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Jia F.,Henan Agricultural University | Liu G.,Henan Agricultural University | Ding S.,Henan Agricultural University | Yang Y.,Henan Agricultural University | And 2 more authors.
Industrial Crops and Products | Year: 2013

The objectives of this study were to determine the relationship between tobacco leaf nicotine content and leaf spectral reflectance, to identify specific regions of the light spectrum that could be used to detect nicotine content, and to develop spectral indices and quantitative models for quick and accurate estimation of nicotine content in flue-cured tobacco leaves under different shade conditions. The flue-cured tobacco cultivar Yunyan 87 was subjected to shade treatments in the field extending from the root elongation stage through the vigorous growth and maturing stages. Three shade treatments of 85% (S1), 65% (S2), and 45% (S3) of full solar radiation (S0, control) were applied and the nicotine, pigment, and nitrogen content and spectral reflectance of tobacco leaves were measured over a time course. Normalized difference vegetation indices (NDVI) and spectral ratio (SR) indices based on leaf reflectance spectra from 350 to 2500nm and correlations with nicotine content were determined. Shading significantly increased the nicotine content during the maturing stage. Significant differences in reflectance were measured for different shade treatments during the vigorous growth stage particularly at 350-700nm in the visible range and 750-1000nm in the near-infrared range. There was a significant correlation between nicotine content and nitrogen and pigment content. The regions of the spectrum that gave the best indication of nicotine content in flue-cured tobacco leaves were 420-750nm in the visible range in combination with 1400-1800nm and 2000-2400nm in the short-wave infrared range. Optimal spectral indices for SR (R450, R500) and NDVI (R2150, R610) were derived from measurements in these ranges. We also established linear models based on the spectral indices derived from field data gathered in 2012 for NDVI (R2150, R610), SR (450, 500), stepwise multiple linear regression (SMLR), and a back-propagation (BP) neural network with R2 values of 0.796, 0.810, 0.842, and 0.968, respectively. The linear models were validated using an independent data set from 2011 with RMSE values of 0.784, 0.958, 0.883, and 0.109 for NDVI (R2150, R610), SR (450, 500), SMLR, and the BP neural network, respectively. The results indicated that hyperspectral remote sensing can be used for quick and accurate monitoring of the leaf nicotine content and shading status of flue-cured tobacco crops. © 2013. Source


Jia F.,Henan Agricultural University | Liu G.,Henan Agricultural University | Liu D.,Henan Agricultural University | Zhang Y.,Henan Agricultural University | And 2 more authors.
Field Crops Research | Year: 2013

Leaf nitrogen content (LNC) is an important indicator of tobacco quality and is used in the prediction of tobacco yield. Reflectance experiments for flue-cured tobacco were conducted over 2 consecutive years. Leaf hyperspectral reflectance and nitrogen content data were collected at 15-day intervals from 30 days after transplant until harvest. In this work, we identified the central band that sensitive to tobacco LNC and the optimum combination to establish new spectral indices (SR and NDVI), which were used in linear models of the specific ratio vegetation index (SR), normalized difference vegetation index (NDVI), stepwise multiple linear regression (SMLR), and back-propagation (BP) neural network models as independent variable or input factors. The central bands for the LNC were concentrated in the visible range (450-750nm) in combination with the shortwave infrared range (1450-2500nm) range. The optimum band combinations for SR and NDVI were (590 and 1980nm) and (1970 and 650nm), respectively. The BP neural network model was the most stable and accurate model (R2=0.91, RMSE=0.09, and K-=0.00). The SR, NDVI, and SMLR models had R2 values of 0.77, 0.76, and 0.86; RMSE values of 0.26, 0.51, and 0.60, and K- values of 0.05, 0.11, and 0.14, respectively. The results indicate the possibility of monitoring LNC by combining remote sensing with predictive models. © 2013. Source

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