Tracking Poultry Drinking Behavior and Floor Eggs in Cage-Free Houses with Innovative Depth Anything Model

Le résumé fourni par la source

In recent years, artificial intelligence (AI) has significantly impacted agricultural operations, particularly with the development of deep learning models for animal monitoring and farming automation. This study focuses on evaluating the Depth Anything Model (DAM), a cutting-edge monocular depth estimation model, for its potential in poultry farming. DAM leverages a vast dataset of over 62 million images to predict depth using only RGB images, eliminating the need for costly depth sensors. In this study, we assess DAM’s ability to monitor poultry behavior, specifically detecting drinking patterns. We also evaluate its effectiveness in managing operations, such as tracking floor eggs. Additionally, we evaluate DAM’s accuracy in detecting disparity within cage-free facilities. The accuracy of the model in estimating physical depth was assessed using root mean square error (RMSE) between predicted and actual perch frame depths, yielding an RMSE of 0.11 m, demonstrating high precision. DAM demonstrated 92.3% accuracy in detecting drinking behavior and achieved an 11% reduction in motion time during egg collection by optimizing the robot’s route using cluster-based planning. These findings highlight DAM’s potential as a valuable tool in poultry science, reducing costs while improving the precision of behavioral analysis and farm management tasks.

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