Intelligent Monitoring of Individual Nest-building Behavior in Group-raised Geese

Abstract

Breeding geese often exhibit frequent nest-building behaviors under group rearing conditions, which significantly reduces egg-laying efficiency. However, it remains challenging to accurately identify the occurrence and duration of individual nest-building in such environments. To address this, this study integrates wearable activity monitoring sensors with RFID identification technology to develop a nest-building behavior characterization system based on activity rhythms and posture features. Building on this foundation, we propose an Integrated Feature Engineering-Driven Stacking Recognition Model (IFESM-GND) that enhances nest-building behavior recognition accuracy by leveraging interpretable feature engineering. Experimental results demonstrate a 93.5% detection accuracy, representing a 15%-21% improvement over traditional methods. The application value of nest-building behavior monitoring in early egg-laying anomaly detection and breeding management decision-making is validated. This precise monitoring method for individual nest-building behaviors in breeding geese provides crucial technical support for optimizing reproductive performance, improving egg-laying efficiency, and achieving precision management. The research holds significant theoretical and practical implications for advancing the intelligent and modernization of China's goose industry.