Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 243 In conclusion, the future of HTP in wheat breeding is promising, with potential advancements in sensor technologies, AI, and robotics poised to enhance the accuracy and scalability of phenotyping systems. International collaborations will be instrumental in driving these advancements, fostering the exchange of knowledge and resources, and promoting the adoption of HTP technologies worldwide. By working together, the global research community can accelerate the development of disease-resistant wheat varieties, contributing to food security and sustainable agriculture. 9 Concluding Remarks High-throughput phenotyping (HTP) has significantly advanced wheat disease resistance research by enabling the rapid and precise measurement of phenotypic traits across large populations. This technology has facilitated the identification of disease-resistant genotypes by providing detailed and objective data on plant responses to various stressors, including pathogens and environmental conditions. For instance, automated HTP systems have been developed to assess traits such as green leaf area and green normalized difference vegetation index, which are indicative of disease resistance and stress tolerance. Additionally, drone-based HTP has proven effective in quantifying complex traits like lodging, which impacts yield and quality, thereby enhancing the accuracy and efficiency of phenotyping in large breeding nurseries. These advancements underscore the transformative role of HTP in accelerating the breeding of disease-resistant wheat varieties. HTP holds immense potential to revolutionize future wheat breeding efforts by integrating advanced imaging technologies, machine learning, and genomic selection. The ability to non-destructively measure a wide range of traits, from morphological to physiological, allows for the comprehensive evaluation of genotypes under various environmental conditions. This integration can significantly enhance the selection process for traits associated with disease resistance, drought tolerance, and overall yield stability. For example, the use of spectral indices and remote sensing technologies in HTP enables the precise monitoring of plant health and stress responses, which are critical for developing resilient wheat varieties. Moreover, the scalability of HTP systems, such as unmanned aerial systems (UAS), allows for the high-throughput assessment of thousands of plots, making it feasible to conduct large-scale genetic studies and improve breeding efficiency. To fully realize the potential of HTP in wheat breeding, continued research and development are essential. Future efforts should focus on enhancing the accuracy and affordability of HTP systems to make them accessible to a broader range of breeding programs. This includes the development of more sophisticated image processing algorithms and machine learning models to better analyze phenotypic data and predict complex traits. Additionally, there is a need for standardized protocols and data-sharing platforms to facilitate the integration and comparison of HTP data across different studies and environments. Collaborative efforts between researchers, breeders, and technology developers will be crucial in advancing HTP technologies and ensuring their effective application in breeding programs. By addressing these challenges, HTP can continue to drive innovations in wheat breeding, ultimately leading to the development of more resilient and high-yielding wheat varieties. Acknowledgments The authors extend sincere thanks to two anonymous peer reviewers for their feedback on the manuscript. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Adak A., Kang M., Anderson S., Murray S., Jarquín D., Wong R., and Katzfuss M., 2023, Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data, Journal of Experimental Botany, 74(17): 5307-5326. https://doi.org/10.1093/jxb/erad216
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