Regulation of Meat Duck Activeness through Photoperiod Based on Deep Learning

Simple Summary In commercial duck farming, the photoperiod is a crucial farming parameter that can influence physiological and health indicators. Among these indicators, duck activeness, which is directly affected by the photoperiod, is one of the most intuitive and currently a hot research topic in the field of animal welfare. However, there are existing limitations in the calculation methods for activeness, which often focus on relationships between adjacent frames, neglecting medium- to long-term features and sometimes lacking intuitiveness. Additionally, the relationship between duck activeness and the photoperiod has not been clearly defined, making it challenging to provide practical guidance for production. This study introduces a duck activeness estimation method based on machine vision technology. It involves tracking the movement of group-raised ducks over a 6 min period. The average displacement of all ducks within each frame is used as an indicator to measure activeness, resulting in more accurate results. This method is applied to assess duck activeness under different photoperiods, and the experimental results are analyzed to determine the most suitable lighting cycle for duck farming, demonstrating the superiority of the model. The proposed method and the conclusions regarding the optimal photoperiod provide both a methodology and data support to enhance duck breeding and farming efficiency.Abstract The regulation of duck physiology and behavior through the photoperiod holds significant importance for enhancing poultry farming efficiency. To clarify the impact of the photoperiod on group-raised duck activeness and quantify duck activeness, this study proposes a method that employs a multi-object tracking model to calculate group-raised duck activeness. Then, duck farming experiments were designed with varying photoperiods as gradients to assess this impact. The constructed multi-object tracking model for group-raised ducks was based on YOLOv8. The C2f-Faster-EMA module, which combines C2f-Faster with the EMA attention mechanism, was used to improve the object recognition performance of YOLOv8. Furthermore, an analysis of the tracking performance of Bot-SORT, ByteTrack, and DeepSORT algorithms on small-sized duck targets was conducted. Building upon this foundation, the duck instances in the images were segmented to calculate the distance traveled by individual ducks, while the centroid of the duck mask was used in place of the mask regression box's center point. The single-frame average displacement of group-raised ducks was utilized as an intuitive indicator of their activeness. Farming experiments were conducted with varying photoperiods (24L:0D, 16L:8D, and 12L:12D), and the constructed model was used to calculate the activeness of group-raised ducks. The results demonstrated that the YOLOv8x-C2f-Faster-EMA model achieved an object recognition accuracy (mAP@50-95) of 97.9%. The improved YOLOv8 + Bot-SORT model achieved a multi-object tracking accuracy of 85.1%. When the photoperiod was set to 12L:12D, duck activeness was slightly lower than that of the commercial farming's 24L:0D lighting scheme, but duck performance was better. The methods and conclusions presented in this study can provide theoretical support for the welfare assessment of meat duck farming and photoperiod regulation strategies in farming.