Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging

This study was carried out to investigate the potential of visible and near infrared (VIS–NIR) hyperspectral imaging system for rapid and non-destructive content determination and distribution estimation of nitrogen (N), phosphorus (P) and potassium (K) in oilseed rape leaves. Hyperspectral images of 140 leaf samples were acquired in the wavelength range of 380–1030 nm and their spectral data were extracted from the region of interest (ROI). Partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) were applied to relate the nutrient content to the corresponding spectral data and reasonable estimation results were obtained. The regression coefficients of the resulted PLSR models with full range spectra were used to identify the effective wavelengths and reduce the high dimensionality of the hyperspectral data. LS-SVM model for N with RP of 0.882, LS-SVM model for P with RP of 0.710, and PLSR model for K with RP of 0.746 respectively got the best prediction performance for the determination of the content of these three macronutrients based on the effective wavelengths. Distribution maps of N, P and K content in rape leaves were generated by applying the optimal calibration models in each pixel of reduced hyperspectral images. The different colours represented indicated the change of nutrient content in the leaves under different fertiliser treatments. The results revealed that hyperspectral imaging is a promising technique to detect macronutrients within oilseed rape leaves non-destructively and could be applied to in situ detection in living plants.

[1]  David Scholefield,et al.  Assessment of the nitrogen status of grassland , 2004 .

[2]  J. Moyer,et al.  Effect of nitrogen on the preference and performance of a biological control agent for an invasive plant , 2008 .

[3]  Di Wu,et al.  Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM , 2008 .

[4]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[5]  Wei Tang,et al.  Effects of plant density and nitrogen and potassium fertilization on cotton yield and uptake of major nutrients in two fields with varying fertility , 2010 .

[6]  Consuelo Pizarro,et al.  Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy , 2004 .

[7]  Gamal ElMasry,et al.  Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. , 2012, Analytica chimica acta.

[8]  Yong He,et al.  [Study on the relationship between spectral properties of oilseed rape leaves and their chlorophyll content]. , 2007, Guang pu xue yu guang pu fen xi = Guang pu.

[9]  Josep Peñuelas,et al.  Visible and near-infrared reflectance techniques for diagnosing plant physiological status , 1998 .

[10]  Gamal ElMasry,et al.  Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef , 2012 .

[11]  K. Barry,et al.  Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling , 2011 .

[12]  B. Rivard,et al.  Spectroscopic determination of leaf water content using continuous wavelet analysis , 2011 .

[13]  C. Petisco,et al.  Measurement of quality parameters in intact seeds of Brassica species using visible and near-infrared spectroscopy , 2010 .

[14]  Roman M. Balabin,et al.  Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. , 2011, The Analyst.

[15]  R. D. Evans,et al.  Physiological mechanisms influencing plant nitrogen isotope composition. , 2001, Trends in plant science.

[16]  D. Major,et al.  DISTRIBUTION OF PHOTOSYNTHATES AFTER 14CO2 ASSIMILATION BY STEMS, LEAVES, AND PODS OF RAPE PLANTS , 1978 .

[17]  L. Hedin.  Global organization of terrestrial plant-nutrient interactions. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Albrecht E. Melchinger,et al.  Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes , 2012 .

[19]  M. R. Holmes.  Nutrition of the Oilseed Rape Crop , 1980 .

[20]  Di Wu,et al.  Exploring near and midinfrared spectroscopy to predict trace iron and zinc contents in powdered milk. , 2009, Journal of agricultural and food chemistry.

[21]  Pengcheng Nie,et al.  Application of Visible and Near Infrared Spectroscopy for Rapid Analysis of Chrysin and Galangin in Chinese Propolis , 2013, Sensors.

[22]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[23]  W. Diepenbrock.  Yield analysis of winter oilseed rape (Brassica napus L.): a review , 2000 .

[24]  M. Andersen,et al.  Development and evaluation of a CERES-type model for winter oilseed rape , 1998 .

[25]  G. F. Sassenrath-Cole,et al.  Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration , 2000 .

