Author(s)

Fangfang Jia^1,2*, Shuang Han¹, Dong Chang³, Haitao Yan³, Yueqi Xu³, Wenna Song¹

¹School of Life Sciences, Shangqiu Normal University, Shangqiu, China.
²School of Information Engineering, Zhengzhou University, Zhengzhou, China.
³Henan Pingdingshan Municipal Tobacco Company, Pingdingshan, China.

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 combinations RVI, 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.

Chlorophyll-a, Light Quality, Hyperspectral Reflectance, Error BackPropagation Neural Networks, Factorial Experimental Design

Jia, F. , Han, S. , Chang, D. , Yan, H. , Xu, Y. and Song, W. (2020) Monitoring Flue-Cured Tobacco Leaf Chlorophyll Content under Different Light Qualities by Hyperspectral Reflectance. American Journal of Plant Sciences, 11, 1217-1234. doi: 10.4236/ajps.2020.118086.