Development and validation of machine-learning models for monitoring individual behaviors in group-housed broiler chickens.

ABSTRACT: Animals' individual behavior is commonly monitored by live or video observation by a person. This can be labor intensive and inconsistent. An alternative is the use of machine learning-based computer vision systems. The objectives of this study were to 1) develop and optimize machine learning model frameworks for detecting, tracking and classifying individual behaviors of group-housed broiler chickens in continuous video recordings; and 2) use an independent dataset to evaluate the performance of the developed machine leaning model framework for individual poultry behaviors differentiation. Forty-two video recordings from 4 different pens (total video duration = 1,620 min) were used to develop and train multiple models for detecting and tracking individual birds and classifying 4 behaviors

SUBMITTER: Merenda VR 

PROVIDER: S-EPMC11536003 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature