Estimation of leaf nitrogen levels in sugarcane using hyperspectral models

ABSTRACT: Sugarcane is a good source of renewable energy and helps reduce the emission of greenhouse gases. Nitrogen has a critical role in plant growth; therefore,estimating nitrogen levels is essential, and remote sensing can improve fertilizer management. This field study selects wavelengths from hyperspectral data on a sugarcane canopy to generate models for estimating leaf nitrogen concentrations. The study was carried out in the municipalities of Piracicaba, Jaú, and Santa Maria da Serra, state of São Paulo, in the 2013/2014 growing season. The experiments were carried out using a completely randomized block design with split plots (three sugarcane varieties per plot [variety SP 81-3250 was common to all plots] and four nitrogen concentrations [0, 50, 100, and 150 kgha-1] per subplot) and four repetitions. The wavelengths that best correlated with leaf nitrogen were selected usingsparse partial least square regression. The wavelength regionswere combinedby stepwise multiple linear regression. Spectral bands in the visible (700-705 nm), red-edge (710-720 nm), near-infrared (725, 925, 955, and 980 nm), and short-wave infrared (1355, 1420, 1595, 1600, 1605, and 1610 nm) regions were identified. The R² and RMSE of the model were 0.50 and 1.67 g.kg-1, respectively. The adjusted R² and RMSE of the models for Piracicaba, Jaú, and Santa Maria were 0.31 (unreliable) and 1.30 g.kg-1, 0.53 and 1.96 g.kg-1, and 0.54 and 1.46 g.kg-1, respectively. Our results showed that canopy hyperspectral reflectance can estimate leaf nitrogen concentrations and manage nitrogen application in sugarcane.

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Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.12.022204 ◽

2018 ◽

Vol 12 (02) ◽

pp. 1 ◽

Cited By ~ 10

Author(s):

Wenzhi Zeng ◽

Dongying Zhang ◽

Yuanhao Fang ◽

Jingwei Wu ◽

Jiesheng Huang

Keyword(s):

Support Vector Machine ◽

Deep Learning ◽

Soil Salinity ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Least Square Regression ◽

Learning Techniques

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Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

Remote Sensing ◽

10.3390/rs13193991 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3991

Author(s):

Raquel Peron-Danaher ◽

Blake Russell ◽

Lorenzo Cotrozzi ◽

Mohsen Mohammadi ◽

John Couture

Keyword(s):

Winter Wheat ◽

Leaf Nitrogen ◽

Hyperspectral Data ◽

Plant Nitrogen ◽

Nitrogen Levels ◽

Nitrogen Concentrations ◽

Use Efficiency ◽

Canopy Nitrogen ◽

Wheat Lines

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

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LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-99-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 99-105

Author(s):

Elnaz Neinavaz ◽

Andrew K. Skidmore ◽

Roshanak Darvishzadeh ◽

Thomas A. Groen

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Narrow Band ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Growth Potential ◽

Partial Least Square Regression ◽

Area Index ◽

Thermal Data

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R<sup>2</sup> < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.

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Estimation of vegetation Equivalent Water Thickness using hyperspectral data and partial least square regression

2010 18th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2010.5567984 ◽

2010 ◽

Cited By ~ 1

Author(s):

Jie Zhang ◽

Jianjun Wu ◽

Lei Zhou

Keyword(s):

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Equivalent Water Thickness ◽

Least Square Regression

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Comparison of Three Regression Methods for the Winter Wheat Biomass Estimation Using Hyperspectral Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1843 ◽

2013 ◽

Vol 380-384 ◽

pp. 1843-1846

Author(s):

Yuan Yuan Fu ◽

Ji Hua Wang ◽

Gui Jun Yang ◽

Hai Kuan Feng

Keyword(s):

Winter Wheat ◽

Principal Component ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Estimation Accuracy ◽

Biomass Estimation ◽

Regression Methods ◽

Continuum Removal

To make full use of the hyperspectral data, the strong multi-collinearity in the data is supposed to be taken into account. With this study we evaluated three multivariate regression methods which are principal component regression, partial least square regression (PLSR) and stepwise multiple linear regression. Furthermore, to identify reliable winter wheat biomass predictive models, two different types of spectral transformations (continuum removal, first derivative) were combined with the three regression methods, respectively. Amongst these combinations, the respective combination of three regression methods and continuum removal got the highest estimation accuracy, especially, the combination of PLSR and continuum removal (R2=0.715, RMSE=0.218kg/m2). The experimental results demonstrated that PLSR is recommended for highly multi-collinear data sets. The combination of continuum removal and PLSR could improve the estimation accuracy of winter wheat biomass.

