Monitoring Activity for Recognition of Illness in Experimentally Infected Weaned Piglets Using Received Signal Strength Indication ZigBee-based Wireless Acceleration Sensor

A critical review on experimental Streptococcus suis infection in pigs with a focus on clinical monitoring and refinement strategies

Streptococcus suis (S. suis) is a major pig pathogen worldwide with zoonotic potential. Though different research groups have contributed to a better understanding of the pathogenesis of S. suis infections in recent years, there are still numerous neglected research topics requiring animal infection trials. Of note, animal experiments are crucial to develop a cross-protective vaccine which is highly needed in the field. Due to the severe clinical signs associated with S. suis pathologies such as meningitis and arthritis, implementation of refinement is very important to reduce pain and distress of experimentally infected pigs. This review highlights the great diversity of clinical signs and courses of disease after experimental S. suis pig infections. We review clinical read out parameters and refinement strategies in experimental S. suis pig infections published between 2000 and 2021. Currently, substantial differences exist in describing clinical monitoring and humane endpoints. Most of the reviewed studies set the body temperature threshold of fever as high as 40.5°C. Monitoring intervals vary mainly between daily, twice a day and three times a day. Only a few studies apply scoring systems. Published scoring systems are inconsistent in their inclusion of parameters such as body temperature, feeding behavior, and respiratory signs. Locomotion and central nervous system signs are more common clinical scoring parameters in different studies by various research groups. As the heterogenicity in clinical monitoring limits the comparability between studies we hope to initiate a discussion with this review leading to an agreement on clinical read out parameters and monitoring intervals among S. suis research groups.

Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming

The size of the pork market is increasing globally to meet the demand for animal protein, resulting in greater farm size for swine and creating a great challenge to swine farmers and industry owners in monitoring the farm activities and the health and behavior of the herd of swine. In addition, the growth of swine production is resulting in a changing climate pattern along with the environment, animal welfare, and human health issues, such as antimicrobial resistance, zoonosis, etc. The profit of swine farms depends on the optimum growth and good health of swine, while modern farming practices can ensure healthy swine production. To solve these issues, a future strategy should be considered with information and communication technology (ICT)-based smart swine farming, considering auto-identification, remote monitoring, feeding behavior, animal rights/welfare, zoonotic diseases, nutrition and food quality, labor management, farm operations, etc., with a view to improving meat production from the swine industry. Presently, swine farming is not only focused on the development of infrastructure but is also occupied with the application of technological knowledge for designing feeding programs, monitoring health and welfare, and the reproduction of the herd. ICT-based smart technologies, including smart ear tags, smart sensors, the Internet of Things (IoT), deep learning, big data, and robotics systems, can take part directly in the operation of farm activities, and have been proven to be effective tools for collecting, processing, and analyzing data from farms. In this review, which considers the beneficial role of smart technologies in swine farming, we suggest that smart technologies should be applied in the swine industry. Thus, the future swine industry should be automated, considering sustainability and productivity.

Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods.