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Soil nutrient information extraction model based on transfer learning and near infrared spectroscopy

文献类型： 外文期刊

作者： Cai, Hao-Tian 1 ; Liu, Jun 1 ; Chen, Jie-Ying 2 ; Zhou, Ke-Hong 3 ; Pi, Jie 1 ; Xia, Li-Ru 1 ;

作者机构： 1.Minist Agr, Inst Agr Facil & Equipment, Jiangsu Acad Agr Sci, Key Lab Protected Agr Engn Middle & Lower Reaches, Nanjing 210014, Peoples R China

2.Jiangsu Yinbao Agr Sci Res Inst Co Ltd, Nanjing 210014, Peoples R China

3.Jiangsu Univ, Sch Agr Equipment Engn, Zhenjiang 212013, Jiangsu, Peoples R China

关键词： Transfer learning; Near-infrared spectroscopy; Soil nutrient; Information extraction model; Straw method; Principal component analysis

期刊名称：ALEXANDRIA ENGINEERING JOURNAL （ 影响因子：2.46； ）

ISSN： 1110-0168

年卷期： 2021 年 60 卷 3 期

页码：

收录情况： SCI

摘要： This study aims to combine the transfer learning algorithm and near-infrared spectroscopy technology to build a soil nutrient information extraction model. We determine the soil nutrients with near-infrared spectroscopy technology; moreover, the characteristic bands of soil nutrients were also determined for preprocessing the soil nutrient information. Then, combined with the transfer learning algorithm, soil nutrient information extraction model was constructed. Simulation results showed that the proposed model could improve the efficiency of soil nutrient information extraction, and also obtain high-confidence feature extraction results. This will solve the problem of low-accuracy and efficiency of soil nutrient information extraction through traditional models. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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