Hi! I'm Michele Croci 🌍

I am a researcher at the Department of Sustainable Crop Production at Università Cattolica del Sacro Cuore. 🎓 I graduated in Agricultural Sciences and Technologies from the same university, where I also earned my PhD at the AGRISYSTEM PhD School, focusing on the agri-food system. 🌱 My research mainly revolves around the analysis of remote sensing data 📡 for optimal crop and land management. 🚜

I specialize in leveraging cutting-edge technologies such as machine learning 🤖 and multispectral imagery 🌈 to develop advanced solutions for precision agriculture and sustainable resource management. My work addresses critical challenges in modern agriculture, including high-throughput phenotyping 📷 for crop improvement, dynamic yield prediction 📊 to enhance food security, and agrivoltaic system modeling ☀️🌾 to maximize land use efficiency. Currently, I am a postdoctoral researcher at the Remote Sensing and Spatial Analysis Research Center (CRAST) 🛰️ and a guest scientist at the Leibniz Centre for Agricultural Landscape Research (ZALF) in Germany. 🇩🇪

Over the years, I have contributed to several high-impact research initiatives:

• Mo.Re Farming 🚜: Focused on promoting site-specific management techniques to support sustainable farming practices.
• Nutrivigna 🍇: Aimed at improving nutrient efficiency and reducing the environmental footprint of vineyards through remote and proximal sensing technologies.
• ClieNFarms 🌍🚜: Developed solutions for climate-neutral farming to support the European Green Deal.
• GRACE BBI 🌾: Advanced the use of bioeconomy crops like miscanthus and hemp on marginal or contaminated lands.

Other key projects include Positive 💧, targeting precision irrigation through satellite and ground sensor data, and Agro.Big.Data.Science 🥬🍐🥝, where I worked on creating data-driven solutions for supply chain optimization in kiwi, pear, and spinach production.

My scientific contributions, reflected in numerous peer-reviewed publications, explore topics like early crop classification 🛰️, biostimulant evaluation for stress resilience 🌱, and the integration of machine learning for yield prediction 📡. I actively combine theoretical research with practical applications to deliver tangible benefits to farmers, agronomists, and stakeholders in the agricultural sector.

Beyond my research, I am proficient in programming languages such as R 🟢🟢🟢🟢⭕ and Python 🟢🟢⭕⭕⭕, as well as advanced GIS tools like QGIS and OrfeoToolBox. My work also relies on various operating systems, including Windows, macOS, and Linux. I am passionate about mentoring young researchers and sharing knowledge through teaching and collaborative projects.

I firmly believe that technological innovation and remote sensing will play a transformative role in the future of agriculture. By fostering sustainability, efficiency, and resilience, we can address pressing global challenges such as food security, resource management, and climate change. 🌍🌱🚜