Special Session 6

Precision Livestock Farming (PLF) exploits a set of electronic sensors, devices, cameras, and many IoT devices for the efficient management of livestock. It involves measurement sensors, automated monitoring systems, wireless sensor networks, infrared and video camera, edge-based, and cloudbased artificial intelligence applications. The goal is the improvement of animals’ health, welfare, production/reproduction, and their impact on the environment. PLF also involves the monitoring of each individual animal by using wearable sensors to enable systems to get data from individual animals such as temperature and activity patterns that can be used in the identification of illness, heat stress, oestrous, etc. Major innovations in the PLF are related to the introduction of automated systems for managing livestock and data analysis to support farmers day by day in making decisions. Some examples are: Automatic milking systems; Automatic feeders; Activity Collars; Inline Milk Sensors; Automated Weight Detection Cameras; Methane and Emission Monitoring systems; Weather Stations, etc.

This Special Session brings together all the innovative ideas and technologies about precision livestock technologies to manage health and welfare and their benefits in terms of production.

Given the context of zootechnical problems, topics for this special session include, but are not limited to:

• Contactless measurements: Infrared Thermography, photogrammetry, 3D scanning, proximal and remote sensing;
• Computer Vision, Machine Learning and other Artificial Intelligence and Data Science methodologies;
• Internet of Things and Edge Computing;
• Methane and Emission Monitoring systems;
• Automatic milking system;
• Wearable Sensors Network;
• Automatic feeder;
• Inline Milk Sensors;
• Temperature-Humidity-Index monitoring;
• LoRa, LoWAN-based technologies;
• Instrumentation and Measurements for Smart Farm.

Flora Amato
received the Ph.D. degree in computer engineering from the University of Naples Federico II, Naples, Italy.
She is currently an Assistant Professor and is with CINI Research Consortium. She has authored more than 150 papers, published on international journals and conference proceedings. Her research interests include formal modeling, knowledge management, and cloud computing.
Dr. Amato coordinated many national and international research projects. She is in the committee of many international conferences and she was a Editor of International Journals.

Francesco Bonavolontà
received the Ph.D. degree from the Department of Electrical and Information Technologies, University of Naples Federico II, Naples, Italy, in 2015. He is currently a Research Fellow with the Department of Electrical and Information Technologies, University of Naples Federico II. He has received the national license as an Associate Professor of the scientific area 09/E4 Measurements. His research activity is centered in the area of instrumentation and measurement and can be divided into three main areas: remote control of measurement instruments, measurement methods based on compressive sampling, and distributed measurement systems for monitoring and protecting electrical networks. More recently, his research activities are focusing on the development of innovative measurement sensors based on artificial intelligence algorithms. He has founded the Spin off ARCADIA, for the realization of AR environments for remote control of measurement instruments.
Dr. Bonavolontà is a member of the Technical Committee TC-37— Measurements and Networking of IEEE.

Maria Teresa Verde
is a veterinarian with several years of experience in managing Buffalo farms. She is currently a Ph.D. student at DIETI and her research activity is mainly focused on the development of innovative measurement methods and technologies for veterinary medicine and precision breeding. In particular, she is involved in the development of new sensors based on edge artificial intelligence for early detection of subclinical mastitis using infrared thermography, methane emission estimation, body conditioning score monitoring, and analysis of data collected from automatic milking systems.