Tami Brown-Brandl
Professor
Dr. William E. and Eleanor L. Splinter Chair
Biological Systems Engineering
Worldwide, engineering for the maintenance and wellbeing of livestock is referred to as precision livestock farming, but here in the U.S., this emerging area is called precision animal management. Dr. Tami Brown-Brandl uses technology in animal environments to create data streams for each individual animal to better manage livestock. In analyzing the data this technology produces, Tami and her team can determine issues such as illnesses before a human would ever notice. This data emerges from two major types of instrumentation.
Tami can find out and predict several aspects involving livestock without having to be physically present and active with the animal by using image processing and depth image processing in connection with radio frequency identification (RFID). She has several different research projects she is focused on to help livestock producers manage their growing responsibilities. With this new depth image camera technology, producers have been able to more than double their efficiency.
Depth image cameras allow researchers to see a digital image and a depth image within the area it’s placed. By placing this camera within an animal pin, Tami can eliminate everything except the animal and tell the heights and weight of each individual. Height can be determined by looking at the distance from the camera to every pixel in the picture. With pigs, for example, Tami can remove from the image the head and tail and sum all of the columns of height across. Tami then analyzes this data and finds the volume of the pig and relates it to weight. Her accuracy on weight prediction is down to a 4.6% error, which equates to about 5 pounds or less. She can also predict the width and length of the animals using other depth image camera modeling. Without this technology, producers spend days weighing each animal. This process is time consuming for producers who must weigh an animal several times over its lifespan. These depth imaging cameras allow producers to place a camera out in the pen and capture weights daily.
Tami can also capture other important information using an RFID tag. Placing an RFID tag in a pig’s ear allows Tami to track each pig daily. This tag is passive, meaning there is no battery in it. The tag absorbs the energy and shoots back a number via antennas that she and her team can then analyze. Tami uses these antennas within feeders to tell when the animal comes, how long they spend eating, which antenna they eat by, how many animals are at the feeder with them, and what time of day they ate. With all of that information, she can then start looking at individual animal’s time spent eating. Tami then collects several days’ worth of data to predict the animal's eating pattern, for example saying they should spend 45 minutes eating each day. If an animal were to spend two standard deviations less than this time, Tami puts up a “red flag” showing something might be wrong with the animal. These red flags help producers identify which animals they need to be looking for and taking extra care of. Without this technology, producers aren’t able to spot these red flags as quickly. Animals may take up to four days of not feeling well before they show signs of sickness. Producers can take better can of their animals and spot issues quicker than usual with this technology.
Tami also teaches the senior design class where she enjoys helping seniors with their projects. She reminds students of all they’ve learned over the years and encourages them to think about and use information from previous classes, encouraging them to see the breadth of their knowledge. Tami is also working with other professors to develop a class for precision animal management. This class includes areas of Tami’s current research such as image processing, modeling of data sets, and information on the thermal environment.
Article written by Emi Lesser