Abstract written by 2023 REU student Jelena Herriott.

Abstract

Ractopamine hydrochloride is a dietary supplement that increases growth rate when added to feed. The downside to using this supplement is an increase in agonistic behavior in pigs, and now an alternative supplement needs to be identified. The goal for this project is to develop an activity index model that can distinguish between various pig activity levels. This activity index will be developed by training a model that identifies low, medium, and high pig activity from image frames extracted from video. This Pig Activity Index Model (PAIM) is being developed to aid in solving the problem of efficiency of workers on farms, pig health, and the monitoring pig outcomes, such as during trials for new feed supplements. Pig activity can be used to determine the health status of pigs based on the change of activity levels, and the outcomes of this project will be used in future health detection tools.

This version of a PAIM utilized a learning model to recognize image differences to represent overall activity level. The development of the PAIM model used over 300 frames of groups of pigs representing a variety of behaviors and postures in six different pens of the same pig barn. Frames were categorized based on low, medium, and high activity level. Low pig activity is defined as lying and/or sitting. Medium pig activity is defined as maintenance behavior including: urinating, defecating, or feeding. High activity level is defined as exploring the pen, socializing, or agonistic behavior

Abstract written by 2023 REU student Scotteria Scott.

Alternative Title: Using Computer Vision Tools to Track Animal Movements

Abstract

Many studies have shown that using Computer Vision (CV) tools can help strength animal research through deep learning. CV plays a significant role in food production, animal welfare and their behavior.  The current goal of the study is to analyze an animal’s location and correlate it with their behavior to be able to predict an animal’s movement and activity. Within this project a Standard Operating Procedure (SOP) was created to document the steps of animal tracking and detection. The SOP breaks down the following detection models: YOLOV4, Label Studio, Detectron2, and Deep Simple Online Realtime Tracking (SORT). The detection models created an algorithm using the bounding boxes to create x and y coordinates tracking the animal. Using this method is important because it can contribute to detecting problems in animals before they happen and help animal scientist create solutions and solve them proactively. Applying their location and pairing it with their behavior can provide opportunities to further computer vision work in animal management. In the end of this study progress would have been made to correlate an animal’s location with their behavior and make farm life much easier.

Abstract written by 2023 REU student Emma Fuentes.

Alternative Title: Relevant Behavior and Posture for Emerging Respiratory Infection in Pigs

Abstract

Behavioral analysis could be utilized for the early detection of respiratory illness in sows. When executed with labor reducing computer technology, this could enable early treatment which could mitigate the economic and animal welfare costs of illnesses such as porcine reproductive and respiratory syndrome virus (PRRS), which burdens American breeding herds by an estimated $117.71 per breeding female and $302.6 million annually in additional costs and lost revenue (Holtkamp et al., 2012). The Behavioral Observation Research Interactive Software (BORIS (Friard & Gamba, 2016) was used to analyze behavioral footage of 10 sows subjected to a Lipopolysaccharide (LPS) immune challenge, which resulted in 3 mortalities. Postmortem evaluation revealed evidence of pre-existing subclinical respiratory conditions. A behavior ethogram, which consisted of 26 behavior labels, with paired postures, behaviors, and animal identifications was developed and applied between 10:30a.m. and 11:00a.m. shortly after the 2nd injections. This selected time frame served as a preliminary sample for anticipated further analysis between 10:30a.m. and 11:30a.m on the day prior to the trial and on the days of the 1st and 2nd injections. Behavior observations were continuously recorded for the selected time frame.

The goal of the behavior analysis of this study was to detect pigs with subclinical respiratory infections. Information about an animal’s health status gleaned from behavioral analysis could lead to achieving a more generous timeline for management intervention. Additionally, the methods of this experiment are supportive of an optimistic future for eventually training computer vision models for animal behavior analysis, which would relieve the pork industry of strain from labor shortages.