Automated non-laying hen identification via deep learning: A phenotypic characteristics attribution analysis framework - Wenxiang Qin; Xiao Yang; Liyang Yu; Yujia Chen; Junjie Wang; Yan Zhou; Weichao Zheng (Smart Agricultural Technology)
The widespread occurrence of non-laying hens in commercial poultry flocks significantly affects the management and efficiency of egg production. The current identification of non-laying hens in commercial layer operations relies predominantly on manual empirical methods involving visual inspection and physical touch. These labor-intensive and time-consuming processes not only lack standardized protocols for objective assessment but also induce physiological stress in poultry populations, thereby contradicting contemporary animal welfare standards. This study proposes an intelligent detection framework for non-laying hens by leveraging a proprietary dataset comprising 977 images of chickens that capture multi-perspective views and behavioral patterns. We systematically compared the feature extraction capabilities of five CNN architectures (ResNet50, ResNeXt50, ResNeXt101, EfficientNet, and ConvNeXt) for non-laying hen identification, analyzed the performance enhancement effects of channel attention and self-attention mechanisms, employed Grad-CAM visualizations to interpret deep feature representations, systematically investigated the contribution of distinct regions to the recognition results, and ultimately established an objective classification framework for non-laying hens. Experimental results demonstrated that ConvNeXt achieved superior classification performance for non-laying hens, with accuracy, precision, recall, and F1 scores of 94.81 %, 93.48 %, 97.37 %, and 95.16 %, respectively. The integration of squeeze-and-excitation (SE) attention mechanisms further enhanced ConvNeXt's classification metrics with accuracy, precision, recall, and F1 scores of 1.29 %, 7.12 %, 2.37 %, and 4.68 %. Although Vision Transformers (ViTs) and Swin Transformers exhibit constrained performances due to dataset scale limitations, they demonstrate potential recognition capabilities. Heatmap visualization analysis revealed that the head features contributed most significantly to identification, followed sequentially by the feet, tail, wings, abdomen, and back regions, suggesting that feather distribution patterns and phenotypic characteristics in these areas may serve as critical biomarkers for non-laying hen classification.