Yang Xiao, Chai Lilong, Bist Ramesh Bahadur, Subedi Sachin, Wu Zihao
Department of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.
Department of Computer Science, Franklin College of Arts and Sciences, University of Georgia, Athens, GA 30602, USA.
Animals (Basel). 2022 Aug 5;12(15):1983. doi: 10.3390/ani12151983.
Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition. However, such identification becomes more challenging under cage-free conditions comparing to caged hens. In this study, we developed a deep learning model (YOLOv5x-hens) based on YOLOv5, an advanced convolutional neural network (CNN), to monitor hens’ behaviors in cage-free facilities. More than 1000 images were used to train the model and an additional 200 images were adopted to test it. One-way ANOVA and Tukey HSD analyses were conducted using JMP software (JMP Pro 16 for Mac, SAS Institute, Cary, North Caronia) to determine whether there are significant differences between the predicted number of hens and the actual number of hens under various situations (i.e., age, light intensity, and observational angles). The difference was considered significant at p < 0.05. Our results show that the evaluation metrics (Precision, Recall, F1 and mAP@0.5) of the YOLOv5x-hens model were 0.96, 0.96, 0.96 and 0.95, respectively, in detecting hens on the litter floor. The newly developed YOLOv5x-hens was tested with stable performances in detecting birds under different lighting intensities, angles, and ages over 8 weeks (i.e., birds were 8−16 weeks old). For instance, the model was tested with 95% accuracy after the birds were 8 weeks old. However, younger chicks such as one-week old birds were harder to be tracked (e.g., only 25% accuracy) due to interferences of equipment such as feeders, drink lines, and perches. According to further data analysis, the model performed efficiently in real-time detection with an overall accuracy more than 95%, which is the key step for the tracking of individual birds for evaluation of production and welfare. However, there are some limitations of the current version of the model. Error detections came from highly overlapped stock, uneven light intensity, and images occluded by equipment (i.e., drinking line and feeder). Future research is needed to address those issues for a higher detection. The current study established a novel CNN deep learning model in research cage-free facilities for the detection of hens, which provides a technical basis for developing a machine vision system for tracking individual birds for evaluation of the animals’ behaviors and welfare status in commercial cage-free houses.
基于图像识别的精准家禽养殖，其基石在于对鸡（如蛋鸡和肉鸡）进行实时自动检测。然而，与笼养母鸡相比，在散养条件下进行此类识别更具挑战性。在本研究中，我们基于先进的卷积神经网络（CNN）YOLOv5开发了一种深度学习模型（YOLOv5x-hens），用于监测散养设施中母鸡的行为。使用了1000多张图像来训练该模型，并另外采用200张图像对其进行测试。使用JMP软件（适用于Mac的JMP Pro 16，SAS Institute，北卡罗来纳州卡里）进行单因素方差分析和Tukey HSD分析，以确定在各种情况下（即年龄、光照强度和观察角度）预测的母鸡数量与实际母鸡数量之间是否存在显著差异。当p < 0.05时，差异被认为具有统计学意义。我们的结果表明，YOLOv5x-hens模型在检测垫料地面上的母鸡时，其评估指标（精确率、召回率、F1和mAP@0.5）分别为0.96、0.96、0.96和0.95。新开发的YOLOv5x-hens在检测8周龄以上（即8至16周龄）不同光照强度、角度和年龄的鸡时，表现出稳定的性能。例如，在鸡8周龄后对该模型进行测试，准确率为95%。然而，由于喂食器、饮水线和栖木等设备的干扰，1周龄等较年轻的雏鸡更难被追踪（例如，准确率仅为百分之二十五）。根据进一步的数据分析，该模型在实时检测中表现高效，总体准确率超过95%，这是跟踪个体鸡以评估生产和福利的关键步骤。然而，该模型的当前版本存在一些局限性。错误检测来自高度重叠的鸡群、不均匀的光照强度以及被设备（即饮水线和喂食器）遮挡的图像。未来需要开展研究以解决这些问题，从而实现更高的检测率。本研究在散养研究设施中建立了一种用于检测母鸡的新型CNN深度学习模型，为开发机器视觉系统提供了技术基础，该系统用于跟踪个体鸡，以评估商业散养舍中动物的行为和福利状况。
