitrogen leaching—by proposing a dynamic monitoring framework integrating Sentinel-2 time-series remote sensing and machine learning. We systematically construct the first crop-specific spectral response dataset (encompassing EOMI, NDVI, and EVI) following digestate application across four major crops: wheat, maize, sunflower, and sugar beet. A hybrid remote sensing–machine learning detection architecture is developed, incorporating Random Forest, k-Nearest Neighbors, Gradient Boosting, and Feedforward Neural Networks. Applied at scale in Thessaly, Greece, the method enables large-area, low-cost, and near-real-time identification of digestate presence, achieving a maximum F1-score of 0.85. This approach overcomes the spatial limitations and high operational costs of conventional field-based monitoring, establishing a novel paradigm for precision organic fertilizer management and intelligent environmental risk control.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Distributed Machine Learning & Federated Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Machine learning and data science for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satellite imagery for detecting digestate application, a practice that enhances soil fertility but poses environmental risks like microplastic contamination and nitrogen losses. In the first instance, Sentinel-2 satellite image time series (SITS) analysis of specific indices (EOMI, NDVI, EVI) was used to characterize EOM's spectral behavior after application on the soils of four different crop types in Thessaly, Greece. Furthermore, Machine Learning (ML) models (namely Random Forest, k-NN, Gradient Boosting and a Feed-Forward Neural Network), were used to investigate digestate presence detection, achieving F1-scores up to 0.85. The findings highlight the potential of combining remote sensing and ML for scalable and cost-effective monitoring of EOM applications, supporting precision agriculture and sustainability.

Problem

• Monitoring digestate application effects on soil and crops using Sentinel-2 imagery
• Detecting environmental risks like microplastic contamination from digestate use
• Combining remote sensing and ML for scalable EOM application monitoring

Innovation

• Sentinel-2 imagery monitors digestate application spectrally
• Machine Learning models detect digestate with high accuracy
• Combines remote sensing and ML for scalable monitoring