This dissertation investigates the utility of deep learning and machine learning approaches for livestock management and quantitative genetic modeling of rice grain size under climate change. Monitoring the live body weight of animals is crucial to support farm management decisions due to its direct relationship with animal growth, nutritional status, and health. However, conventional manual weighing methods are time consuming and can cause potential stress to animals. While there is a growing trend towards the use of three-dimensional cameras coupled with computer vision techniques to predict animal body weight, their validation with deep learning models as well as large-scale data collected in commercial environments is still limited. Therefore, the first two research chapters show how deep learning-based computer vision systems can enable accurate live body weight prediction for dairy cattle and pigs. These studies also address the challenges of managing large, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy in an industry-scale commercial setting. The dissertation then shifts the focus to crop resilience, particularly in rice, where the asymmetric increase in average nighttime temperatures relative to the increase in average daytime temperatures due to climate change is reducing grain yield and quality in rice. Through the use of deep learning and machine learning models, the last two chapters explore how metabolic data can be used in quantitative genetic modeling in rice under environmental stress conditions such as high night temperatures. These studies showed that the integration of metabolites and genomics provided an improvement in the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Further research showed that metabolic accumulation was low to moderately heritable, and genomic prediction accuracies were consistent with expected genomic heritability estimates. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, this dissertation highlights the potential of integrating digital technologies and multi-omic data to advance data analytics in agriculture, with applications in livestock management and quantitative genetic modeling of rice. / Doctor of Philosophy / This dissertation explores the application of deep learning and machine learning to computer vision-based livestock management and quantitative genetic modeling of rice grain size under climate change. The first half of the research chapters illustrate how computer vision systems can enable digital phenotyping of dairy cows and pigs, which is critical for informed management decisions and quantitative genetic analysis. These studies address the challenges of managing large-scale, complex phenotypic data and highlight the potential of deep learning models to automate data processing and improve prediction accuracy. Chapter 3 showed that a deep learning-based segmentation, Mask R-CNN, improved the prediction performance of cow body weight from longitudinal depth video data. Among the image features, volume followed by width correlated best with body weight. Chapter 4 showed that efficient deep learning-based supervised learning models are a promising approach for predicting pig body weight from industry-scale depth video data. Although the sparse design, which simulates budget and time constraints by using a subset of the data for training, resulted in some performance loss compared to the full design, the Vision Transformer models effectively mitigated this loss. The second half of the research chapters focuses on integrating metabolomic and genomic data to predict grain traits and metabolic content in rice under climate change. Through the use of machine learning models, these studies investigate how combining genomic and metabolic data can improve predictions, particularly under high night temperature stress in rice. Chapter 5 showed that the integration of metabolites and genomics improved the prediction of rice grain size-related traits, and certain metabolites were identified as potential candidates for improving multi-trait genomic prediction. Chapter 6 showed that metabolic accumulation was low to moderately heritable. Genomic correlations between control and high night temperature conditions indicated genotype-by-environment interactions in metabolic accumulation, and the effectiveness of genomic prediction models for metabolic accumulation varied across metabolites. Joint analysis of multiple metabolites improved the accuracy of genomic prediction by exploiting correlations between metabolite accumulation. Overall, the dissertation provides insight into how cutting-edge methods can be used to improve livestock management and multi-omic quantitative genetic modeling for breeding.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/121267 |
| Date | 03 October 2024 |
| Creators | Bi, Ye |
| Contributors | Animal and Poultry Sciences, Morota, Gota, Santantonio, Nicholas, Hanigan, Mark Daniel, Lourentzou, Ismini |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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