High‐throughput phenotyping of canopy height in cool‐season crops using sensing techniques

Zhang, Chongyuan; Craine, Wilson; McGee, Rebecca J.; Vandemark, George J.; Davis, James B.; Brown, J.; Hulbert, Scot H.; Sankaran, Sindhuja

doi:10.1002/agj2.20632

Cited by 15 publications

(9 citation statements)

References 31 publications

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“…Given the ease of UAS image acquisition and mapping common CT QTL with UAS and manual phenotyping, improved scalability with UAS phenomics could lead to strong QTL-to-marker associations in applied breeding programs for developing climate-resilient crops (Varshney et al, 2021). Application of UAS phenomics was limited to upstream genetic studies for high heritability traits, such as plant height, lodging, and disease resistance (Wang et al, 2018; Singh et al, 2019; Sarkar et al, 2020; Zhou et al, 2020; Zhang et al, 2021). Our study directly demonstrates the utility of UAS phenomics for prebreeding of complex traits.…”

Section: Discussionmentioning

confidence: 99%

Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum

Marla

Felderhoff

Hayes

et al. 2022

Preprint

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In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index (NDVI) values from the chilling nested association mapping population detected CT QTL that colocialized with manual phenotyping CT QTL. Two of the four first-generation KASP molecular markers, generated using the peak QTL SNPs, failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic FST analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from two independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

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Section: Discussionmentioning

confidence: 99%

Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum

Marla

Felderhoff

Hayes

et al. 2022

Preprint

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“…This study focused on measuring tree height and canopy crown volume using 2D images and 3D point cloud. The 2D canopy height model utilizing DTM from Pix4D (directly acquired from the software (technique 1, referred to as T1) and DTM (technique 2, referred to as T2) using point sampling from DSM (similar to that reported in [28]) for the individual sensor angle and integrated angle images were generated and used for measuring the tree height and canopy crown volume (Figure 2). In addition, UAV-based point cloud data and LiDAR point cloud data were also used to extract similar features.…”

Section: Extraction Of Architectural Featuresmentioning

confidence: 99%

Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees

Raman

Carlos

Sankaran

2022

Sensors

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Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University’s Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.

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“…In contrast to the active LiDAR sensing technologies, passive sensing-based methods (e.g., Multi-view images) can also measure 3D structure through methods like structure from motion (SFM) [ 3 , 18 , 45 , 69 ]. Among the passive sensing-based techniques, digital aerial photogrammetry (DAP) is one of the most popular ways for field CH estimation due to its low cost, high efficiency, and high accuracy comparable to ULS [ 17 , 21 , 75 , 76 ]. These 3D sensing techniques have been successfully applied to CH measurement, including the adoption of TLS for accurate height measurement of maize (R 2 = 0.93) [ 64 ], cotton (R 2 = 0.97) [ 60 ], rice (R 2 = 0.91) [ 63 ], barley (R 2 = 0.95), pea (R 2 = 0.93), and bean (R 2 = 0.91) [ 9 ], the use of BLS for efficient height measurement of large-scale wheat [ 78 ] and forest [ 22 , 32 , 58 ]; the exploration of ULS for estimating CH of sugar beet (R 2 = 0.70), wheat (R 2 = 0.78), and potato (R 2 = 0.50) [ 24 ], and DAP for measuring corn CH (R 2 = 0.78) [ 57 ].…”

Section: Introductionmentioning

confidence: 99%

Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing

et al. 2023

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Canopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-measured height is as reliable as believed. This study uncovered these issues by comparing traditional height measurement with four advanced 3D sensing technologies, including terrestrial laser scanning (TLS), backpack laser scanning (BLS), gantry laser scanning (GLS), and digital aerial photogrammetry (DAP). A total of 1920 plots covering 120 varieties were selected for comparison. Cross-comparisons of different data sources were performed to evaluate their performances in CH estimation concerning different CH, leaf area index (LAI), and growth stage (GS) groups. Results showed that 1) All 3D sensing data sources had high correlations with field measurement (r > 0.82), while the correlations between different 3D sensing data sources were even better (r > 0.87). 2) The prediction accuracy between different data sources decreased in subgroups of CH, LAI, and GS. 3) Canopy height showed high heritability from all datasets, and 3D sensing datasets had even higher heritability (H2 = 0.79–0.89) than FM (field measurement) (H2 = 0.77). Finally, outliers of different datasets are analyzed. The results provide novel insights into different methods for canopy height measurement that may ensure the high-quality application of this important trait.

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High‐throughput phenotyping of canopy height in cool‐season crops using sensing techniques

Cited by 15 publications

References 31 publications

Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum

Genomics and Phenomics Enabled Prebreeding Improved Early-Season Chilling Tolerance in Sorghum

Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees

Field-measured canopy height may not be as accurate and heritable as believed: evidence from advanced 3D sensing

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