Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves

Neto, Antônio José Steidle; Lopes, Darci Clementino; Pinto, Francisco; Zolnier, Sérgio

doi:10.1016/j.biosystemseng.2016.12.008

Cited by 59 publications

(20 citation statements)

References 46 publications

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“…The leaf chlorophyll content and color related traits in fresh leaves can be accurately predicted using field-base reflectance spectroscopy. The study, supported by Steidle Neto et al [23, 24] and Xie et al [24], presents a reliable and robust methodology on NIR reflectance spectra for all leaf traits to estimate the prediction model accuracy. This methodology was firstly reported by Couture et al [13].…”

Section: Discussionmentioning

confidence: 94%

Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu

Sun

Jiang

2019

Plant Methods

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Background Reflectance spectroscopy, like IR, VIS–NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. But less studies have been made on the application of NIR reflectance spectroscopy to plant leaf color and pigments analysis and the possibility of using it for genetic breeding selection. Here, we examine the ability of NIR reflectance spectroscopy to determine the plant leaf color and chlorophyll content in Sassafras tzumu leaves and use the prediction results for genetic selection. Fresh and living tree leaves were used for NIR spectra collection, leaf color parameters (a*, b* and L*) and chlorophyll content were measured with standard analytical methods, partial least squares regression (PLSR) were used for model construction, the coefficient of determination (R 2 ) [cross-validation ( ) and validation ( )] and root mean square error (RMSE) [cross-validation (RMSE CV ) and validation (RMSE V )] were used for model performance evaluation, significant Multivariate Correlation algorithm was applied for model improvement, to find out the most important region related to the leaf color parameters and chlorophyll model, which have been simulated 100 times for accuracy estimation. Results Leaf color parameters (a*, b* and L*) and chlorophyll content were well predicted by NIR reflectance spectroscopy on fresh leaves in vivo. The mean and RMSE CV of a*, b*, L* and chlorophyll content were (0.82, 4.43), (0.63, 3.72), (0.61, 2.35) and (0.86, 0.13%) respectively. Three most important NIR regions, including 1087, 1215 and 2219 nm, which were highly related to a*, b*, L* and chlorophyll content were found. NIR reflectance spectra technology can be successfully used for genetic breeding program. High heritability of a*, b*, L* and chlorophyll content ( h 2 = 0.77, 0.89, 0.78, 0.81 respectively) were estimated. Several families with bright red color or bright yellow color were selected. Conclusions NIR spectroscopy is promising for the rapid prediction of leaf color and chlorophyll content of living fresh leaves. It has the ability to simultaneously measure multiple plant leaf traits, potentially allowing for quick and economic prediction in situ.

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Section: Discussionmentioning

confidence: 94%

Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu

Sun

Jiang

2019

Plant Methods

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“…S1–S7). The strong absorption bands of chlorophyll and water in the plants were between 420 and 460 nm (Steidle Neto et al, 2017) and were due to the strong absorption of carotenoids; the strong absorption of chlorophyll in plants near 700 nm, as well as the red edge information of plants and the weak absorption of water, was due to a trough of most vegetation reflectivity (Haboudane et al, 2002). The plant red edge information was near 750 nm, which was the point of strong water and oxygen absorption (Okin et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Wang

Ding

et al. 2019

PeerJ

141

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Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

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“…These results agree with Cozzolino et al (2011) who affirmed that if more than optimum number of latent variables is used, the solution can become over-fitted and the model will be very dependent on the dataset, giving poor prediction results. On the other hand, as noted by Steidle Neto et al (2017), using less than the optimum number of latent variables will cause under-fitting and the model will not be accurate enough to capture the variability in the data. Figure 2 shows the correlations between the R global values and those predicted by the multivariate model after the external validation.…”

Section: Resultsmentioning

confidence: 99%

Influence of Atmospheric Aerosols on Evapotranspiration over a Semiarid Region in Northeast Brazil

Neto

Lopes

2018

Rev. bras. meteorol.

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The knowledge of atmospheric aerosol characteristics at regional and local scales, their temporal changes and interrelations with meteorological variables are of great importance because can modify the global radiative energy balance. This study was carried out to evaluate the influence of aerosols on evapotranspiration over a semiarid region in Northeast Brazil. Aerosol optical depth, global solar radiation, air temperature, air relative humidity, and wind speed were measured in the Research National Center of Semiarid, located at Petrolina, Pernambuco State, Brazil, along two consecutive years. A multivariate model was calibrated and validated for estimating the global solar radiation (Rglobal) based on the aerosol optical depths (AOD) and extraterrestrial solar radiation (R0). Estimates of the reference evapotranspiration (ET0) were obtained by using the FAO standard Penman-Monteith method. Results confirmed that larger aerosol optical depth tends to decrease both Rglobal and ET0. Otherwise, ticker aerosol optical depth tends to absorb less radiation, increasing the ET0. For the semiarid region in Northeast Brazil, most of ET0 deviations were smaller than ±4%. However, higher deviations were also observed, contributing jointly to around 30%, which may result in relevant consequences on the irrigation management, water balance, agroclimatic zoning, among other activities based on the ET0 estimates.

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Vis/NIR spectroscopy and chemometrics for non-destructive estimation of water and chlorophyll status in sunflower leaves

Cited by 59 publications

References 46 publications

Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu

Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Influence of Atmospheric Aerosols on Evapotranspiration over a Semiarid Region in Northeast Brazil

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