This training session is part of the NISHATI seminar series on Active Learning and explores the growing role of machine learning in agriculture, with a specific focus on olive production systems. The presentation introduces how modern data-driven techniques can be applied to agricultural challenges, including crop monitoring, disease detection, and variety recognition.
The session discusses opportunities and risks associated with adopting machine learning technologies, highlighting how artificial intelligence can support decision-making and improve efficiency in agricultural practices. Through a practical case study from the olive sector, participants gain insight into how image-based data and automated recognition systems can help address challenges related to climate change, pests, and plant diseases.
This training is particularly relevant for academics, researchers, students, and practitioners interested in digital agriculture, artificial intelligence, and innovative approaches to sustainable food production.
Dr. Dorjan Hitaj is an Assistant Professor in the Computer Science Department at Sapienza University of Rome. He holds a PhD in Computer Science and his research focuses on the intersection of machine learning and cybersecurity, with particular attention to emerging risks and threats associated with new technologies.
In addition to cybersecurity, Dr. Hitaj applies artificial intelligence techniques to agriculture and related domains. He is currently involved in European research initiatives such as Gen4Olive, a Horizon 2020 project aimed at accelerating genetic resource mobilization and supporting pre-breeding activities to address challenges posed by climate change, pests, and diseases in olive production.
