Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat.
Ontology highlight
ABSTRACT:
SUBMITTER: Silva-Perez V
PROVIDER: S-EPMC5853784 | biostudies-literature | 2018 Jan
REPOSITORIES: biostudies-literature
Publications
Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat.
Silva-Perez Viridiana V Molero Gemma G Serbin Shawn P SP Condon Anthony G AG Reynolds Matthew P MP Furbank Robert T RT Evans John R JR
Journal of experimental botany 20180101 3
Improving photosynthesis to raise wheat yield potential has emerged as a major target for wheat physiologists. Photosynthesis-related traits, such as nitrogen per unit leaf area (Narea) and leaf dry mass per area (LMA), require laborious, destructive, laboratory-based methods, while physiological traits underpinning photosynthetic capacity, such as maximum Rubisco activity normalized to 25 °C (Vcmax25) and electron transport rate (J), require time-consuming gas exchange measurements. The aim of ...[more]
Similar Datasets
Project description:BACKGROUND:Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. RESULTS:This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. CONCLUSIONS:We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
| S-EPMC5209864 | biostudies-literature
Project description:Drought is one of the most prominent limiting factors that negatively affect crop productivity by manipulating its physiological pathway. One hundred twenty diverse bread wheat genotypes were used in a pot experiment to explore the relationship among their fifteen physio-biochemical traits (PBT) by using multivariate analysis, heatmapping and stress tolerance index (STI) for grain yield as a marker trait to identify high yielding genotype with maximum stress tolerance capability. Increased proline and sugar accumulation were observed from control to moisture deficient environments by 159% and 122%, respectively. Moreover, leaf membrane stability index (LMSI), leaf relative water content (LRWC), relative dry weight (RDW), chlorophyll content, leaf surface area (LSA), Leaf succulence (LS), canopy temperature depression (CTD), relative excised leaf water loss (RELWL) and leaf osmotic potential (LOP) showed significantly decreasing trend in drought stress treatment as compared to well-watered plants by -21%, -21%, -34%, -22%, -38%, -37%, -46%, -18% and -35% respectively. Additionally, principal component analysis and genotype by trait biplot analysis showed that initial 7 principal components (PC1 to PC7) represented 77.27% and 79.02% of total cumulative variation under control and drought stress respectively. Genotypic-Phenotypic correlation revealed that most of the attributes were higher in case of genotypic correlation component (rg) as compared to the phenotypic correlation component (rp) indicating more genetic association between traits. The darker and lighter colour scale produced by heatmap exhibited contrasting nature of genotypes, as positive side with higher values represented drought resistance while values on the negative side with lower values showed susceptible performance of genotypes. Our results concluded that the studied PBT associated with STI for grain yield are the main factors which may contribute in improved productivity of wheat crop and if these traits show appropriate performance under stress condition the crop will show the more productive returns under changing climate.
| S-EPMC7189857 | biostudies-literature
Project description:Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.
| S-EPMC6469421 | biostudies-other
Project description:The use of remote sensing techniques allows monitoring of photosynthesis at the ecosystem level and improves our knowledge of plant primary productivity. The main objective of the current study was to develop a remote sensing based method to measure microphytobenthos (MPB) primary production from intertidal mudflats. This was achieved by coupling hyperspectral radiometry (reflectance, ? and second derivative, ??) and PAM-fluorometry (non-sequential light curves, NSLC) measurements. The latter allowed the estimation of primary production using a light use efficiency parameter (LUE) and electron transport rates (ETR) whereas ? allowed to estimate pigment composition and optical absorption cross-section (a*). Five MPB species representative of the main growth forms: epipelic (benthic motile), epipsammic (benthic motile and non motile) and tychoplanktonic (temporarily resuspended in the water column) were submitted to increasing light intensities from dark to 1950 ?mol photons.m-2.s-1. Different fluorescence patterns were observed for the three growth-forms and were linked to their xanthophyll cycle (de-epoxydation state). After spectral reflectance measurements, a* was retrieved using a radiative transfer model and several radiometric indices were tested for their capacity to predict LUE and ETR measured by PAM-fluorometry. Only one radiometric index was not species or growth-form specific, i.e. ??496/508. This index was named MPBLUE and could be used to predict LUE and ETR. The applicability of this index was tested with simulated bands of a wide variety of hyperspectral sensors at spectral resolutions between 3 and 15 nm of Full Width at Half Maximum (FWHM).
