Billah M, Bermann M, Hollifield M K, Tsuruta S, Chen C Y, Psota E, Holl J, Misztal I, Lourenco D
University of Georgia, Department of Animal and Dairy Science, Athens, GA 30602, USA.
Pig Improvement Company, Hendersonville, TN 37075, USA.
Animal. 2025 Mar 17:101486. doi: 10.1016/j.animal.2025.101486.
Promoting sustainable breeding programs requires several measures, including genomic selection and continuous data recording. Digital phenotyping uses images, videos, and sensor data to continuously monitor animal activity and behaviors, such as feeding, walking, and distress, while also measuring production traits like average daily gain, loin depth, and backfat thickness. Coupled with machine learning techniques, any feature of interest can be extracted and used as phenotypes in genomic prediction models. It can also help define novel phenotypes that are hard or expensive for humans to measure. For the already recorded traits, it may add extra precision or lower phenotyping costs. One example is lameness in pigs, where digital phenotyping has allowed moving from a categorical scoring system to a continuous phenotypic scale, resulting in increased heritability and greater selection potential. Additionally, digital phenotyping offers an effective approach for generating large datasets on difficult-to-measure behavioral traits at any given time, enabling the quantification and understanding of their relationships with production traits, which may be recorded at a less frequent basis. One example is the strong, negative genetic correlation between distance traveled and average daily gain in pigs. Conversely, despite improvements in computer vision, phenotype accuracy may not be maximized for some production or carcass traits. In this review, we discuss various image processing techniques to prepare the data for the genomic evaluation models, followed by a brief description of object detection and segmentation methodology, including model selection and objective-specific modifications to the state-of-the-art models. Then, we present real-life applications of digital phenotyping for various species, and finally, we provide further challenges. Overall, digital phenotyping is a promising tool to increase the rates of genetic gain, promote sustainable genomic selection, and lower phenotyping costs. We foresee a massive inclusion of digital phenotypes into breeding programs, making it the primary phenotyping tool.
推广可持续育种计划需要采取多项措施，包括基因组选择和持续的数据记录。数字表型分析利用图像、视频和传感器数据来持续监测动物的活动和行为，如进食、行走和痛苦状况，同时还能测量平均日增重、腰部深度和背膘厚度等生产性状。结合机器学习技术，可以提取任何感兴趣的特征并将其用作基因组预测模型中的表型。它还有助于定义人类难以测量或测量成本高昂的新表型。对于已经记录的性状，它可能会提高精度或降低表型分析成本。一个例子是猪的跛足情况，数字表型分析使得从分类评分系统转变为连续的表型尺度，从而提高了遗传力并增加了选择潜力。此外，数字表型分析提供了一种有效的方法，可在任何给定时间生成关于难以测量的行为性状的大型数据集，从而能够量化并理解它们与生产性状之间的关系，而生产性状的记录频率可能较低。一个例子是猪的行走距离与平均日增重之间存在强烈的负遗传相关性。相反，尽管计算机视觉有所改进，但对于某些生产或胴体性状，表型准确性可能无法达到最大值。在本综述中，我们讨论了各种图像处理技术，以便为基因组评估模型准备数据，随后简要描述了目标检测和分割方法，包括模型选择以及对现有先进模型的特定目标修改。然后，我们展示了数字表型分析在各种物种中的实际应用，最后，我们提出了进一步的挑战。总体而言，数字表型分析是一个有前景的工具，可提高遗传进展速度、促进可持续的基因组选择并降低表型分析成本。我们预计数字表型将大量纳入育种计划，使其成为主要的表型分析工具。
