TITLE:
Monitoring Flue-Cured Tobacco Leaf Chlorophyll Content under Different Light Qualities by Hyperspectral Reflectance
AUTHORS:
Fangfang Jia, Shuang Han, Dong Chang, Haitao Yan, Yueqi Xu, Wenna Song
KEYWORDS:
Chlorophyll-a, Light Quality, Hyperspectral Reflectance, Error BackPropagation Neural Networks, Factorial Experimental Design
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.11 No.8,
August
17,
2020
ABSTRACT: Rapid assessment of foliar chlorophyll content in
tobacco is critical for assessment of growth and precise management to improve
quality and yield while minimizing adverse environmental impact. Our objective
is to develop a precise agricultural practice predicting tobacco-leaf chlorophyll-a content. Reflectance experiments have
been conducted on flue-cured tobacco over 3 consecutive years under different
light quality. Leaf hyperspectral reflectance and chlorophyll-a content data have been collected at
15-day intervals from 30 days after transplant until harvesting. We identified
the central band that is sensitive to tobacco-leaf chlorophyll-a content and the optimum wavelength
combinations for establishing new spectral indices (simple ratio index, RVI;
normalized difference vegetation index, NDVI; and simple difference vegetation
index, DVI). We then established linear and BackPropagation (BP) neural network
models to estimate chlorophyll-a content. The central bands for leaf chlorophyll-a content are concentrated in the visible range (410 - 680 nm) in
combination with the shortwave infrared range (1900 - 2400 nm). The optimum spectral
range for the spectral band combinationsRVI, NDVI, and DVI are 440 and 470 nm, 440 and 470 nm, and 440 and 460 nm, respectively. The
linear RVI, NDVI, and DVI models, SMLR model and the BP neural network model
have respective R2 values of 0.76, 0.77, 0.69, 0.78 and 0.86, and
root mean square error values of 0.63, 1.60, 1.59, 2.04 and 0.05 mg chlorophyll-a/g (fresh weight), respectively. Our
results identified chlorophyll-a sensitive spectral regions and new indices facilitate a rapid, non-destructive
field estimation of leaf chlorophyll-a content for tobacco.