|GORE, MICHAEL - Cornell University - New York|
|ANDRADE-SANCHEZ, P - University Of Arizona|
|CARMO-SILVA, E - Rothamsted Research|
|WELCH, S - Kansas State University|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/6/2015
Publication Date: 9/22/2015
Citation: Thorp, K.R., Gore, M.A., Andrade-Sanchez, P., Carmo-Silva, E., Welch, S.M., White, J.W., French, A.N. 2015. Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Computers and Electronics in Agriculture. 118:225-236.
Interpretive Summary: A pressing objective in plant science is to understand the connection between a plant's observable characteristics (phenotype) and its genetic makeup (genotype). Great advances in DNA sequencing have unlocked the genetic code for many important commodity crops, such as rice, sorghum, and maize. However, understanding how genes control complex traits, such as drought tolerance, time to flowering, and harvestable yield, remains challenging. Information technologies, including sensing and computing tools, are now being used to rapidly characterize the growth responses of genetically diverse plant populations in the field and relate these responses to individual genes. This study tested spectral reflectance measurements of cotton canopies as a way to rapidly estimate four cotton phenotypes related to leaf properties and canopy architecture. The spectral data was combined with statistical modeling and physical modeling of light interaction with the cotton canopy to estimate the cotton phenotypes. The study identified successful data analysis protocols and provided recommendations for incorporating spectral reflectance measurements into field-based phenomics research programs. In the next decades, field-based plant phenomics will likely revolutionize our understanding of how plant genetics interacts with the environment to produce food, feed, fiber and fuel resources. The results of this study will be used internationally by plant scientists in both the public and private sectors to advance current field-based plant phenomics efforts.
Technical Abstract: Field-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 (Cw), specific leaf mass (Cm), leaf chlorophyll a + b content (Cab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars for 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 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 analysis of variance was used to identify differences in the estimates among the cultivars and irrigation regimes. The PLSR approach performed best and estimated Cw, Cm, Cab, 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 Cw, Cm, Cab, and LAI with RMSEs better than 9.6%, 16.9%, 14.2%, and 28.8%, respectively. PROSAIL model inversion could estimate Cab and LAI with RMSEs of about 16% and 29%, depending on the objective function. However, the RMSEs for Cw and Cm 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, though differences in PLSR estimates were more similar to measured differences. Improvements to these proximal hyperspectral sensing approaches could be realized with a high-throughput phenotyping platform able to rapidly collect canopy spectral reflectance data from multiple view angles.