Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 240 Another study focused on the use of HTP in early selection cycles of wheat breeding. The researchers employed UAVs to assess grain yield in different plot designs and found that aerial-based sensing provided higher precision and stronger correlations with grain yield compared to ground-based spectral sensing. This approach enabled the efficient selection of improved genotypes in early breeding stages, thereby accelerating the breeding process (Rutkoski et al., 2016). In maize, the integration of field-based HTP with genomic data revealed time-dependent associations between genotypes and abiotic stresses, such as heat stress during flowering. This study demonstrated that combining phenomic and genomic data could significantly improve the prediction ability for complex traits like flowering times and plant height, thereby enhancing the understanding of plant-environment interactions (Adak et al., 2023). 6.3 Future trends in wheat disease resistance research using multi-environmental experiments The future of wheat disease resistance research lies in the integration of multi-environmental experiments (METs) with advanced genomic and phenomic tools. METs allow for the evaluation of genotypic performance across diverse environmental conditions, providing insights into genotype-by-environment interactions and the stability of disease resistance traits. The use of HTP in METs can facilitate the rapid collection of phenotypic data across different environments, thereby improving the accuracy of genomic predictions and enabling the selection of genotypes with broad-spectrum disease resistance. One promising trend is the use of envirotyping, which involves the characterization of environmental conditions at various spatial and temporal scales. By combining envirotyping with HTP and GS, researchers can better account for environmental variability and improve the estimation of genotypic performance across different environments (Smith et al., 2021). This approach can help identify genotypes that are resilient to a range of abiotic and biotic stresses, thereby enhancing the overall robustness of wheat breeding programs. Additionally, the development of machine learning and deep learning algorithms for analyzing HTP data is expected to further advance the field. These algorithms can handle the high dimensionality and complexity of phenotypic data, enabling more accurate predictions of complex traits and the identification of novel genetic associations (Cabrera-Bosquet et al., 2012). As sensor technologies and data analytics continue to evolve, the integration of HTP with GS will become increasingly sophisticated, paving the way for more efficient and effective breeding strategies. The combination of genomic selection with high-throughput phenotyping holds great potential for accelerating wheat breeding for disease resistance. By leveraging the strengths of both approaches, researchers can enhance the accuracy of predictions, improve the efficiency of selection processes, and ultimately develop more resilient wheat varieties. Future research should focus on the integration of multi-environmental experiments, envirotyping, and advanced data analytics to fully exploit the potential of these technologies in wheat breeding programs. 7 Innovative Wheat Disease Resistance Screening Method Using High-Throughput Phenotype Analysis 7.1 Novel image-based disease quantification approaches High-throughput phenotyping (HTP) has revolutionized the way we quantify disease resistance in wheat by leveraging advanced imaging technologies. One of the most promising approaches involves the use of multispectral and hyperspectral imaging to capture detailed information about plant health and disease status. For instance, multispectral imaging has been successfully used to monitor barley resistance against powdery mildew by capturing reflectance data at various wavelengths, which helps in differentiating between susceptible and resistant genotypes (Kuska et al., 2018). This method allows for the early detection of disease symptoms, which is crucial for timely intervention and breeding decisions. In another study, a vehicle-based multispectral active sensor was employed to score early plant vigor in winter wheat, demonstrating the feasibility of using spectral indices to reflect plant health accurately (Kipp et al., 2014). This approach not only enhances the speed and accuracy of phenotyping but also reduces the labor and cost
RkJQdWJsaXNoZXIy MjQ4ODYzMg==