Determination of water contents in leaves by a near-infrared multispectral imaging technique

Tran, Chieu D.; Grishko, Victor I.

doi:10.1016/j.microc.2003.10.015

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Generally, spectral interval between 1,888 and 1,956 nm has been associated with the second overtone for biding O-H, while the second overtone and combination for free-OH located on 960–980 and 1,920–1,980 nm, respectively (Haaland and Thomas, 1988 ; Suykens and Vanderwalle, 1999 ; Fearn, 2002 ; Chauchard et al, 2004 ; Cherkassky and Ma, 2004 ; Tran and Grishko, 2004 ; Arana et al, 2005 ; Frost et al, 2007 ; Fagan et al, 2011 ). In the current study, 75 wavelengths were identified as sensitive and important for determination of leaf water content in Miscanthus (Figure S1 ; Table S2 ).…”

Section: Resultsmentioning

confidence: 99%

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Jin

Shi

et al. 2017

Front. Plant Sci.

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Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.

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Section: Resultsmentioning

confidence: 99%

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Jin

Shi

et al. 2017

Front. Plant Sci.

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“…However, hyperspectral imaging technology still faces some problems, such as its high cost and excessive redundant information. In contrast, multispectral imaging technology does not have these problems, so it is easier to understand and apply [10,11].…”

Section: Introductionmentioning

confidence: 99%

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

Wang

et al. 2023

Forests

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The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R2 of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species.

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“…Moreover, this method requires ex situ analysis, indicating its incapacity for real-time monitoring. Other methods which allow to determine the water in liquid and solid samples include gas chromatography (Iguchi et al 1988 ; Nubbaum et al 2000 ), liquid chromatography (Chen and Fritz 1989 ), infrared spectroscopy (Tran and Grishko 2004 ; Troshin et al 2008 ; Bampi et al 2013 ), refractometric method (Sanchez et al 2010 ), microwave accelerated Dean–Stark method (Villet et al 2009 ), electrochemical method (Gąsiorowska et al 2009 ), as well as absorption and fluorescence spectroscopy (Bai and Yang 2007 ; Cha et al 2011 ; Rahimi-Nasrabadi et al 2015 ; Shen et al 2016 ). Recently, the development of highly sensitive fluorescent and colorimetric molecular sensors seems to have great meaning.…”

Section: Introductionmentioning

confidence: 99%

The application of 7H-indolo[1,2-a]quinolinium merocyanine as a new water sensor in organic solvents

Sawicka

Wróblewska

2017

Chem. Pap.

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The 7H-indolo[1,2-a]quinolinium merocyanine was applied as a new water sensor in organic solvents such as ethanol, propane-1-ol, propane-2-ol, DMSO, and DMF. The spectral changes of the dye caused by the addition of increasing amount of water into an organic solvent were investigated. Based on the results, the calibration curves were found as a relation between the position of the absorption band of the dye and the water concentration ranging from about 0.05 to 11% (w/w). In case of ethanol, propane-1-ol and propane-2-ol the plots were linear, whereas in DMSO and DMF, better results were obtained with the use of a polynomial function. The method allowed to determine the water content in a fast and precise manner.Graphical Abstract Electronic supplementary materialThe online version of this article (10.1007/s11696-017-0328-z) contains supplementary material, which is available to authorized users.

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Determination of water contents in leaves by a near-infrared multispectral imaging technique

Cited by 12 publications

References 26 publications

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

The application of 7H-indolo[1,2-a]quinolinium merocyanine as a new water sensor in organic solvents

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