Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 239 automated image analysis to measure quantitative resistance to septoria tritici blotch (STB) in wheat and the association of these phenotypes with SNP markers in a genome-wide association study (GWAS) exemplify the challenges and potential of integrating phenotypic and genotypic data (Yates et al., 2019). 5.3 Overcoming variability in environmental conditions that can affect phenotypic measurements Environmental variability is a major challenge in HTP, as phenotypic traits can be significantly influenced by changing environmental conditions. This variability can introduce noise into the data, making it difficult to accurately assess disease resistance. For instance, early plant vigor in winter wheat was found to be affected by genotypic differences, but environmental conditions also played a role, highlighting the need for methods that can account for such variability (Kipp et al., 2014). To address this, some HTP systems incorporate environmental sensors to collect simultaneous environmental data, which can then be used to normalize phenotypic measurements. The integration of solar radiation sensors and air temperature/relative humidity sensors into a multi-sensor system for field phenotyping is an example of how environmental data can be collected alongside phenotypic data to improve the accuracy of measurements (Bai et al., 2016). Additionally, the use of deep learning and machine learning techniques can help mitigate the impact of environmental variability by identifying patterns and correlations that may not be immediately apparent. For example, the application of deep learning on proximal imaging to score plant morphology and developmental stages in wheat demonstrated high accuracy and heritability, suggesting that advanced computational methods can enhance the robustness of phenotypic assessments under variable environmental conditions (Wang et al., 2019). While high-throughput phenotyping offers significant potential for advancing wheat disease resistance research, it is not without its challenges. Technological limitations in sensor accuracy, data processing, and image resolution, data management issues, and the need to account for environmental variability all present significant hurdles. Addressing these challenges will require continued innovation in sensor technology, data integration techniques, and computational methods to fully realize the potential of HTP in crop breeding and disease resistance research. 6 Combining Genomic Selection with High-Throughput Phenotyping 6.1 The role of genomic-enabled prediction models in accelerating wheat breeding for disease resistance Genomic selection (GS) has revolutionized plant breeding by enabling the prediction of breeding values using DNA polymorphisms, thus allowing for the selection of superior genotypes without the need for extensive phenotypic evaluations. This approach is particularly beneficial for complex traits such as disease resistance, which are often controlled by multiple genes with small effects. High-throughput phenotyping (HTP) complements GS by providing rapid, non-destructive measurements of phenotypic traits across large populations, thereby enhancing the accuracy and efficiency of selection processes. The integration of HTP with GS models has shown promise in improving the predictive ability of these models. For instance, the use of secondary traits measured through HTP, such as canopy temperature and vegetation indices, has been demonstrated to increase the accuracy of genomic prediction models for grain yield in wheat by up to 70% (Rutkoski et al., 2016). This integration allows for the assessment of traits that are difficult to measure directly, thereby providing a more comprehensive understanding of the genotype-to-phenotype relationship. 6.2 Case studies integrating HTP with genomic selection to enhance prediction accuracy and breeding outcomes Several case studies have highlighted the successful integration of HTP with GS to enhance prediction accuracy and breeding outcomes. For example, a study on wheat breeding demonstrated that the use of UAV-based phenotyping for traits such as lodging could significantly improve the accuracy of genomic predictions. The study found high correlations between digital measures of lodging and visual estimates, with genome-wide association analysis identifying key genomic regions associated with lodging resistance (Singh et al., 2019).
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