Development of a mobile online detector for soil total nitrogen based on visible and short-wave near-infrared spectroscopy

IF 5.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Peng Zhou , Yixiang Gu , Chengqian Jin , Yangxin Zhu , Yazhou Ou , Yinuo Kong , Xiang Yin , Shanshan Hao
{"title":"Development of a mobile online detector for soil total nitrogen based on visible and short-wave near-infrared spectroscopy","authors":"Peng Zhou ,&nbsp;Yixiang Gu ,&nbsp;Chengqian Jin ,&nbsp;Yangxin Zhu ,&nbsp;Yazhou Ou ,&nbsp;Yinuo Kong ,&nbsp;Xiang Yin ,&nbsp;Shanshan Hao","doi":"10.1016/j.measurement.2025.116933","DOIUrl":null,"url":null,"abstract":"<div><div>To develop a soil total nitrogen (STN) detection equipment with high accuracy, strong adaptability, and low cost, this study presents an innovative approach and successfully develops a mobile online STN detector based on visible and short-wave near-infrared (VIS-SWNIR) spectroscopy. The equipment mainly consists of optical system, mechanical system and control system. Different from most of the previous devices, it no longer relies on special optics. Instead, the equipment uses self-developed software to obtain the soil characteristic spectral data through real-time soil spectral extraction and invokes the STN model to obtain the detection value. A dataset comprising spectral reflectance and STN was constructed using 100 soil samples for model training. The Multiplicative Scatter Correction, First Derivative, and Competitive Adaptive Reweighted Sampling algorithms are selected to construct the PLSR model, based on the comparison of the modeling results from 10 preprocessing forms and 3 characteristic wavelength selection algorithms. The coefficient of determination for calibration and validation are 0.8767 and 0.8366, respectively. The Root Mean Square Error of calibration and validation are 0.0229 and 0.0298, respectively. To ensure the accuracy and stability of the equipment, appropriate tests are conducted on it in the laboratory. According to the results, during continuous operation for 2 h, the collection error of the detector’s characteristic wavelength reflectance remains within 1.80%. The self-developed software demonstrates sensitive responsiveness and strong stability. Moreover, the equipment exhibited strong performance in laboratory sample testing and field simulation tests, achieving correlation coefficients of 0.8862 and 0.8780, respectively, between measured and true values.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"248 ","pages":"Article 116933"},"PeriodicalIF":5.2000,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263224125002921","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

To develop a soil total nitrogen (STN) detection equipment with high accuracy, strong adaptability, and low cost, this study presents an innovative approach and successfully develops a mobile online STN detector based on visible and short-wave near-infrared (VIS-SWNIR) spectroscopy. The equipment mainly consists of optical system, mechanical system and control system. Different from most of the previous devices, it no longer relies on special optics. Instead, the equipment uses self-developed software to obtain the soil characteristic spectral data through real-time soil spectral extraction and invokes the STN model to obtain the detection value. A dataset comprising spectral reflectance and STN was constructed using 100 soil samples for model training. The Multiplicative Scatter Correction, First Derivative, and Competitive Adaptive Reweighted Sampling algorithms are selected to construct the PLSR model, based on the comparison of the modeling results from 10 preprocessing forms and 3 characteristic wavelength selection algorithms. The coefficient of determination for calibration and validation are 0.8767 and 0.8366, respectively. The Root Mean Square Error of calibration and validation are 0.0229 and 0.0298, respectively. To ensure the accuracy and stability of the equipment, appropriate tests are conducted on it in the laboratory. According to the results, during continuous operation for 2 h, the collection error of the detector’s characteristic wavelength reflectance remains within 1.80%. The self-developed software demonstrates sensitive responsiveness and strong stability. Moreover, the equipment exhibited strong performance in laboratory sample testing and field simulation tests, achieving correlation coefficients of 0.8862 and 0.8780, respectively, between measured and true values.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Measurement
Measurement 工程技术-工程:综合
CiteScore
10.20
自引率
12.50%
发文量
1589
审稿时长
12.1 months
期刊介绍: Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信