Special Session 12

The slaughterhouse is widely recognised as a useful check-point for assessing the health status of livestock. The meat inspection is the responsibility of official veterinarians (OVs) and their team who are trained to perform these tasks: namely official auxiliaries (OAs), meat hygiene inspectors and/or the abattoir/the plant’s own staff. In this regard the new EU legislation paves the way for new technologies to be used for the post-mortem (PM) inspection of the carcass, which could enable the detection of zoonotic and non-zoonotic animal health hazards. Computer Vision Systems (CVS), which emulates the principle of human vision, opens to the development and implementation of CVS to be used extensively as an assisting tool in meat inspection. The automation of this process and the new technologies would allow an effective and systematic examination of all slaughtered livestock, positively influencing herd management, animal welfare and control over the wise use of antimicrobials.

Andrea Capobianco Dondona, CEO at Farm4trade is a veterinary epidemiologist with over 16 years of experience as a wildlife veterinarian both in the field and in a diagnostic laboratory. He currently works as Global Early Warning System Coordinator for FAO leading the activities on disease monitoring, analysis and forecasting to ensure early warning and early action for transboundary disease threats. Over the years Andrea gained considerable experience in the designing, development and management of animal health information systems and the application of new technologies, including artificial intelligence to the public health sector

Ercole Del Negro, CTO at Farm4trade is a PhD ICT Engineer with more than 13 years of experience in software development and data management. He is currently working as a Project Manager (PM) in several projects in African Countries related to the Animal Health and Food Security sectors. He has ongoing collaborations with international institutions (FAO, SADC, IAEA, etc.), Governments, and private sectors.

Angelo Porrello, obtained a Master's Degree in Computer Engineering in 2017 from the University of Modena and Reggio Emilia. He then pursued his Ph.D. in Information and Communication Technology during the three-year period 2019-2021; at the moment, he works as Research Fellow within the AImageLab Group at the Department of Engineering “Enzo Ferrari”. His research interests focus on Deep Learning techniques: more precisely on the fields of Continual Learning, Re-Identification, and Anomaly Detection.