文献类型：期刊文献

英文题名：Extraction of soil nutrient information from visible and near-infrared signals using deep learning models

作者：Xiong, Chunru[1];Hu, Jufang[2];Cai, Ken[3];Meng, Fangxiu[4];Lin, Qinyong[3];Chen, Huazhou[4]

机构：[1]Guangdong Ocean Univ, Sch Comp Sci & Engn, Yangjiang 529500, Peoples R China;[2]Guangdong Ocean Univ, Sch Mech & Energy Engn, Yangjiang 529500, Peoples R China;[3]Zhongkai Univ Agr & Engn, Coll Automat, Guangzhou 510225, Peoples R China;[4]Guilin Univ Technol, Ctr Data Anal & Algorithm Technol, Sch Math & Stat, Guilin 541004, Peoples R China

年份：2026

卷号：268

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001616031000001)、、WOS

基金：This work was supported by National Natural Science Foundation of China (62365008) , Special Projects in Key Areas of Guangdong's Col-leges, China (2022ZDZX3007, 2023ZDZX4039) , Guangzhou Science and Technology Program (202201011274) , Research Project Initiation Fund of Guangdong Ocean University (360302042202) .

语种：英文

外文关键词：Soil nutrient; Information extraction; Visible and near-infrared (Vis-NIR) spectroscopy; Long Short-term memory (LSTM); Influence function; Dimension reduction; Dimension reduction

外文摘要：This study aims to combine the deep learning algorithm and the visible and near-infrared (Vis-NIR) spectroscopy technology to build a soil nutrient information extraction model. A deep learning framework based on Long Short-Term Memory (LSTM) is proposed to establish optimal calibration model for the analysis of the full range of Vis-NIR spectral data. Moreover, an influence function is designed to select the informative wavelength variables, which is an important goal in engineering application of spectroscopy for reducing the model dimensionality and enhancing model robustness. Experiment was performed for the prediction of nitrogen (N), phosphorus (P) and potassium (K) contents of soil. The modeling results showed that the proposed model could improve the modeling efficiency of soil nutrient information extraction, and also obtained higher accuracy in the modeling and predictive procedures than the conventional model. This will provide effective response to the challenges in engineering applications, to promote the Vis-NIR spectroscopy technology be applied for fast detection, and to obtain robust models with high precisions in soil nutrient information extraction process.