Research

1. AI-derived digital phenotypes for precision livestock farming

• Tang Z, Wang J, De Castro A, Zhang Y, Primo VB, Montevecchio Bernardino AB, Morota G, Wang X, Chebel RC, and Yu H. (2026). Can 3D point cloud data improve automated body condition score prediction in dairy cattle? arXiv. doi: 10.48550/arXiv.2601.22522.

• Wang J, De Castro A, Zhang Y, Basolli Borsatto L, Guo Y, Primo VB, Montevecchio Bernardino AB, Morota G, Chebel RC, and Yu H. (2026). Evaluating transfer learning strategies for improving dairy cattle body weight prediction in small farms using depth-image and point-cloud data. arXiv. doi: 10.48550/arXiv.2601.01044.

• Niño de Guzmán C, Pinedo P, Yu H, Bliznyuk N, and De Vries A. (2026). Estimation of probability of pregnancy based on health status and estrus intensity in organic dairy cows. Dairy. doi: 10.3390/dairy7040058.

• Wang J, Yu Z, Chebel RC, and Yu H. (2025). Impact of cross-validation designs on cattle behavior prediction using machine learning and deep learning models with tri-axial accelerometer data. Smart Agricultural Technology. doi: 10.1016/j.atech.2025.101483.

• Bi Y, Campos LM, Wang J, Yu H, Hanigan MD, and Morota G. (2023). Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms. Smart Agricultural Technology. 6:100352. doi: 10.1016/j.atech.2023.100352.

2. Integration of digital phenotypes and omics data for genetic improvement

• Anas M, Zhao B, Yu H, Dahlen C, Swanson KC, Ringwall KA, and Hanna LLH. (2026). Genome-wide association study of reproductive, body size, and carcass-related latent and directly measured traits in admixed beef heifers. Frontiers in Genetics. doi: 10.3389/fgene.2026.1653878.

• Bi Y, Huang Y, Yu H, and Morota G. (2026). Impact of trait measurement error on quantitative genetic analysis of computer vision derived traits. Genes. doi: 10.3390/genes17050506.

• Watson MT, Feldmann M, Yu H, and Cheng H. (2025). End-to-end genomic prediction: Direct prediction of images and text from genome-wide molecular markers. bioRxiv. doi: 10.1101/2025.11.03.686395.

• Anas M, Zhao B, Yu H, Dahlen C, Swanson KC, Ringwall KA, Hulsman Hanna LL. (2025). Multi-Trait Phenotypic Modeling Through Factor Analysis and Bayesian Network Learning to Develop Latent Reproductive, Body Conformational, and Carcass-Associated Traits in Admixed Beef Heifers. Frontiers in Genetics. doi: 10.3389/fgene.2025.1551967.

• Yan H, Jin Y, Yu H, Wang C, Wu B, Jone CS, Wang X, Xie Z, and Huang L. (2024). Genomic selection for agronomical phenotypes using genome-wide SNPs and SVs in pearl millet. Theoretical and Applied Genetics. doi: 10.1007/s00122-024-04754-2

• Yu H, Fernando RL, and Dekkers JCM. (2024). Use of the linear regression method to evaluate population accuracy of predictions from non-linear models. Frontiers in Genetics. doi: 10.3389/fgene.2024.1380643

• Yu, H, van Milgen, J, Knol, E. F, Fernando, R. L, and Dekkers, J. C. (2022). A bayesian hierarchical model to integrate a mechanistic growth model in genomic prediction. In: Proceedings, 12th World Congress of Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands. [PDF]

• Ni Z, Fernando RL, Yu H, Knol EF, and Dekkers JCM. (2022). Genomic prediction of longitudinal body weights in pigs using a neural network. In: Proceedings, 12th World Congress of Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands. [PDF]

• Pegolo S, Yu H, Morota G, Bisutti V, Rosa GJM, Bittante G, and Cecchinato A. (2021). Structural equation modelling for unravelling the multivariate genomic architecture of milk proteins in dairy cattle. Journal of Dairy Science. doi: 10.3168/jds.2020-18321

• Momen M, Bhatta M, Hussain W, Yu H, and Morota G. (2021). Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct. 00:e00304. doi: 10.1002/pld3.304

• Yu H, Morota G, Celestino EF, Dahlen CR, Wagner SA, Riley DG, and Hanna LLH. (2020). Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling. Frontiers in Genetics. 11:599. doi: 10.3389/fgene.2020.00599

• Yu H, Campbell MT, Zhang Q, Walia H, and Morota G. (2019). Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics. 9:1975-1986. doi: 10.1534/g3.119.400154

3. Open-source software development for animal omics sciences

• De Castro A, Wang J, Bonney-King JG, Morota G, Miller-Cushon EK, and Yu, H. (2025) AnimalMotionViz: an interactive software tool for tracking and visualizing animal motion patterns using computer vision. JDS Communications. In press. doi: 10.3168/jdsc.2024-0706

• Wang J, Hu Y, Xiang L, Morota G, Brooks SA, Wickens CL, Miller-Cushon EK, and Yu, H. (2024). Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision. Journal of Animal Science. doi: 10.1093/jas/skad416

• Dekkers JCM, Su L, Kramer L, and Yu H. (2022). A tool for the design of breeding programs using genomics. In: Proceedings, 12th World Congress of Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands. [PDF]

• Yu H and Morota G. (2021). GCA: An R package for genetic connectedness analysis using pedigree and genomic data. BMC Genomics. 22:119. doi: 10.1186/s12864-021-07414-7