scholarly journals Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat

2021 ◽  
Vol 13 (18) ◽  
pp. 3612
Author(s):  
Imran Haider Khan ◽  
Haiyan Liu ◽  
Wei Li ◽  
Aizhong Cao ◽  
Xue Wang ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Early Detection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Traditional Method ◽  
Vegetation Indices ◽  
Hyperspectral Images ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Estimation Model

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

Developing near infrared spectroscopy models for predicting chemistry and responses to stress in Pinus radiata (D. Don)

2021 ◽  
pp. 096703352110065
Author(s):  
Judith S Nantongo ◽  
BM Potts ◽  
T Rodemann ◽  
H Fitzgerald ◽  
NW Davies ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Pinus Radiata ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Individual Plant ◽  
Least Squares Regression ◽  
Plant Parts ◽  
Quantitative Genetic

Incorporating chemical traits in breeding requires the estimation of quantitative genetic parameters, especially the levels of additive genetic variation. This requires large numbers of samples from pedigreed populations. Conventional wet chemistry procedures for chemotyping are slow, expensive and not a practical option. This study focuses on the chemical variation in Pinus radiata, where the near infrared (NIR) spectral properties of the needles, bark and roots before and after exposure to methyl jasmonate (MJ) and artificial bark stripping (strip) treatments were investigated as an alternative approach. The aim was to test the capability of NIR spectroscopy to (i) discriminate samples exposed to MJ and strip assessed 7, 14, 21 and 28 days after treatment from untreated samples, and (ii) quantitatively predict individual chemical compounds in the three plant parts. Using principal components analysis (PCA) on the spectral data, we differentiated between treated and untreated samples for the individual plant parts. Based on partial least squares–discriminant analysis (PLS-DA) models, the best discrimination of treated from non-treated samples with the smallest root mean square error cross-validation (RMSECV) and highest coefficient of determination (r2) was achieved in the fresh needles (r2 = 0.81, RMSECV= 0.24) and fresh inner bark (r2 = 0.79, RMSECV = 0.25) for MJ-treated samples 14 days and 21 days after treatment, respectively. Using partial least squares regression, models for individual compounds gave high (r2), residual predictive deviation (RPD), lab to NIR error (PRL) or range error ratio (RER) for fructose (r2 = 0.84, RPD = 1.5, PRL = 0.71, RER = 7.25) and glucose (r2 = 0.83, RPD = 1.9, PRL = 1.14, RER = 8.50) and several diterpenoids. This provides an optimistic outlook for the use of NIR spectroscopy-based models for the larger-scale prediction of the P. radiata chemistry needed for quantitative genetic studies.

Oil Content Estimation of Individual Kernels of Quercus Ilex Subsp. Rotundifolia [(Lam) O. Schwarz] Acorns by Fourier Transform near Infrared Spectroscopy and Partial Least Squares Regression

2007 ◽  
Vol 15 (4) ◽  
pp. 247-260 ◽  
Author(s):  
Cristina Sousa-Correia ◽  
Ana Alves ◽  
José C. Rodrigues ◽  
Suzana Ferreira-Dias ◽  
José M. Abreu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Oil Content ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Association Studies ◽  
Extraction Procedure ◽  
Coefficient Of Determination ◽  
Least Squares Regression

The aim of this work was to use Fourier transform near infrared (FT-NIR) spectroscopy and partial least squares regression (PLSR) to estimate the oil content of individual Holm oak ( Quercus sp.) acorn kernels from different trees, sites and years that should be used in the future for molecular marker association studies. Sampling of acorns in two consecutive years (2003 and 2004) and from different sites in Portugal provided independent sample sets. A total of 89 samples (acorn kernels) representative of the natural oil content range were extracted. The results of the analyses performed by three people revealed accuracy of the oil extraction procedure ( n-hexane) and the precision (repeatability) of this method, assessed during a four-day period, gave a standard deviation of 0.1%. Careful wavenumber selection and several steps of validation of the PLSR models led to a final robust model that allowed the precise prediction of the oil content of individual acorns. By using the wavenumber ranges from 5995 to 5323 cm−1 and from 4478 to 4177 cm−1 of the vector normalised spectra, a PLSR model with a coefficient of determination ( r 2) of 0.992 and a root mean square error of cross-validation ( RMSECV) of 0.37% was achieved. The RPD value of about 10 and a bias of almost zero showed that the developed models are good for process control, development, and applied research. Oil content estimation of individual Quercus sp. acorns by FT-NIR and PLSR was shown to be possible. The varying water content detected in the spectra of the milled kernels after drying in similar conditions, within and especially between years, could be handled.

Rapid detection of infrared inactive sodium chloride content in frozen tuna fish for determining commercial value using short wavelengths

2019 ◽  
Vol 27 (6) ◽  
pp. 424-431 ◽  
Author(s):  
Suttahatai Pochanagone ◽  
Ronnarit Rittiron
Keyword(s):  
Sodium Chloride ◽  
Least Squares ◽  
Partial Least Squares ◽  
Rapid Detection ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Chloride Content ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Tuna Fish

The sodium chloride content in the flesh of tuna fish is one of the factors for determining the price in the fishing industry. Titration is a standard method for the analysis of salt and this is time consuming. Near infrared spectroscopy is a potential alternative method for rapid detection without the need for wet chemical assay. Although sodium chloride is infrared inactive, this study investigated the influence of salt on the absorbance of near infrared energy and showed that the sodium chloride content can be determined using changes in the water band at 970 nm. Calibration equations were developed from frozen fish pieces and ground samples using multiple linear regression for the wavelength region of 700–1000 nm. The best result was achieved from frozen samples with a coefficient of determination for the calibration set ([Formula: see text]) = 0.71, standard error of calibration (SEC) = 0.20%, coefficient of determination for the validation set ([Formula: see text]) = 0.64, standard error of prediction (SEP) = 0.26% and bias = − 0.00%. In order to verify the significant variables used to determine infrared inactive sodium chloride, partial least squares regression was performed on frozen samples. The important variable in multiple linear regression and partial least squares regression was the absorbance band at 976 nm attributed to water molecules. The result from partial least squares calibration showed [Formula: see text] = 0.83, SEC = 0.20%, [Formula: see text] = 0.54, SEP = 0.25% and bias = 0.00%. The salt values predicted using the near infrared models were not significantly different from the reference values obtained by the standard titration method at the 95% confidence interval.

Partial Least Squares Regression of Hyperspectral Images for Contaminant Detection on Poultry Carcasses

2006 ◽  
Vol 14 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Bosoon Park ◽  
Gerald W. Heitschmidt ◽  
Douglas P. Smith ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Hyperspectral Images ◽  
Least Squares Regression ◽  
Contaminant Detection

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Sign in / Sign up

Export Citation Format

Share Document