Review: Genomic selection in the era of phenotyping based on digital images

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.