Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 238 loci associated with disease resistance, facilitating marker-assisted selection and genomic prediction in breeding programs (Condorelli et al., 2019; Adak et al., 2023). HTP platforms provide a more accurate, efficient, and scalable approach to phenotyping in large-scale wheat breeding programs. By enabling the rapid and precise evaluation of disease resistance, HTP technologies accelerate the development of resistant varieties and improve the overall efficiency of breeding efforts (Tolley et al., 2020; Crain et al., 2021; Danilevicz et al., 2021). 5 Challenges of High-throughput Phenotype Analysis in Wheat Disease Resistance 5.1 Technological limitations in sensor accuracy, data processing, and image resolution High-throughput phenotyping (HTP) in wheat disease resistance faces significant technological challenges, particularly in sensor accuracy, data processing, and image resolution. The precision of sensors is crucial for capturing accurate phenotypic data, yet many current systems struggle with this aspect. For instance, a multi-sensor system developed for field phenotyping in wheat and soybean demonstrated the potential of using various sensors, including ultrasonic distance sensors, thermal infrared radiometers, and NDVI sensors, to measure crop canopy traits. However, the accuracy and resolution of these sensors can be limiting factors, affecting the reliability of the collected data (Figure 3) (Bai et al., 2016). Figure 3 The average canopy reflectance spectra of the soybean plots derived from the up-looking and down-looking portable spectrometers on different dates during the season (Adopted from Bai et al., 2016) Image caption: The solid vertical lines denote the wavelengths to calculate NDVI (630 and 800 nm) and the dashed vertical lines denote the wavelengths to calculate red-edge NDVI (705 and 750 nm) (Adopted from Bai et al., 2016) Moreover, the integration of multiple sensors and the synchronization of their data streams require sophisticated data processing capabilities. The development of a LabVIEW program to control and synchronize measurements from all sensor modules highlights the complexity involved in managing these systems. Additionally, image resolution is a critical factor, as high-resolution images are necessary to capture detailed phenotypic traits. The use of RGB cameras and the extraction of canopy green pixel fraction as a proxy for biomass illustrate the importance of high-resolution imaging in phenotyping (Bai et al., 2016). 5.2 Data management issues: handling large-scale phenotypic datasets and integrating them with genotypic data The management of large-scale phenotypic datasets poses another significant challenge in HTP. The sheer volume of data generated by high-throughput systems can be overwhelming, necessitating robust data storage, processing, and analysis infrastructure. For example, the use of image-based HTP platforms to monitor phenotypic variation in crops generates vast amounts of data that need to be efficiently managed and shared among researchers (Danilevicz et al., 2021). Integrating phenotypic data with genotypic data adds another layer of complexity. The ability to correlate phenotypic traits with genetic markers is essential for advancing our understanding of disease resistance in wheat. However, this requires sophisticated data integration techniques and computational tools. The development of
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