Author(s)

Xin Wen¹, Xicun Zhu^1,2*, Ruiyang Yu¹, Jingling Xiong¹, Dongsheng Gao³, Yuanmao Jiang³, Guijun Yang⁴

¹College of Resources and Environment, Shandong Agricultural University, Tai’an, China.
²Key Laboratory of Agricultural Ecology and Environment, Shandong Agricultural University, Tai’an, China.
³College of Horticulture Science and Engineering, Shandong Agricultural University, National Apple Engineering and Technology Research Center, Tai’an, China.
⁴National Engineering Research Center for Information Technology in Agriculture, Beijing, China.

We took distribution visualization of chlorophyll content in apple leaves to estimate the nutrient content and growth levels of apple leaves. 130 mature and non-destructive apple leaves were collected, and imaging spectroscopy data were collected by SOC710VP hyperspectral imager. The chlorophyll content of the leaves was determined on the spectral information of the leaves. After pre-processing, we took linear wavelength stepwise regression method to choose the sensitive wavelength of chlorophyll content. And then we established partial least squares, principal component analysis and stepwise regression model. Finally, the chlorophyll content distribution visualization was realized. The results showed that the sensitive wavelengths of the chlorophyll content were 712.50 nm, 509.95 nm, 561.22 nm, 840.62 nm, 696.67 nm and 987.91 nm. The R2, RMSE, RE of the optical chlorophyll content estimation model, and the principal component analysis regression model, were 0.800, 0.319 and 26.4%. The chlorophyll content of each pixel on the hyperspectral image of apple leaves was calculated by the best estimation model and we completed the visualization distribution of chlorophyll content, which provided a technical support for the rapid detection of nutrient distribution.

Apple Leaves, Chlorophyll Content, Hyperspectral, Visualization

Wen, X. , Zhu, X. , Yu, R. , Xiong, J. , Gao, D. , Jiang, Y. and Yang, G. (2019) Visualization of Chlorophyll Content Distribution in Apple Leaves Based on Hyperspectral Imaging Technology. Agricultural Sciences, 10, 783-795. doi: 10.4236/as.2019.106060.