{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T13:50:16Z","timestamp":1769003416022,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,5,11]],"date-time":"2017-05-11T00:00:00Z","timestamp":1494460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.<\/jats:p>","DOI":"10.3390\/s17051102","type":"journal-article","created":{"date-parts":[[2017,5,11]],"date-time":"2017-05-11T11:06:46Z","timestamp":1494500806000},"page":"1102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms"],"prefix":"10.3390","volume":"17","author":[{"given":"Pengcheng","family":"Nie","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"},{"name":"State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7432-8899","authenticated-orcid":false,"given":"Tao","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangfang","family":"Qu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2330","DOI":"10.1080\/00103624.2010.508097","article-title":"Influence of soil particle size on the measurement of sodium by near-infrared reflectance spectroscopy","volume":"41","author":"Cozzolino","year":"2010","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.5513\/JCEA01\/15.2.1465","article-title":"Role of soil properties in water retention characteristics of main Hungarian soil types","volume":"15","author":"Gergely","year":"2014","journal-title":"J. Cent. Eur. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"936","DOI":"10.2136\/sssaj2011.0377","article-title":"Prediction of Soil Nitrogen Supply in Potato Fields using Soil Temperature and Water Content Information","volume":"76","author":"Zebarth","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_4","first-page":"86","article-title":"Evaluation of environmental and economic effects of soil testing and fertilizer application project","volume":"1","author":"Luo","year":"2014","journal-title":"J. Huazhong Agric. Univ. (Soc. Sci. Ed.)"},{"key":"ref_5","first-page":"447","article-title":"Application of near infrared spectroscopy in agriculture and food analysis","volume":"24","author":"Wang","year":"2004","journal-title":"J. Spectrosc. Spectr. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1016\/j.soilbio.2007.12.023","article-title":"Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples","volume":"40","author":"Barthes","year":"2008","journal-title":"Soil Biol. Biochem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geoderma.2010.03.001","article-title":"Comparison among principal component, Comparison between principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy","volume":"158","author":"Mouazen","year":"2008","journal-title":"Geoderma"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.biosystemseng.2013.01.005","article-title":"Non-biased prediction of soil organic carbon and inorganic nitrogen with vis\u2013NIR spectroscopy, as affected by soil moisture content and texture","volume":"114","author":"Kuang","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s11104-012-1436-8","article-title":"Comparison of multivariate methods for estimating soil inorganic nitrogen;with visible\/near-infrared spectroscopy","volume":"366","author":"Shi","year":"2013","journal-title":"Plant Soil"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.orggeochem.2012.02.006","article-title":"Spectral fingerprinting of soil organic matter composition","volume":"46","author":"Certini","year":"2012","journal-title":"Org. Geochem."},{"key":"ref_11","first-page":"62","article-title":"Prediction of soil spectral characteristics and nitrogencontent","volume":"27","author":"Bao","year":"2007","journal-title":"J. Spectrosc. Spectr. Anal."},{"key":"ref_12","first-page":"150","article-title":"Real time detection method of near infrared spectral content of inorganic nitrogen and inorganic phosphorus in soil","volume":"40","author":"Yuan","year":"2009","journal-title":"J. Agric. Mach."},{"key":"ref_13","first-page":"111","article-title":"Prediction of soil organic matter based on visible\/near infrared spectroscopy","volume":"S1","author":"Han","year":"2010","journal-title":"J. Chin. Sci. Inf. Sci."},{"key":"ref_14","first-page":"121","article-title":"Prediction of stratified soil nitrogen content in near infrared spectral analysis","volume":"31","author":"Zhang","year":"2015","journal-title":"J. Agric. Eng."},{"key":"ref_15","first-page":"1240","article-title":"Analysis of the effect of moisture on soil spectra detection by using two-dimensional correlation near infrared spectroscopy","volume":"34","author":"Song","year":"2014","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1109\/JSTARS.2015.2450775","article-title":"Effects of Soil Surface Roughness on Soil Reflectance Measured in Laboratory and Outdoor Conditions","volume":"9","author":"Piekarczyk","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.jfoodeng.2005.06.036","article-title":"Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis\/NIR-spectroscopy techniques","volume":"77","author":"Hernandez","year":"2006","journal-title":"J. Food Eng."