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A machine learning-based positioning method for poultry in cage environments
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AI
DOI：10.1016/j.compag.2023.107764.png)
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摘要
En 中文
Individuals or groups of animals exhibit different activities that characterize domain behavior. Rapid and ac-curate localization of poultry in small and complex cage environments helps analyze the poultry domain behavior. This study proposes a machine-learning-based method for locating poultry in small and complex cage environments. Here, the characteristics of ultra-high frequency-radio frequency identification devices were determined, received signal strength indicator values were collected, and the tag-coordinate regression problem was converted into a multi-area classification problem. Different models were used to predict the target position. The results revealed that the neural network model yielded the best prediction, locating the target within a 40 cm x 40 cm area with 88.74% accuracy or within a 30 cm x 30 cm area with 76.81% accuracy, with average errors of 7.61 cm and 7.97 cm, respectively. Finally, experiments with live chickens were performed, and the results were verified using synchronized video, obtaining a Pearson correlation coefficient exceeding 0.909. This study presents a feasible method for target localization in small and complex cage environments, providing valuable modal information for multimodal learning.
Keyword:
Poultry localization
Ultrahigh-frequency radio frequency
identification
Cage environment
Machine learning range-based