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LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-99-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 99-105 ◽

Cited By ~ 1

Author(s):

Elnaz Neinavaz ◽

Andrew K. Skidmore ◽

Roshanak Darvishzadeh ◽

Thomas A. Groen

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Narrow Band ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Growth Potential ◽

Partial Least Square Regression ◽

Area Index ◽

Thermal Data

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Assessment of the Hyperspectral Data Analysis as a Tool to Diagnose Xylella fastidiosa in the Asymptomatic Leaves of Olive Plants

Plants ◽

10.3390/plants10040683 ◽

2021 ◽

Vol 10 (4) ◽

pp. 683

Author(s):

Carmela Riefolo ◽

Ilaria Antelmi ◽

Annamaria Castrignanò ◽

Sergio Ruggieri ◽

Ciro Galeone ◽

...

Keyword(s):

Lignin Content ◽

Early Stage ◽

Xylella Fastidiosa ◽

Confusion Matrix ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Diagnostic Tools ◽

Reference Analysis

Xylella fastidiosa is a bacterial pathogen affecting many plant species worldwide. Recently, the subspecies pauca (Xfp) has been reported as the causal agent of a devastating disease on olive trees in the Salento area (Apulia region, southeastern Italy), where centenarian and millenarian plants constitute a great agronomic, economic, and landscape trait, as well as an important cultural heritage. It is, therefore, important to develop diagnostic tools able to detect the disease early, even when infected plants are still asymptomatic, to reduce the infection risk for the surrounding plants. The reference analysis is the quantitative real time-Polymerase-Chain-Reaction (qPCR) of the bacterial DNA. The aim of this work was to assess whether the analysis of hyperspectral data, using different statistical methods, was able to select with sufficient accuracy, which plants to analyze with PCR, to save time and economic resources. The study area was selected in the Municipality of Oria (Brindisi). Partial Least Square Regression (PLSR) and Canonical Discriminant Analysis (CDA) indicated that the most important bands were those related to the chlorophyll function, water, lignin content, as can also be seen from the wilting symptoms in Xfp-infected plants. The confusion matrix of CDA showed an overall accuracy of 0.67, but with a better capability to discriminate the infected plants. Finally, an unsupervised classification, using only spectral data, was able to discriminate the infected plants at a very early stage of infection. Then, in phase of testing qPCR should be performed only on the plants predicted as infected from hyperspectral data, thus, saving time and financial resources.

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Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Foods ◽

10.3390/foods9040500 ◽

2020 ◽

Vol 9 (4) ◽

pp. 500

Author(s):

Huihui Wang ◽

Kunlun Wang ◽

Xinyu Zhu ◽

Peng Zhang ◽

Jixin Yang ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Hyperspectral Data ◽

Correlation Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

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Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

Journal of Spectroscopy ◽

10.1155/2016/1081674 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 10

Author(s):

Dong Zhang ◽

Tashpolat Tiyip ◽

Jianli Ding ◽

Fei Zhang ◽

Ilyas Nurmemet ◽

...

Keyword(s):

Fractional Derivative ◽

Spectral Reflectance ◽

Salt Content ◽

Hyperspectral Data ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Lake Basin ◽

Soil Salt Content ◽

Soil Salt

Most present researches on estimation of soil salinity by hyperspectral data have focused on the spectral reflectance or their integer derivatives but ignored the fractional derivative information of hyperspectral data. Motivated by this situation, the selected study area is the Ebinur Lake basin located in the southwest border in the Xinjiang Uygur Autonomous Region, China, with severe salinization. The field work was conducted from 15 to 25 October, 2014, and a total of 180 soil samples were collected from 45 sampling sites; after measuring the soil salt content and spectral reflectance in the laboratory, the range from 0 to 2 was divided into 11 orders (interval 0.2) and then the hyperspectral data were treated by 4 kinds of mathematical transformations and 11 orders of fractional derivatives. Combined with the soil salt content, partial least square regression method was applied for model calibrations and predictions and some indexes were used to evaluate the performance of models. The results showed that the retrieval model built up by 250 bands based on 1.2-order derivative of 1/lg⁡R had excellent capacity of estimating soil salt content in the study area (RMSEC=14.685 g/kg, RMSEP=14.713 g/kg, R2C=0.782, R2P=0.768, and RPD = 2.080). This study provides an application reference for quantitative estimations of other land surface parameters and some other applications on hyperspectral technology.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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