},{"key":"ref_18","first-page":"3249","article-title":"Spectral characteristics analysis of water content on soil organic matter and establishment of water resistance model","volume":"36","author":"Wang","year":"2016","journal-title":"J. Spectrosc. Spectr. Anal."},{"key":"ref_19","first-page":"167","article-title":"Study on the effect of water content on the content of organic matter in purple soil by near infrared spectroscopy","volume":"10","author":"Zeng","year":"2015","journal-title":"J. Southwestern Univ. (Nat. Sci. Ed.)"},{"key":"ref_20","first-page":"40","article-title":"Study on the effect of soil water content on soil nitrogen content by near infrared spectroscopy","volume":"11","author":"Zhu","year":"2016","journal-title":"Sci. Technol. Innov. Appl."},{"key":"ref_21","first-page":"29","article-title":"Influences of Soil Particle Size Difference on Detecting Inorganic Nitrogen Contents in Soil by Spectrometry","volume":"4","author":"Zhu","year":"2015","journal-title":"J. Tianjin Agric. Univ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.jpba.2007.03.023","article-title":"A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies","volume":"44","author":"Roggo","year":"2007","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_23","first-page":"702","article-title":"For the research and application progress of chemometrics methods near infrared spectroscopy analysis","volume":"36","author":"Zhu","year":"2008","journal-title":"J. Anal. Chem."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1002\/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7","article-title":"Use of partial least squares (PLS) in strategic management research: A review of four recent studies","volume":"20","author":"Hulland","year":"2015","journal-title":"Strateg. Manag. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.vibspec.2006.11.005","article-title":"Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of \u2018Fuji\u2019 apple based on BiPLS and FiPLS models","volume":"44","author":"Zou","year":"2007","journal-title":"Vib. Spectrosc."},{"key":"ref_26","first-page":"262","article-title":"The local effect of partial least squares regression modeling parameters on near infrared detection results of","volume":"02","author":"Li","year":"2007","journal-title":"J. Spectrosc. Spectr. Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3851","DOI":"10.1021\/ac960321m","article-title":"Elimination of uninformative variables for multivariate calibration","volume":"68","author":"Centner","year":"1996","journal-title":"Anal. Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/cem.1192","article-title":"Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression","volume":"23","author":"Teofilo","year":"2009","journal-title":"J. Chemom."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1007\/s11947-010-0492-4","article-title":"Determination of calcium content in powdered milk using near and mid-infrared spectroscopy with variable selection and chemometrics","volume":"5","author":"Wu","year":"2012","journal-title":"J. Food Bioprocess Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jksuci.2013.01.001","article-title":"Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm","volume":"26","author":"Parvinnia","year":"2014","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.aca.2014.12.048","article-title":"Using variable combination population analysis for variable selection in multivariate calibration","volume":"862","author":"Yun","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1039\/C5AN01656H","article-title":"The Monte Carlo validation framework for the discriminant partial least squares model extended with variable selection methods applied to authenticity studies of Viagra\u00ae based on chromatographic impurity profiles","volume":"141","author":"Krakowska","year":"2016","journal-title":"Analyst"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_34","first-page":"83","article-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","volume":"27","author":"Franklin","year":"2010","journal-title":"J. R. Stat. Soc. Ser. A Stat. Soc."},{"key":"ref_35","first-page":"32","article-title":"Introduction to statistical learning theory and support vector machines","volume":"26","author":"Zhang","year":"2000","journal-title":"Acta Autom. Sin."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/5\/1102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:35:26Z","timestamp":1760207726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/5\/1102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,11]]},"references-count":35,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2017,5]]}},"alternative-id":["s17051102"],"URL":"https:\/\/doi.org\/10.3390\/s17051102","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,11]]}}}