Construction of apple tree leaves nutrients prediction model based on spectral analysis

Abstract: This research aimed at exploring the VIS/NIR (Visible Spectrum/ Near Infra Red) reflectance spectral characteristics of apple tree leaves, and establishing a high-precision model to predict nutrient content for these leaves. Samples were collected from the apple orchard of Beijing Xiangtang culture village during the period of fruit-bearing, fruit-falling and fruit-maturing separately. The apple trees in the orchard were in the full productive age. Twenty apple trees (15 year-on trees and 5 year-off trees) were selected randomly from different regions. Then a main branch of each target tree was selected and three representative parts (base part, middle part and top part) of every bough were marked, and the leaves from the same representative part were considered as one sample. In the end, 60 samples of apple leaves were collected in each phenological period, and the visible and near infrared spectral reflectance were measured using Shimadzu UV-2450 spectrograph. At the same time, the chlorophyll content for each sample was detected using spectrophotometry and the nitrogen content of each sample was measured using the Kjeldahl method in the laboratory. The obtained spectral reflectance and nutrient content were clustered based on each bough individually. The first through seventh layer wavelet decompositions were done to the original spectrum respectively. It can be perceived that with the decomposition scale increasing, the curve became smoother because of eliminating the impact of random noise, while some valid information was lost at the same time. According to the correlation analysis, this study selected 3-layer db4 wavelet filtering spectral information to predict the nitrogen and chlorophyll content. After that, correlation analyses were conducted between: 1) the chlorophyll content of apple tree leaves and their spectral reflectance; 2) the chlorophyll content of apple tree leaves and their spectral reflectance under wavelet filtering; 3) the nitrogen content of apple tree leaves and their spectral reflectance; and 4) the nitrogen content of apple tree leaves and their spectral reflectance under wavelet filtering. Then, the regression models for predicting nitrogen content and chlorophyll content of apple tree leaves were established using PLS (Partial Least Square) and SVM (Support Vector Machine) methods, respectively, based on the above spectral signal. The results indicated that: 1) with the advance of growth stage, the reflectance at visible waveband increased gradually, while at the near infrared waveband, the reflectance decreased gradually; 2) wavelet analyzing technology could distinguish the mutation part and noise in the spectral signals effectively, which make it possible to retain the maximum amount of effective information during the signal denoising process. The wavelet filtering technology played a significant role in promoting the modeling accuracy in predicting the Chlorophyll; 3) the models based on the SVM method had higher accuracies; 4) for the Chlorophyll regression model based on the spectral reflectance under wavelet filtering, the calibration R2 reached to 0.9841, RMSEC was 0.0039, and the validation R2 of reached to 0.9036, RMSEP was 0.0567; and 5) for the nitrogen regression model, the R2 of calibration and validation model were all above 0.74, RMSEC was 0.0554 and RMSEP was 0.1215. It was concluded that the chlorophyll SVM regression model reached a high accuracy, and the nitrogen SVM regression model also reached the practical level with high stability.