A cage-level egg production monitoring framework for quality inspection, weighing, and tracing

Caged-layer farming is the dominant mode of automated layer production, making precise production monitoring essential. However, current methods rely on detached, single-task models that cause computational redundancy and lack data synergy. Furthermore, these isolated systems struggle with dynamic occlusion on conveyors and fail to provide the granular traceability required for individual cage management. To address these challenges, this study proposes an integrated monitoring framework that synergizes instance segmentation, machine learning, and spatiotemporal tracking. First, the Egg-Seg model was developed by incorporating Multi-Scale Convolutional Attention (MSCA) modules and Dice Loss to precisely segment irregular defects and egg boundaries. Second, the Egg-Weight model was established using Standard Ellipse Fitting and LightGBM to mitigate geometric distortions and optimize mass estimation on moving conveyors. Finally, the Egg-Trace method combined conveyor motion analysis with ByteTrack, implementing a “Time-Cage” mapping logic for counting and traceability. Experimental results demonstrate that Egg-Seg achieved an mAP50–95 of 93.5% for multi-class targets. The Egg-Weight model attained an R 2 of 0.966 and an MAE of 0.83 g, validating robustness of the proposed feature engineering. Moreover, the system achieved 100% Accuracy of Egg Counting (AEC), 99.16% Accuracy of Egg Traceback (AET), and 97.32% Accuracy of Egg Production in Cage (AEPC), without mechanical resetting. This framework successfully digitizes multi-dimensional production metrics (quality, weight, count, location) at the individual cage level, providing important technical support for the advancement of precision livestock farming.