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. 2022 Oct 12;11(20):2691.
doi: 10.3390/plants11202691.

UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies

Karsten M E Nielsen  1 , Hema S N Duddu  2 , Kirstin E Bett  1 , Steve J Shirtliffe  1
Affiliations

UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies

Karsten M E Nielsen et al. Plants (Basel). .

Abstract

Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity.

Keywords: breeding efficiency; digital plant volume; high-throughput; plant growth rate; plant phenotyping.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Three-parameter growth curves showing dry weight biomass accumulation for each genotype throughout the growing season at each site-year; Nasser 2017 (A), Rosthern 2017 (B), Sutherland 2017 (C), Nasser 2018 (D), and Rosthern 2018 (E). Data at Nasser 2017 was best described by combining all genotypes within the model.
Figure 2
Figure 2
Estimated growth rate parameters: for dry weight biomass (A), vegetation area (B), and plot volume (C) are displayed for each genotype at each site-year. Error bars show standard error.
Figure 3
Figure 3
Estimated maximum predicted growth parameters: for dry weight biomass (A), vegetation area (B), and plot volume (C) of each genotype at each site-year. Error bars show standard error.
Figure 4
Figure 4
Three-parameter growth curves showing green pixel area accumulation for each genotype throughout the growing season at each site-year; Nasser 2017 (A), Rosthern 2017 (B), Sutherland 2017 (C), Nasser 2018 (D), and Rosthern 2018 (E). Data at Nasser 2017 was best described by combing all genotypes within the model.
Figure 5
Figure 5
Estimate of G50: for dry weight biomass (A), vegetation area (B), and plot volume (C) of each genotype at each site-year. Error bars show standard error.
Figure 6
Figure 6
Three-parameter growth curves showing volume accumulation for each genotype throughout the growing season at each site-year; Nasser 2017 (A), Rosthern 2017 (B), Sutherland 2017 (C), Nasser 2018 (D), and Rosthern 2018 (E). Data at Nasser 2017 was best described by combing all genotypes within the model.
Figure 7
Figure 7
Six orthomosaics represent six biomass harvest time points. Whole-plot biomass was measured approximately every two weeks throughout the growing season (harvests 1 to 6), with aerial images collected within 24 h prior to destructive sampling. Six diverse lentil genotypes were grown in a Randomized Complete Block Design (RCBD) with three replicates in five site years. Larger plots in each orthomosaic are pea plots used to separate replicates.
Figure 8
Figure 8
Image processing and analysis workflow.
See this image and copyright information in PMC

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