Abstract
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.
Highlights
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 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
The multi-linear regression model was established by using hyperspectral image information, and the chlorophyll content distribution map was drawn, indicating that non-destructive testing was performed by hyperspectral image technology
Summary
Hyperspectral imaging technology can be used to obtain spectral information and image information, and chlorophyll content distribution inversion [9] [10] [11] [12]. Hyperspectral imaging technology combines the advantages of both spectroscopy and image It has the characteristics of high resolution, multi-band, and map integration. It can detect the appearance characteristics and internal components of objects, and can utilize the multi-band spectrum to the content of plant nutrients. This study carried out the estimation and visualization of chlorophyll content in apple leaves, and used hyperspectral imaging technology to obtain the changes of leaf nutrient status during apple growth, which could provide technical support for precise fertilization management
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