Monitoring Flue-Cured Tobacco Leaf Chlorophyll Content under Different Light Qualities by Hyperspectral Reflectance

Rapid assessment of foliar chlorophyll content in\r\ntobacco is critical for assessment of growth and precise management to improve\r\nquality and yield while minimizing adverse environmental impact. Our objective\r\nis to develop a precise agricultural practice predicting tobacco-leaf chlorophyll-a content. Reflectance experiments have\r\nbeen conducted on flue-cured tobacco over 3 consecutive years under different\r\nlight quality. Leaf hyperspectral reflectance and chlorophyll-a content data have been collected at\r\n15-day intervals from 30 days after transplant until harvesting. We identified\r\nthe central band that is sensitive to tobacco-leaf chlorophyll-a content and the optimum wavelength\r\ncombinations for establishing new spectral indices (simple ratio index, RVI;\r\nnormalized difference vegetation index, NDVI; and simple difference vegetation\r\nindex, DVI). We then established linear and BackPropagation (BP) neural network\r\nmodels to estimate chlorophyll-a content. The central bands for leaf chlorophyll-a content are concentrated in the visible range (410 - 680 nm) in\r\ncombination with the shortwave infrared range (1900 - 2400 nm). The optimum spectral\r\nrange for the spectral band combinations RVI, NDVI, and DVI are 440 and 470 nm, 440 and 470 nm, and 440 and 460 nm, respectively. The\r\nlinear RVI, NDVI, and DVI models, SMLR model and the BP neural network model\r\nhave respective R2 values of 0.76, 0.77, 0.69, 0.78 and 0.86, and\r\nroot mean square error values of 0.63, 1.60, 1.59, 2.04 and 0.05 mg chlorophyll-a/g (fresh weight), respectively. Our\r\nresults identified chlorophyll-a sensitive spectral regions and new indices facilitate a rapid, non-destructive\r\nfield estimation of leaf chlorophyll-a content for tobacco.