Predicting apple sugar content based on spectral characteristics of apple tree leaf in different phenological phases

Abstract

Sugar degree is an important factor in determining the quality of apple. The sugar accumulation in apple fruit is closely related to fruit tree growth and development in different phases. In order to reveal the relationship between tree growth state and apple sugar content, the spectral information of apple tree leaves in different phenological phases was used to predict the fruit sugar degree. The visible and near infrared spectral reflectance of the leaves samples were measured by using a Shimadzu UV-2450 spectrograph, and the sugar content of each fruit sample growing near each leaves sample was collected and measured using laboratory methods. Then two dimensional correlation spectrum analysis was brought in, and the dynamic spectra in different phenological phases were obtained by using sugar contents as the perturbation quantity. Comprehensive observation on the spectral characteristics of leaf samples was conducted much accurately by analyzing two-dimensional correlation spectra of both synchronous and asynchronous. And then the effective spectral response bands of sugar contents and the contribution proportion to fruit sugar accumulation in different periods were investigated. And then, using the contribution proportion of each band as the single-period weighting factor, the fruit sugar sensitive wavebands were acquired. The fruit sugar content was forecasted using the sensitive bands in different phenological phases. After comparing and analyzing, it was found that the model based on parametric optimal solution of SVM showed good accuracy. The calibration R2 of the model reached to 0.8934, the RMSEC was 0.4925 Brix, the validation R2 reached to 0.8805, and its RMSEP was 0.4906 Brix. It reaches to a practical level and can be used to predict the sugar content in apple fruit.