Cameras and AI used to monitor turkey weights in Penn State research

By Jeff Mulhollem, Penn State

UNIVERSITY PARK, Pa. — Body weight monitoring is one of the most important tools for poultry farmers. It provides valuable information about growth, uniformity, feed conversion efficiency — how well birds turn feed into meat — and the occurrence of diseases in a flock. But obtaining frequent and accurate body weight measurements is a significant challenge in commercial poultry production, according to researchers at Penn State who may have developed a solution. They recently found that a camera paired with artificial intelligence (AI) could monitor individual turkeys and make reasonably accurate bodyweight predictions up to three weeks into the future.

The team, who published their findings in Frontiers of Animal Science, said computer vision and AI can potentially be used not only to estimate a turkey’s current body weight, but also to predict its future body weight with approximately 93% accuracy.

The researchers used a camera positioned above the birds to capture two types of information: normal color images and depth images. Depth images, like the one here, reveal more details about the three-dimensional shape and size of the birds — information that indicate their weights. Credit: Penn State. Creative Commons

“Traditional approaches to individual body weight monitoring require extensive manual labor and frequent animal handling, creating both economic and animal welfare concerns,” said study senior author Enrico Casella, assistant professor of data science for animal systems in Penn State’s College of Agricultural Sciences. “In the poultry industry, weight information is essential for maximizing the value of each individual animal, as the average weight of a flock determines equipment settings for processing operations.”

The study, conducted at the Penn State Poultry Education and Research Center, involved 30 male turkeys that were housed together and observed them from day 37 to day 133 of age, almost 14 weeks of growth monitoring. The researchers used a camera positioned above the birds to capture two types of information: normal color images and depth images, which provided information about how far different parts of the turkey were from the camera. This information revealed more details about the three-dimensional shape and size of the bird, which are informed by its weight.

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