Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Abstract

a b s t r a c tField-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (C w ), specific leaf mass (C m ), leaf chlorophyll a þ b content (C ab ), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the physiological (or photochemical) reflectance index (PRI) were compared with partial least squares regression (PLSR) approaches to estimate the four phenotypes. Additionally, inversion of the PROSAIL plant canopy reflectance model was investigated to estimate phenotypes based on 3.68 billion PROSAIL simulations on a supercomputer. Phenotypic estimates from each approach were compared with field measurements, and hierarchical linear mixed modeling was used to identify differences in the estimates among the cultivars and water levels. The PLSR approach performed best and estimated C w ; C m ; C ab , and LAI with root mean squared errors (RMSEs) between measured and modeled values of 6.8%, 10.9%, 13.1%, and 18.5%, respectively. Using linear regression with the vegetation indices, no index estimated C w ; C m ; C ab , and LAI with RMSEs better than 9.6%, 16.9%, 14.2%, and 28.8%, respectively. PROSAIL model inversion could estimate C ab and LAI with RMSEs of about 16% and 29%, depending on the objective function. However, the RMSEs for C w and C m from PROSAIL model inversion were greater than 30%. Compared to PLSR, advantages to the physically-based PROSAIL model include its ability to simulate the canopy's bidirectional reflectance distribution function (BRDF) and to estimate phenotypes from canopy spectral reflectance without a training data set. All proximal hyperspectral approaches were able to identify differences in phenotypic estimates among the cultivars and irrigation regimes tested during the field studies. Improvements to these proximal hyperspectral sensing approaches could be realized with a highthroughput phenotyping platform able to rapidly collect canopy spectral reflectance data from multiple view angles.Published by Elsevier B.V.

Spectral-temporal characterization of wheat cultivars through NDVI obtained by terrestrial sensors

“…Similar results were obtained by Thorp et al (2015), studying cotton cultivars. These authors found higher variability of reflectance in the near infrared (760-1350 nm) compared with the visible (400-700 nm) and medium infrared (1450-2400 nm).…”

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

“…/j.fcr.2017 green fraction, leaf area index (LAI), and the leaf biochemistry, e.g., chlorophyll and nitrogen contents (Comar et al, 2012;Jay et al, 2015;Thorp et al, 2015). An accurate retrieval of such structural and biochemical parameters is critical for plant phenotyping.…”

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

“…Some authors show that, in comparison to univariate approaches, multivariate approaches were able to provide better results in detection of early stages of biotic stress (Römer et al 2011), or in the predict nitrogen and water content (Kusnierek and Korsaeth 2015). Another approach, based on radiative transfer models, seems promising for field phenotyping, as this takes into account biochemical and structural properties of the leaf and canopy (Thorp et al 2015).…”

“…By using the BRDF output, the effects of canopy structure, composition and geometry, as well as illumination on the spectra collected, can be corrected for. Moreover, model inversion techniques and reflectance measurements can be used to obtain input parameters of the PROSAIL model, such as chlorophyll concentration, dry matter content or leaf water thickness (Thorp et al 2015). A challenging approach to overcoming issues caused by canopy geometry may be through the fusion of 3D point cloud images with 2D spectral images, whereby each point cloud within a 3D image is assigned a value corresponding to the same point in an image from a spectral image, however this is an avenue requiring further exploration.…”