The task “Big Data Analysis and Decision Support Systems for Climate Adaptation in Agricultural and Forestry Systems” arises from the need to develop innovative solutions aimed at improving the management and sustainability of agricultural and forestry activities in the context of climate change.
By leveraging advanced data from sensors, GNSS technologies, machine learning, and artificial intelligence, our research focuses on developing cutting-edge tools for:
Two flagship projects drive this scientific endeavor:
Automated classification of agricultural machinery operational states, using GNSS data alone, to enable reliable and cost-effective evaluation of fieldwork.
Development of advanced anomaly detection methodologies for tractors, through deep learning techniques and synthetic anomaly generation using artificial language models. This ensures predictive maintenance even when real-world data is scarce.
These innovative approaches support the adoption of precision agriculture practices and contribute to making agricultural and forestry systems more resilient and sustainable in the face of climate challenges.
