Department of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.
Department of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USA.
Poult Sci. 2023 Jul;102(7):102729. doi: 10.1016/j.psj.2023.102729. Epub 2023 Apr 20.
Floor egg-laying behavior (FELB) is one of the most concerning issues in commercial cage-free (CF) houses because floor eggs (i.e., mislaid eggs on the floor) result in high labor costs and food safety concerns. Farms with poor management may have up to 10% of daily floor eggs. Therefore, it is critical to improving floor eggs management. Detecting FELB timely and identifying the reason behind its cause may address the issue. The primary objectives of this research were to develop and test a new deep-learning model to detect FELB and evaluate the model's performance in 4 identical research CF houses (200 Hy-Line W-36 hens per house), where perches and litter floor were provided to mimic commercial tiered aviary system. Five different YOLOv5 models (i.e., YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) were trained and compared. According to a dataset of 5400 images (i.e., 3780 for training, 1080 for validation, and 540 for testing), YOLOv5m-FELB and YOLOv5x-FELB models were tested with higher precision (99.9%), recall (99.2%), mAP@0.50 (99.6%), and F1-score (99.6%) than others. However, the YOLOv5m-NFELB model has lower recall than other YOLOv5-NFELB models, although it was tested with higher precision. Similarly, the speed of data processing (4%-45% FPS), and training time (3%-148%) were higher in the YOLOv5s model while requiring less GPU (1.8-4.8 times) than in other models. Furthermore, the camera height of 0.5 m and clean camera outperform compared to 3 m height and dusty camera. Thus, the newly developed and trained YOLOv5s model will be further innovated. Future studies will be conducted to verify the performance of the model in commercial CF houses to detect FELB.
地面产卵行为（FELB）是商业笼养（CF）鸡舍中最令人关注的问题之一，因为地面蛋（即在地面上误产的蛋）会导致高劳动力成本和食品安全问题。管理不善的农场可能会有高达 10%的日地面蛋。因此，改善地面蛋管理至关重要。及时发现 FELB 并找出其原因可能有助于解决这个问题。本研究的主要目的是开发和测试一种新的深度学习模型来检测 FELB，并评估该模型在 4 个相同的研究 CF 鸡舍中的性能（每个鸡舍有 200 只海兰 W-36 母鸡），其中提供栖息处和垫料地板以模拟商业分层式禽类系统。训练和比较了五个不同的 YOLOv5 模型（即 YOLOv5n、YOLOv5s、YOLOv5m、YOLOv5l 和 YOLOv5x）。根据 5400 张图像的数据集（即 3780 张用于训练、1080 张用于验证和 540 张用于测试），YOLOv5m-FELB 和 YOLOv5x-FELB 模型的精度（99.9%）、召回率（99.2%）、mAP@0.50（99.6%）和 F1 分数（99.6%）都高于其他模型。然而，与其他 YOLOv5-NFELB 模型相比，YOLOv5m-NFELB 模型的召回率较低，尽管其精度较高。同样，YOLOv5s 模型的数据处理速度（4%-45% FPS）和训练时间（3%-148%）较高，而所需的 GPU 较少（1.8-4.8 倍），与其他模型相比。此外，与 3m 高和多尘相机相比，0.5m 高和清洁相机的性能更好。因此，将进一步改进新开发和训练的 YOLOv5s 模型。未来的研究将在商业 CF 鸡舍中进行，以验证该模型检测 FELB 的性能。
