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Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR.

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Title: Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR.
Authors: Rasooli Sharabiani, Vali1 (AUTHOR) vrasooli@uma.ac.ir, Soltani Nazarloo, Araz1 (AUTHOR), Taghinezhad, Ebrahim2,3 (AUTHOR), Veza, Ibham4 (AUTHOR), Szumny, Antoni3 (AUTHOR), Figiel, Adam5 (AUTHOR)
Source: Food Science & Nutrition. May2023, Vol. 11 Issue 5, p2166-2175. 10p.
Subject Terms: *Chlorophyll, Winter wheat, Partial least squares regression, Standard deviations, Artificial neural networks, Optical spectroscopy, Near infrared spectroscopy
Abstract: Visible–near‐infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky–Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350–1400 nm wavelength were preprocessed using S. Golay smoothing, D1, SNV, and MSC. The preprocessing with SNV‐S.G, followed by PLS and ANN modeling, was able to achieve the most accurate prediction, with the correlation coefficient of 0.92 and 0.97, along with the root mean square error of 0.9131 and 0.7305 receptivity. The experimental findings also revealed that the suggested method utilizing the PLS and ANN model with SNV‐S. G preprocessing was practically feasible to estimate the chlorophyll content of a particular winter wheat leaf area according to the visible and near‐infrared spectroscopy sensors, achieving improved precision and accuracy. The nonlinear technique was proposed as a more refined technique for LCC estimating. [ABSTRACT FROM AUTHOR]
Copyright of Food Science & Nutrition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR.
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  Data: <searchLink fieldCode="AR" term="%22Rasooli+Sharabiani%2C+Vali%22">Rasooli Sharabiani, Vali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vrasooli@uma.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Soltani+Nazarloo%2C+Araz%22">Soltani Nazarloo, Araz</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Taghinezhad%2C+Ebrahim%22">Taghinezhad, Ebrahim</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Veza%2C+Ibham%22">Veza, Ibham</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Szumny%2C+Antoni%22">Szumny, Antoni</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Figiel%2C+Adam%22">Figiel, Adam</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Food+Science+%26+Nutrition%22">Food Science & Nutrition</searchLink>. May2023, Vol. 11 Issue 5, p2166-2175. 10p.
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  Data: *<searchLink fieldCode="DE" term="%22Chlorophyll%22">Chlorophyll</searchLink><br /><searchLink fieldCode="DE" term="%22Winter+wheat%22">Winter wheat</searchLink><br /><searchLink fieldCode="DE" term="%22Partial+least+squares+regression%22">Partial least squares regression</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+spectroscopy%22">Optical spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Near+infrared+spectroscopy%22">Near infrared spectroscopy</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Visible–near‐infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky–Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350–1400 nm wavelength were preprocessed using S. Golay smoothing, D1, SNV, and MSC. The preprocessing with SNV‐S.G, followed by PLS and ANN modeling, was able to achieve the most accurate prediction, with the correlation coefficient of 0.92 and 0.97, along with the root mean square error of 0.9131 and 0.7305 receptivity. The experimental findings also revealed that the suggested method utilizing the PLS and ANN model with SNV‐S. G preprocessing was practically feasible to estimate the chlorophyll content of a particular winter wheat leaf area according to the visible and near‐infrared spectroscopy sensors, achieving improved precision and accuracy. The nonlinear technique was proposed as a more refined technique for LCC estimating. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Food Science & Nutrition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/fsn3.3071
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        Text: English
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        PageCount: 10
        StartPage: 2166
    Subjects:
      – SubjectFull: Chlorophyll
        Type: general
      – SubjectFull: Winter wheat
        Type: general
      – SubjectFull: Partial least squares regression
        Type: general
      – SubjectFull: Standard deviations
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Optical spectroscopy
        Type: general
      – SubjectFull: Near infrared spectroscopy
        Type: general
    Titles:
      – TitleFull: Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR.
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            NameFull: Rasooli Sharabiani, Vali
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            NameFull: Soltani Nazarloo, Araz
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            – D: 01
              M: 05
              Text: May2023
              Type: published
              Y: 2023
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