Efficient auto-labeling of large-scale poultry datasets (ALPD) using an ensemble model with self- and active-learning approaches

Abstract

The rapid growth of artificial intelligence in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets. Manual annotation is time-consuming and costly, making it impractical for modern systems that continuously generate data. This study addresses this challenge by exploring semi-supervised auto-labeling methods, integrating self and active learning approaches to develop an efficient, label-scarce framework for auto-labeling large poultry datasets (ALPD). For this study, video data were collected from broilers and laying hens housed. Various machine learning models, including zero-shot models and supervised models, were utilized for broilers and hens detection. The results showed that YOLOv8s-World and YOLOv9s performed better when compared performance metrics for broiler and hen detection under supervised learning, while among the semi-supervised model, YOLOv8s-ALPD achieved the highest precision (96.1%) and recall (99%) with an RMSE of 1.87. The hybrid YOLO-World model, incorporating the optimal YOLOv8s backbone with zero-shot models, demonstrated the highest overall performance. It achieved a precision of 99.2%, recall of 99.4%, and an F1 score of 98.7% for detection. In addition, the semi-supervised models with minimal human intervention (active learning) reduced annotation time by over 80% compared to full manual labeling. Moreover, integrating zero-shot models with the best models enhanced broiler and hen detection, achieving comparable results to supervised models while significantly increasing speed. In conclusion, integrating semi-supervised auto-labeling and zero-shot models significantly improves detection accuracy. It reduces manual annotation efforts, offering a promising solution to optimize AI-driven systems in poultry farming, advancing precision livestock management, and promoting more sustainable practices.