Search

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. Highlights: The effects of canopy height, leaf area index, and growth stage on the accurate monitoring of canopy height with different 3D sensors were systematically evaluated. Field-measured canopy height may not be as accurate as believed, especially in the plots with higher canopy height and at later growth stages. 3D sensing methods achieved higher heritable canopy height estimation than field measurement. [ABSTRACT FROM AUTHOR]

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics. [ABSTRACT FROM AUTHOR]

High throughput plant phenomics enables precise quantification of structural information for the complex crop canopy. Leaf to panicle ratio (LPR) in terms of light interception is a physiological trait we formerly developed to clarify the light distribution pattern within the canopy of japonica rice. Here, using the methodology of deep learning neural network (Transformer Feature Pyramid Network), we proposed a general method for LPR calculation for both japonica and indica rice, and tested it in the study on variation of canopy structure across nitrogen (N) fertilization modes. Field experiments over three years (2020–2022) with three nitrogen levels and two basal to topdressing ratios were conducted for two cultivars of each japonica and indica rice. Results showed contrasting dynamic variation of LPR between the two species, ascending for indica rice but descending for japonica rice along with the grain-filling progression. Indica rice had larger temporal variation in LPR than the japonica. N topdressing significantly increased the LPR of indica rice cultivars at same N level, whereas that of japonica was dependent on N level and genotype. Morphological measurement revealed that the differential response of LPR to N was associated with the height difference between the flag leaf and panicle, panicle curvature, leaf area index and panicle area index. Correlation analysis revealed that the relation between LPR and grain yield was significantly positive for indica rice but negative for japonica rice. Our findings suggest that LPR can effectively reflect the characteristics of canopy structure as affected by cultivars and fertilization modes, thus being a valuable physiological indicator for crop science. • LPR (leaf to panicle ratio) was used to compare the canopy structure between japonica and indica rice. • The two subspecies were contrasting in the dynamics of canopy structure. • LPR of indica rice was more sensitive to nitrogen topdressing. • LPR and yield was positively related for indica but negatively for japonica. [ABSTRACT FROM AUTHOR]