Lin Hao-Ting
Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan.
Departement of International Doctoral Program in Agriculture, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan.
Sensors (Basel). 2025 Aug 13;25(16):5028. doi: 10.3390/s25165028.
With growing global attention on animal welfare and food safety, humane and efficient slaughtering methods in the poultry industry are in increasing demand. Traditional manual inspection methods for stunning broilers need significant expertise. Additionally, most studies on electrical stunning focus on white broilers, whose optimal stunning conditions are not suitable for red-feathered Taiwan chickens. This study aimed to implement a smart, safe, and humane slaughtering system designed to enhance animal welfare and integrate an IoT-enabled vision system into slaughter operations for red-feathered Taiwan chickens. The system enables real-time monitoring and smart management of the poultry stunning process using image technologies for dynamic object tracking recognition. Focusing on red-feathered Taiwan chickens, the system applies dynamic tracking objects with chicken morphology feature extraction based on the YOLO-v4 model to accurately identify stunned and unstunned chickens, ensuring compliance with animal welfare principles and improving the overall efficiency and hygiene of poultry processing. In this study, the dynamic tracking object recognition system comprises object morphology feature detection and motion prediction for red-feathered Taiwan chickens during the slaughtering process. Images are firsthand data from the slaughterhouse. To enhance model performance, image amplification techniques are integrated into the model training process. In parallel, the system architecture integrates IoT-enabled modules to support real-time monitoring, sensor-based classification, and cloud-compatible decisions based on collections of visual data. Prior to image amplification, the YOLO-v4 model achieved an average precision (AP) of 83% for identifying unstunned chickens and 96% for identifying stunned chickens. After image amplification, AP improved significantly to 89% and 99%, respectively. The model achieved and deployed a mean average precision (mAP) of 94% at an IoU threshold of 0.75 and processed images at 39 frames per second, demonstrating its suitability for IoT-enabled real-time dynamic tracking object recognition in a real slaughterhouse environment. Furthermore, the YOLO-v4 model for poultry slaughtering recognition in transient stability, as measured by training loss and validation loss, outperforms the YOLO-X model in this study. Overall, this smart slaughtering system represents a practical and scalable application of AI in the poultry industry.
随着全球对动物福利和食品安全的关注度不断提高，家禽行业对人道且高效的屠宰方法的需求日益增加。传统的肉鸡电击昏人工检查方法需要大量专业知识。此外，大多数关于电击昏的研究都集中在白羽肉鸡上，其最佳电击昏条件并不适用于红羽台湾鸡。本研究旨在实现一个智能、安全且人道的屠宰系统，以提高动物福利，并将物联网视觉系统集成到红羽台湾鸡的屠宰操作中。该系统利用图像技术进行动态目标跟踪识别，实现对家禽电击昏过程的实时监测和智能管理。针对红羽台湾鸡，该系统基于YOLO-v4模型应用具有鸡形态特征提取的动态跟踪目标，以准确识别已电击昏和未电击昏的鸡，确保符合动物福利原则，并提高家禽加工的整体效率和卫生水平。在本研究中，动态跟踪目标识别系统包括红羽台湾鸡在屠宰过程中的目标形态特征检测和运动预测。图像是来自屠宰场的第一手数据。为提高模型性能，图像增强技术被集成到模型训练过程中。同时，系统架构集成了物联网模块，以支持基于视觉数据收集的实时监测、基于传感器的分类以及与云兼容的决策。在图像增强之前，YOLO-v4模型识别未电击昏鸡的平均精度（AP）为83%，识别已电击昏鸡的平均精度为96%。图像增强后，AP分别显著提高到89%和99%。该模型在交并比（IoU）阈值为0.75时实现并部署了94%的平均精度均值（mAP），并以每秒39帧的速度处理图像，证明其适用于在实际屠宰场环境中进行基于物联网的实时动态跟踪目标识别。此外，通过训练损失和验证损失衡量，用于家禽屠宰识别的YOLO-v4模型在瞬态稳定性方面优于本研究中的YOLO-X模型。总体而言，这种智能屠宰系统代表了人工智能在家禽行业中的实际且可扩展的应用。
