The proper spatial distribution of chickens is an indication of a healthy flock. Routine\ninspections of broiler chicken floor distribution are done manually in commercial grow-out houses\nevery day, which is labor intensive and time consuming. This task requires an efficient and automatic\nsystem that can monitor the chickenâ??s floor distributions. In the current study, a machine vision-based\nmethod was developed and tested in an experimental broiler house. For the new method to recognize\nbird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and\nrest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately\nto avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles\nwere extracted from images collected from 18 to 35 days of age to build a BP neural network model\nfor floor distribution analysis, and another 200 images were used to validate the model. The results\nshowed that the identification accuracies of bird distribution in the drinking and feeding zones were\n0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean\nabsolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of\nbroiler distribution. Missed detections were mainly caused by interference with the equipment (e.g.,\nthe feeder hanging chain and water line); studies are ongoing to address these issues. This study\nprovides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution\nand behavior in commercial facilities.
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