MPB_2024v15n5

Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 237 Figure 2 Schematic of developed High-Throughput Phenotyping Platform (HTPP) with utilized devices (Adopted from Pour et al., 2021) Image caption: Utilized devices distances in the field; IRT, US and CC stand for Infra-Red Thermometer, Ultrasonic Sensor and Crop Circle, respectively (Adopted from Pour et al., 2021) 4.2 Use of HTP for real-time monitoring and prediction of disease outbreaks HTP technologies have enabled real-time monitoring and prediction of disease outbreaks in wheat fields. By continuously collecting phenotypic data through sensors and imaging systems, HTP platforms can detect early signs of disease and monitor their progression over time. This real-time data collection is critical for timely intervention and management of disease outbreaks. For instance, UAVs equipped with multispectral cameras can capture temporal changes in vegetation indices, which are indicative of plant health and stress levels. These indices can be used to identify areas of the field that are experiencing disease stress, allowing for targeted application of fungicides or other control measures (Haghighattalab et al., 2016; Adak et al., 2023). Additionally, the integration of HTP data with environmental and weather data can improve the prediction of disease outbreaks, enabling proactive management strategies. The ability to monitor disease progression in real-time also facilitates the study of disease dynamics and the identification of resistant genotypes. By analyzing temporal phenotypic data, researchers can identify genotypes that exhibit stable resistance across different environmental conditions and disease pressures. This information is invaluable for breeding programs aiming to develop wheat varieties with durable disease resistance (Shakoor et al., 2017; Smith et al., 2021). 4.3 Comparison between traditional phenotyping methods and HTP platforms for precision in large-scale breeding programs Traditional phenotyping methods in wheat breeding have relied heavily on manual measurements and visual assessments, which are time-consuming, labor-intensive, and often subjective. These methods are limited in their ability to accurately and consistently evaluate large breeding populations, particularly for complex traits such as disease resistance. In contrast, HTP platforms offer significant improvements in precision, efficiency, and scalability. HTP systems utilize advanced sensors and imaging technologies to collect high-resolution phenotypic data, enabling the accurate measurement of multiple traits simultaneously. For example, UAV-based HTP platforms can capture detailed images of entire field plots, allowing for the precise assessment of disease symptoms and plant health (Haghighattalab et al., 2016; Condorelli et al., 2019). Ground-based platforms equipped with various sensors can provide continuous, non-destructive measurements of plant traits, reducing the need for destructive sampling and increasing the throughput of phenotyping (Pour et al., 2021). The precision of HTP platforms is further enhanced by the use of advanced data processing and analysis techniques. Machine learning and computer vision algorithms can be applied to HTP data to automatically detect and quantify disease symptoms, reducing the potential for human error and bias (Shakoor et al., 2017; Singh et al., 2019). Additionally, the integration of HTP data with genomic information enables the identification of genetic

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