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 dev...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-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. Reflectance experiments have been conducted on flue-cured tobacco over 3 consecutive years under different light quality. Leaf hyperspectral reflectance and chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> 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-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> 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-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. The central bands for leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> 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 combinations</span><span style="font-family:Verdana;"> RVI, NDVI, and DVI</span><span style="font-family:Verdana;"> 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 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> values of 0.76, 0.77, 0.69, 0.展开更多
文摘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-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. Reflectance experiments have been conducted on flue-cured tobacco over 3 consecutive years under different light quality. Leaf hyperspectral reflectance and chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> 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-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> 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-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> content. The central bands for leaf chlorophyll-</span><i><span style="font-family:Verdana;">a</span></i><span style="font-family:Verdana;"> 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 combinations</span><span style="font-family:Verdana;"> RVI, NDVI, and DVI</span><span style="font-family:Verdana;"> 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 R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> values of 0.76, 0.77, 0.69, 0.
