Monitoring of Nitrogen Transport Data in Pear Leaves Based on Infrared Spectroscopy

Abstract

In order to better monitor the data of nitrogen transport in pear leaves, a method based on infrared spectroscopy was proposed. The near-infrared reflection spectrum imaging technology is used to collect the leaf scale spectral image of the target crop. Computer image analysis software is used to process the spectral digital image and extract the spectral data. After statistical analysis, the data are selected as variables. Combined with the chemical analysis test results, the crop nutrition detection model is established, and the conclusion is drawn. The experimental results show that the band gray data involved in the model are scaled and reorganized according to the coefficient proportion by using ENVI through the band calculation command. The final gray image, the original image, and the gray image in the process default to the three-channel analog image of the band (the wavelengths of the bands are 1446, 1373, and 1304 nm, respectively); 944 nm gray image; 1043 nm gray scale image; 1662 nm gray image; (0.102R944 +0.103R1 043 +0.206R1662)/(0.102 + 0.103 + 0.206) grayscale image with signal scaling according to the scale of model coefficient. It is proved that infrared spectroscopy can effectively monitor the data of nitrogen transport in pear leaves.