Fusion of Convolutional Neural Network with Xgboost Feature Extraction for Predicting Multi-Constituents in Corn Using Near Infrared Spectroscopy

作     者：Zou, Xin Wang, Qiaoyun Chen, Yinji Wang, Jilong Xu, Shunyuan Zhu, Ziheng Yan, Chongyue Shan, Peng Wang, Shuyu Fu, YongQing 

作者机构：College of Information Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Faculty of Engineering & Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Feature extraction 

摘      要：Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band, overlapping and non-specific nature. To solve these problems and extract implicit features from the raw data of NIR spectra and improve performance of quantitative models, a one-dimensional shallow convolutional neural network (CNN) model based on an XGBoost feature extraction method was proposed in this paper. The leaf node feature information in the XGBoost was encoded and reconstructed to obtain the implicit features of raw data in the NIR spectra. A two-parametric Swish (TSwish or TS) activation function was proposed to improve the performance of CNN, and the elastic net (EN) was also applied to avoid the overfitting problem of the CNN model. Performance of the developed XGBoost-CNN-TS-EN model was evaluated using two public NIR spectroscopy datasets of corn and soil, and the obtained determination coefficients (R2) for moisture, oil, protein, and starch of the corn were 0.993, 0.991, 0.998, and 0.992, respectively, with that of the soil organic matter being 0.992. The XGBoost-CNN-TS-EN model exhibits superior stability, good prediction accuracy, and generalization ability, demonstrating its great potentials for quantitative analysis of multi-constituents in spectroscopic applications. © 2024, The Authors. All rights reserved.