This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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 body weight 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.
"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 housed together and observed from days 37–133 of age, nearly 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 influenced by its weight. Because there often were multiple birds in the camera's view, the AI had to learn that a group of pixels—the smallest single points or dots that make up a digital image on a screen—belonged to one turkey, while another group of pixels showed a different turkey. Distinguishing between the two is called instance segmentation, and it's different from simply recognizing "there is a turkey," Casella said.
The system needed to identify which individual pixels belonged to an individual animal, as opposed to pixels that belonged to the background or a different animal. To train and test the AI, the researchers manually weighed the turkeys five times per week. These actual weights served as references for the AI.
They employed a type of deep learning neural network called ResNet, commonly used for analyzing images. In this study, ResNet was trained to look at information from the turkey images and learn relationships between the birds' appearance, size and shape and actual body weights. The camera-AI system's prediction of future body weight was nearly as accurate at estimating the turkey's current weight.
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