[26]  W. Thies,et al.  Contribution of Leaves, Stem, Siliques and Seeds to Dry Matter Accumulation in Ripening Seeds of Rapeseed, Brassica napus L. , 1977 .

[27]  Jiewen Zhao,et al.  In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. , 2011, Analytica chimica acta.

[28]  Consuelo Pizarro,et al.  Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy , 2004 .

[29]  N. Dupuy,et al.  Rapid quantitative determination of oleuropein in olive leaves (Olea europaea) using mid-infrared spectroscopy combined with chemometric analyses , 2012 .

[30]  D. Miralles,et al.  Factors that modify early and late reproductive phases in oilseed rape (Brassica napus L.): Its impact on seed yield and oil content , 2011 .

[31]  A. Guckert,et al.  Influence of sulfur on apparent N-use efficiency, yield and quality of oilseed rape (Brassica napus L.) grown on a calcareous soil , 2000 .

[32]  W. Pettigrew.  Potassium influences on yield and quality production for maize, wheat, soybean and cotton. , 2008, Physiologia plantarum.

[33]  Gamal ElMasry,et al.  Application of NIR hyperspectral imaging for discrimination of lamb muscles , 2011 .

[34]  Min Huang,et al.  Nondestructive determination of nutritional information in oilseed rape leaves using visible/near infrared spectroscopy and multivariate calibrations , 2011, Science China Information Sciences.

[35]  D. Oosterhuis,et al.  CANOPY PHOTOSYNTHESIS, SPECIFIC LEAF WEIGHT, AND YIELD COMPONENTS OF COTTON UNDER VARYING NITROGEN SUPPLY , 2001 .

[36]  B. Mistele,et al.  Estimating the nitrogen nutrition index using spectral canopy reflectance measurements , 2008 .

[37]  Wulf Diepenbrock,et al.  Integrated nitrogen management strategies to improve seed yield, oil content and nitrogen efficiency of winter oilseed rape (Brassica napus L.): A review , 2006 .

[38]  Di Wu,et al.  Application of near infrared spectroscopy for the rapid determination of antioxidant activity of bamboo leaf extract. , 2012, Food chemistry.

[39]  Weixing Cao,et al.  Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[40]  Min-zan Li,et al.  [Analysis and estimation of the phosphorus content in cucumber leaf in greenhouse by spectroscopy]. , 2008, Guang pu xue yu guang pu fen xi = Guang pu.

[41]  Fei Liu,et al.  Classification of brands of instant noodles using Vis/NIR spectroscopy and chemometrics , 2008 .

[42]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[43]  Yidan Bao,et al.  Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. , 2012, Analytica chimica acta.

[44]  Fan Zhang,et al.  Applying Near-Infrared Spectroscopy and Chemometrics to Determine Total Amino Acids in Herbicide-Stressed Oilseed Rape Leaves , 2011 .

[45]  Colm P. O'Donnell,et al.  Identification of mushrooms subjected to freeze damage using hyperspectral imaging. , 2009 .

[46]  W. Zhou,et al.  Genetic analyses of agronomic and seed quality traits of doubled haploid population in Brassica napus through microspore culture , 2006, Euphytica.

[47]  Pengcheng Nie,et al.  Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. , 2010, Analytica chimica acta.

[48]  Min Huang,et al.  [Nitrogen stress measurement of canola based on multi-spectral charged coupled device imaging sensor]. , 2006, Guang pu xue yu guang pu fen xi = Guang pu.

[49]  Rasmus Bro,et al.  Spectral reflectance at sub‐leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression , 2007 .

[50]  Fei Liu,et al.  Determination of acetolactate synthase activity and protein content of oilseed rape (Brassica napus L.) leaves using visible/near-infrared spectroscopy. , 2008, Analytica chimica acta.

[51]  D. Wu,et al.  Short-wave near-infrared spectroscopy of milk powder for brand identification and component analysis. , 2008, Journal of dairy science.