| S-EPMC5951593 | biostudies-literature
Project description:BackgroundMacrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data, but existing applications are biased towards terrestrial plants. We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). We explored optimal spectral band combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data could capture spatial-temporal macrophyte variability.ResultsOur key finding is that structural-leaf dry matter content, leaf mass per area-and biochemical-chlorophyll-a content and chlorophylls to carotenoids ratio-traits can be surrogated by leaf reflectance with normalized error under 17% across macrophyte species. On the other hand, the performance of reflectance-based models for photophysiological traits substantively varies, depending on macrophyte species and target parameters.ConclusionsOur main results show the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus envisioning a crucial role for remote sensing in enhancing the level of detail of macrophyte functional diversity analysis to intra-site and intra-species scales. At the same time, we highlighted some difficulties in establishing a general link between reflectance and photosynthetic performance under high environmental heterogeneity, potentially opening further investigation directions.
| S-EPMC8582205 | biostudies-literature
Project description:The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350-2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30-0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.
| S-EPMC6848100 | biostudies-literature
Project description:The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements—including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth—were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light–tissue interactions and characterizing biophotonic system performance.
| S-EPMC4881289 | biostudies-literature
Project description:A hyperspectral reflectance confocal microscope (HSCM) was realized by CNR-ISC (Consiglio Nazionale delle Ricerche-Istituto dei Sistemi Complessi) a few years ago. The instrument and data have been already presented and discussed. The main activity of this HSCM has been within biology, and reflectance data have shown good matching between spectral signatures and the nature or evolution on many types of cells. Such a relationship has been demonstrated mainly with statistical tools like Principal Component Analysis (PCA), or similar concepts, which represent a very common approach for hyperspectral imaging. However, the point is that reflectance data contains much more useful information and, moreover, there is an obvious interest to go from reflectance, bound to the single experiment, to reflectivity, or other physical quantities, related to the sample alone. To accomplish this aim, we can follow well-established analyses and methods used in reflectance spectroscopy. Therefore, we show methods of calculations for index of refraction n, extinction coefficient k and local thicknesses of frequency starting from phase images by fast Kramers-Kronig (KK) algorithms and the Abeles matrix formalism. Details, limitations and problems of the presented calculations as well as alternative procedures are given for an example of HSCM images of red blood cells (RBC).
| S-EPMC6274177 | biostudies-literature
Project description:In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1-DS14) and used as inputs to build the classification models. Models' performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).
| S-EPMC8415606 | biostudies-literature
Project description:Salinity stress is a major constraint to sustainable crop production due to its adverse impact on crop growth, physiology, and productivity. As potato is the fourth most important staple food crop, enhancing its productivity is necessary to ensure food security for the ever-increasing population. Identification and cultivation of salt-tolerant potato genotypes are imperative mitigating strategies to cope with stress conditions. For this purpose, fifty-three varieties of potato were screened under control and salt stress conditions for growth and yield-related traits during 2020. Salt stress caused a mean reduction of 14.49%, 8.88%, and 38.75% in plant height, stem numbers, and tuber yield, respectively in comparison to control. Based on percent yield reduction, the genotypes were classified as salt-tolerant (seven genotypes), moderately tolerant (thirty-seven genotypes), and salt-sensitive genotypes (nine genotypes). Seven salt-tolerant and nine salt-sensitive genotypes were further evaluated to study their responses to salinity on targeted physiological, biochemical, and ionic traits during 2021. Salt stress significantly reduced the relative water content (RWC), membrane stability index (MSI), photosynthesis rate (Pn), transpiration rate (E), stomatal conductance, and K+/Na+ ratio in all the sixteen genotypes; however, this reduction was more pronounced in salt-sensitive genotypes compared to salt-tolerant ones. The better performance of salt-tolerant genotypes under salt stress was due to the strong antioxidant defense system as evidenced by greater activity of super oxide dismutase (SOD), peroxidase (POX), catalase (CAT), and ascorbate peroxidase (APX) and better osmotic adjustment (accumulation of proline). The stepwise regression approach identified plant height, stem numbers, relative water content, proline content, H2O2, POX, tuber K+/Na+, and membrane stability index as predominant traits for tuber yield, suggesting their significant role in alleviating salt stress. The identified salt-tolerant genotypes could be used in hybridization programs for the development of new high-yielding and salt-tolerant breeding lines. Further, these genotypes can be used to understand the genetic and molecular mechanism of salt tolerance in potato.
| S-EPMC9316722 | biostudies-literature