Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 234 2 Technological Advances in High-Throughput Phenotyping 2.1 Recent developments in sensor technologies Recent advancements in sensor technologies have significantly enhanced the capabilities of high-throughput phenotyping (HTP) in agriculture, particularly for disease resistance in wheat. Unmanned Aerial Vehicles (UAVs) equipped with various sensors have become a cornerstone of modern phenotyping. These sensors include regular RGB cameras, multispectral imaging cameras, hyperspectral imaging cameras, thermal imaging sensors, and LiDAR sensors. UAVs can collect high-resolution remote sensing data over large field trials, enabling the non-destructive estimation of plant traits such as yield, biomass, height, and leaf area index (Xie and Yang, 2020; Feng et al., 2021). The use of UAVs offers several advantages over traditional ground-based methods. UAVs can cover larger areas more quickly and provide higher resolution images compared to satellite-based techniques. This increased throughput and frequency of data collection are crucial for identifying crops with high yield and strong stress resistance, including disease resistance (Xie and Yang, 2020). Additionally, UAV-based phenotyping has shown higher precision in assessing traits like grain yield in wheat, making it a valuable tool for early selection cycles in breeding programs (Hu et al., 2020). 2.2 Application of automated image analysis and machine learning in phenotyping The integration of automated image analysis and machine learning (ML) techniques has revolutionized HTP by enabling the efficient processing and analysis of vast amounts of phenotypic data. Digital Image Processing (DIP) and ML methods can analyze images captured by UAVs to extract valuable information about plant traits. These technologies minimize the time and cost associated with traditional phenotyping methods, making it feasible to analyze entire crops quickly and accurately (Nogueira et al., 2023). Automated image analysis involves the use of computer vision techniques to process images and extract phenotypic traits. Machine learning algorithms can then be applied to these data to identify patterns and make predictions about plant performance. For example, spectral indices derived from near-infrared (NIR) imaging have shown significant correlations with grain yield in wheat, indicating their potential as indirect selection traits (Hu et al., 2020). Moreover, ML models can be used to predict complex traits such as lodging in wheat, which is influenced by multiple genetic factors (Singh et al., 2019). 2.3 Case studies on the application of HTP in identifying disease resistance loci Several case studies have demonstrated the effectiveness of HTP in identifying disease resistance loci in wheat. One notable example is the use of UAV-based phenotyping to assess lodging in wheat. Lodging is a complex trait that affects yield and quality, and traditional visual assessment methods are time-consuming and subjective. By using UAVs to capture high-resolution images and generate digital elevation models, researchers were able to quantitatively assess lodging across thousands of wheat plots. This approach led to the identification of a key genomic region on chromosome 2A associated with lodging resistance, highlighting the potential of HTP to uncover genetic factors underlying complex traits (Singh et al., 2019). Another case study focused on the use of UAVs to monitor disease resistance in wheat breeding programs. High-resolution imaging and spectral indices were used to assess disease symptoms and stress responses in wheat genotypes. The data collected from UAVs were integrated with genomic information to identify disease resistance loci and improve the selection of resistant genotypes. This approach not only increased the efficiency of phenotyping but also provided valuable insights into the genetic basis of disease resistance (Shakoor et al., 2017; Hu et al., 2020). The advancements in sensor technologies, automated image analysis, and machine learning have significantly enhanced the capabilities of high-throughput phenotyping in agriculture. These technologies have enabled the efficient and accurate assessment of complex traits, including disease resistance in wheat, and have the potential to accelerate crop improvement programs. The case studies presented here demonstrate the practical applications
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