关键词: 磷含量, 橡胶树叶片, 自编码器, 前馈神经网络, 分类

Abstract: Phosphorus is very important for the growth and development of rubber trees. There are many studies on the rapid non-destructive detection of nitrogen, phosphorus and potassium content of rubber trees based on near infrared spectroscopy, but the detection effect of phosphorus content is far less than that of nitrogen and potassium. Based on near infrared hyperspectral technology, combined with nonlinear feature extraction method and modeling algorithm, the rapid non-destructive detection of phosphorus content in rubber tree was realized. The near-infrared hyperspectral data of rubber leaves were taken as the analysis object, and the feature extraction idea of neural network nonlinear transformation was used to construct a model fusing autoencoder and feedforward neural network (AE-FFNN). The nonlinear spectral feature information of rubber leaves was extracted by autoencoder, and the model was established by using feedforward neural network to deal with classification tasks with different levels of fineness to realize the qualitative analysis of phosphorus content in rubber leaves. The results showed that AE-FFNN model could effectively extract spectral nonlinear features and compress the number of features. 31 features were extracted by this method, and the accuracy of qualitative analysis model was improved, which reached to 91.10%. Compared with the machine learning model widely used in the field of spectral detection, the performance of the AE-FFNN model was greatly improved. The model could be used not only for qualitative analysis of phosphorus content in rubber leaves, but also for quantitative study of phosphorus content.

Key words: phosphorus, rubber tree leaves, autoencoder, feedforward neural network, classification