Bery Shubham, Brown-Brandl Tami M, Jones Bradley T, Rohrer Gary A, Sharma Sudhendu Raj
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
Genetics and Breeding Research Unit, USDA-ARS U.S. Meat Animal Research Center, Clay Center, NE 68933, USA.
Animals (Basel). 2023 Dec 29;14(1):131. doi: 10.3390/ani14010131.
Shoulder sores predominantly arise in breeding sows and often result in untimely culling. Reported prevalence rates vary significantly, spanning between 5% and 50% depending upon the type of crate flooring inside a farm, the animal's body condition, or an existing injury that causes lameness. These lesions represent not only a welfare concern but also have an economic impact due to the labor needed for treatment and medication. The objective of this study was to evaluate the use of computer vision techniques in detecting and determining the size of shoulder lesions. A Microsoft Kinect V2 camera captured the top-down depth and RGB images of sows in farrowing crates. The RGB images were collected at a resolution of 1920 × 1080. To ensure the best view of the lesions, images were selected with sows lying on their right and left sides with all legs extended. A total of 824 RGB images from 70 sows with lesions at various stages of development were identified and annotated. Three deep learning-based object detection models, YOLOv5, YOLOv8, and Faster-RCNN, pre-trained with the COCO and ImageNet datasets, were implemented to localize the lesion area. YOLOv5 was the best predictor as it was able to detect lesions with an mAP@0.5 of 0.92. To estimate the lesion area, lesion pixel segmentation was carried out on the localized region using traditional image processing techniques like Otsu's binarization and adaptive thresholding alongside DL-based segmentation models based on U-Net architecture. In conclusion, this study demonstrates the potential of computer vision techniques in effectively detecting and assessing the size of shoulder lesions in breeding sows, providing a promising avenue for improving sow welfare and reducing economic losses.
肩部溃疡主要出现在繁殖母猪身上，常常导致过早淘汰。报告的患病率差异很大，根据猪场产仔栏内地板类型、动物身体状况或导致跛行的现有损伤情况，患病率在5%至50%之间。这些损伤不仅关乎动物福利，而且由于治疗和用药所需的劳动力，还会产生经济影响。本研究的目的是评估计算机视觉技术在检测和确定肩部损伤大小方面的应用。一台微软Kinect V2相机拍摄了产仔栏内母猪的自上而下的深度图像和RGB图像。RGB图像以1920×1080的分辨率采集。为确保能最佳地观察损伤情况，选择了母猪左右侧卧且四肢伸展的图像。总共识别并标注了来自70头处于不同发育阶段损伤母猪的824张RGB图像。采用在COCO和ImageNet数据集上预训练的三种基于深度学习的目标检测模型YOLOv5、YOLOv8和Faster-RCNN来定位损伤区域。YOLOv5是最佳预测器，因为它能够以0.92的mAP@0.5检测到损伤。为估计损伤面积，使用大津二值化和自适应阈值处理等传统图像处理技术以及基于U-Net架构的基于深度学习的分割模型，对定位区域进行损伤像素分割。总之，本研究证明了计算机视觉技术在有效检测和评估繁殖母猪肩部损伤大小方面的潜力，为改善母猪福利和减少经济损失提供了一条有前景的途径。
