Rapid Determination of Leaf Water Content Using VIS/NIR Spectroscopy Analysis with Wavelength Selection

Water content in plants is one of the most common biochemical parameters limiting efficiency of photosynthesis and crop productivity. Therefore, it has very important meaning to predict the water content rapidly and nondestructively. The objective of this study was to investigate the feasibility of detecting the water content in the leaf using the diffuse reflectance spectra limited in the VIS/NIR region (400–1100 nm), which could be used to determine other biochemical parameters such as chlorophyll and nitrogen content. The experiment with leaves in different water stress was conducted. The statistical test result indicated that the determination of water content in leaf could be successfully performed by VIS/NIR spectroscopy combined with chemometrics method. The performances of different pretreatment methods were compared. The model with best performance was obtained from the first derivative spectra. In order to make the calibration model more parsimonious and stable, a hybrid wavelength selection method was proposed to extract the efficient feature wavelength. Under the optimal condition, an RMSEP of 0.73% with 25 variables was obtained for water content prediction using extern validation. The conclusions presented could lead to the development of portable instrument for synchronous detecting water content and other biochemical parameters rapidly and nondestructively.

[1]  W. Marsden I and J , 2012 .

[2]  M. Pessarakli Protein Synthesis by Plants under Stressful Conditions , 2010 .

[3]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .

[4]  Maosong Li,et al.  Assessment of photochemical reflectance index as a tool for evaluation of chlorophyll fluorescence parameters in cotton and peanut cultivars under water stress condition. , 2010 .

[5]  F. Intrigliolo,et al.  Estimation of plant nutritional status by Vis-NIR spectrophotometric analysis on orange leaves (Citrus sinensis (L) Osbeck cv Tarocco) , 2010 .

[6]  Mei Yang,et al.  Artificial neural network to predict leaf population chlorophyll content from cotton plant images. , 2010 .

[7]  Yang Li,et al.  Discrimination of Ephedra plants with diffuse reflectance FT-NIRS and multivariate analysis. , 2010, Talanta.

[8]  M. Umeda,et al.  Assessment of the water status of mandarin and peach canopies using visible multispectral imagery , 2008 .

[9]  C. Giardino,et al.  Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling , 2008 .

[10]  Lin Li,et al.  Retrieval of vegetation equivalent water thickness from reflectance using genetic algorithm (GA)-partial least squares (PLS) regression , 2008 .

[11]  Xiaoli Li,et al.  Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy , 2007 .

[12]  Shusen Wang,et al.  Remote sensing of grassland–shrubland vegetation water content in the shortwave domain , 2006 .

[13]  Y. Mizukami,et al.  Moisture Content Measurement of Tea Leaves by Electrical Impedance and Capacitance , 2006 .

[14]  Rong Liu,et al.  [Application of O-PLS in fundamental study of non-invasive measurement of human blood glucose concentration with near infrared spectroscopy]. , 2005, Guang pu xue yu guang pu fen xi = Guang pu.

[15]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[16]  Roberto Kawakami Harrop Galvão,et al.  A method for calibration and validation subset partitioning. , 2005, Talanta.

[17]  Jason A. Cole,et al.  Hyperspectral Remote Sensing and Its Applications , 2005 .

[18]  M. Deshayes,et al.  Estimation of foliage moisture content using near infrared reflectance spectroscopy , 2004 .

[19]  Eric R. Ziegel,et al.  Tsukuba Meeting: Largest Attendance Ever , 2004, Technometrics.

[20]  W. M. Miller,et al.  NIR-BASED SENSING TO MEASURE SOLUBLE SOLIDS CONTENT OF FLORIDA CITRUS , 2004 .

[21]  Heather McNairn,et al.  Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopies , 2003 .

[22]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[23]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[24]  S. Engelsen,et al.  Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy , 2000 .

[25]  M. de la Guardia,et al.  PLS-NIR determination of total sugar, glucose, fructose and sucrose in aqueous solutions of fruit juices , 1997 .

[26]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[27]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[28]  V. Maier,et al.  Abscisic acid accumulation and carotenoid and chlorophyll content in relation to water stress and leaf age of different types of citrus , 1990 .

[29]  B. Rock,et al.  Measurement of leaf relative water content by infrared reflectance , 1987 .

[30]  R. G. Brown,et al.  Estimating Leaf Water Content by Reflectance Measurements1 , 1971 .

[31]  R. Stephenson A and V , 1962, The British journal of ophthalmology.