{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T21:35:37Z","timestamp":1777930537422,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2019YFE011962"],"award-info":[{"award-number":["2019YFE011962"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Funds","award":["31471409"],"award-info":[{"award-number":["31471409"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visible-near-infrared spectrum (Vis-NIR) spectroscopy technology is one of the most important methods for non-destructive and rapid detection of soil total nitrogen (STN) content. In order to find a practical way to build STN content prediction model, three conventional machine learning methods and one deep learning approach are investigated and their predictive performances are compared and analyzed by using a public dataset called LUCAS Soil (19,019 samples). The three conventional machine learning methods include ordinary least square estimation (OLSE), random forest (RF), and extreme learning machine (ELM), while for the deep learning method, three different structures of convolutional neural network (CNN) incorporated Inception module are constructed and investigated. In order to clarify effectiveness of different pre-treatments on predicting STN content, the three conventional machine learning methods are combined with four pre-processing approaches (including baseline correction, smoothing, dimensional reduction, and feature selection) are investigated, compared, and analyzed. The results indicate that the baseline-corrected and smoothed ELM model reaches practical precision (coefficient of determination (R2) = 0.89, root mean square error of prediction (RMSEP) = 1.60 g\/kg, and residual prediction deviation (RPD) = 2.34). While among three different structured CNN models, the one with more 1 \u00d7 1 convolutions preforms better (R2 = 0.93; RMSEP = 0.95 g\/kg; and RPD = 3.85 in optimal case). In addition, in order to evaluate the influence of data set characteristics on the model, the LUCAS data set was divided into different data subsets according to dataset size, organic carbon (OC) content and countries, and the results show that the deep learning method is more effective and practical than conventional machine learning methods and, on the premise of enough data samples, it can be used to build a robust STN content prediction model with high accuracy for the same type of soil with similar agricultural treatment.<\/jats:p>","DOI":"10.3390\/s20247078","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T08:59:34Z","timestamp":1607590774000},"page":"7078","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5506-8828","authenticated-orcid":false,"given":"Yueting","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minzan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronghua","family":"Ji","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, J., L\u00fc, S., Zhang, Z., Zhao, X., Li, X., Ning, P., and Liu, M. (2018). Environmentally friendly fertilizers: A review of materials used and their effects on the environment. Sci. Total. Environ., 829\u2013839.","DOI":"10.1016\/j.scitotenv.2017.09.186"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1007\/s12155-016-9763-x","article-title":"Nitrogen Use Efficiency for Sugarcane-Biofuel Production: What Is Next?","volume":"9","author":"Otto","year":"2016","journal-title":"BioEnergy Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.tifs.2017.08.013","article-title":"Hyperspectral imaging technique for evaluating food quality and safety during various processes: A review of recent applications","volume":"69","author":"Liu","year":"2017","journal-title":"Trends Food Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kwan, C. (2018). Remote Sensing Performance Enhancement in Hyperspectral Images. Sensors, 18.","DOI":"10.3390\/s18113598"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.agwat.2013.12.012","article-title":"Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation","volume":"135","author":"Iqbal","year":"2014","journal-title":"Agric. Water Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.jfoodeng.2013.04.027","article-title":"Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique","volume":"118","author":"Jie","year":"2013","journal-title":"J. Food Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111071","DOI":"10.1016\/j.postharvbio.2019.111071","article-title":"Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms","volume":"161","author":"Tian","year":"2020","journal-title":"Postharvest Biol. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sun, X., Xu, S., and Huazhong, L. (2020). Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. Appl. Sci., 10.","DOI":"10.3390\/app10165399"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/JSTARS.2014.2315875","article-title":"Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104943","DOI":"10.1016\/j.compag.2019.104943","article-title":"Monitoring plant diseases and pests through remote sensing technology: A review","volume":"165","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12653","DOI":"10.1029\/JB095iB08p12653","article-title":"High spectral resolution reflectance spectroscopy of minerals","volume":"95","author":"Clark","year":"1990","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_12","first-page":"1549","article-title":"Application of wavelet packet analysis in estimating soil parameters based on NIR spectra","volume":"29","author":"Zheng","year":"2009","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_13","first-page":"1160","article-title":"Estimation of soil organic matter and soil total nitrogen based on NIR spectroscopy and BP neural network","volume":"28","author":"Zheng","year":"2008","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0003-2670(01)01265-X","article-title":"Possibilities of visible\u2013near-infrared spectroscopy for the assessment of soil contamination in river floodplains","volume":"446","author":"Kooistra","year":"2001","journal-title":"Anal. Chim. Acta"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.geoderma.2008.04.007","article-title":"Comparison of multivariate methods for inferential modeling of soil carbon using visible\/near-infrared spectra","volume":"146","author":"Vasques","year":"2008","journal-title":"Geoderma"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Veum, K., Parker, P.A., Sudduth, K.A., and Holan, S.H. (2018). Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization. Sensors, 18.","DOI":"10.3390\/s18113869"},{"key":"ref_17","first-page":"21","article-title":"Use of VIS-NIRS for land management classification with a support vector machine and prediction of soil organic carbon and other soil properties","volume":"41","author":"Debaene","year":"2014","journal-title":"Cienc. E Investig. Agrar."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compag.2019.03.015","article-title":"Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors","volume":"160","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","first-page":"271","article-title":"Soil Total nitrogen content pre- diction based on gray correlation-extreme learning machine","volume":"48","author":"Zhou","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, H., Jia, S., and Le, Z. (2019). Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine. Sensors, 19.","DOI":"10.3390\/s19204355"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.aca.2019.01.002","article-title":"DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis","volume":"1058","author":"Zhang","year":"2019","journal-title":"Anal. Chim. Acta"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"615","DOI":"10.5194\/isprs-annals-IV-2-W5-615-2019","article-title":"Soil texture classification with 1D convolutional neural networks based on hyperspectral data","volume":"4","author":"Riese","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.aca.2016.08.022","article-title":"Boosting model performance and interpretation by entangling preprocessing selection and variable selection","volume":"938","author":"Gerretzen","year":"2016","journal-title":"Anal. Chim. Acta"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.aca.2016.01.010","article-title":"A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation","volume":"909","author":"Bi","year":"2016","journal-title":"Anal. Chim. Acta"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1021\/ac034173t","article-title":"A Perfect Smoother","volume":"75","author":"Eilers","year":"2003","journal-title":"Anal. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1080\/00401706.2000.10485719","article-title":"Local Regression and Likelihood","volume":"42","author":"Pan","year":"2000","journal-title":"Technometrics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.biosystemseng.2016.04.018","article-title":"Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy","volume":"152","author":"Morellos","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6371","DOI":"10.1016\/j.eswa.2014.04.019","article-title":"MIFS-ND: A mutual information-based feature selection method","volume":"41","author":"Hoque","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhou, X., Dai, Z., and Zou, X. (2010). Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm. BMC Bioinform., 11.","DOI":"10.1186\/1471-2105-11-325"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ross, B.C. (2014). Mutual Information between Discrete and Continuous Data Sets. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0087357"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"i197","DOI":"10.1093\/bioinformatics\/btv268","article-title":"Integrative random forest for gene regulatory network inference","volume":"31","author":"Petralia","year":"2015","journal-title":"Bioinformatics"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_34","unstructured":"Fern\u00e1ndez-Ugalde, O., Ballabio, C., Lugato, E., Scarpa, S., and Jones, A. (2020). Assessment of Changes in Topsoil Properties in LUCAS Samples between 2009\/2012 and 2015 Surveys, Publications Office of the European Union. JRC120138."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104444","DOI":"10.1016\/j.still.2019.104444","article-title":"Responses of soil carbon, nitrogen, and wheat and maize productivity to 10 years of decreased nitrogen fertilizer under contrasting tillage systems","volume":"196","author":"Liu","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106926","DOI":"10.1016\/j.agee.2020.106926","article-title":"May conservation tillage enhance soil C and N accumulation without decreasing yield in intensive irrigated croplands? Results from an eight-year maize monoculture","volume":"296","author":"Fiorini","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107871","DOI":"10.1016\/j.fcr.2020.107871","article-title":"Cover crops during transition to no-till maintain yield and enhance soil fertility in intensive agro-ecosystems","volume":"255","author":"Boselli","year":"2020","journal-title":"Field Crop. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1080\/03650340.2019.1681588","article-title":"Unraveling the local and structured variation of soil nutrients using two-dimensional empirical model decomposition in Fen River Watershed, China","volume":"66","author":"Zhu","year":"2020","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fenvs.2020.534774","article-title":"Root Features Determine the Increasing Proportion of Forbs in Response to Degradation in Alpine Steppe, Tibetan Plateau","volume":"8","author":"Zhang","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.geoderma.2012.12.011","article-title":"Spatial patterns of soil total nitrogen and soil total phosphorus across the entire Loess Plateau region of China","volume":"197\u2013198","author":"Liu","year":"2013","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.compag.2016.03.016","article-title":"Soil nitrogen content forecasting based on real-time NIR spectroscopy","volume":"124","author":"Zhang","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"138319","DOI":"10.1016\/j.scitotenv.2020.138319","article-title":"Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area","volume":"724","author":"Fathololoumi","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.trac.2013.04.015","article-title":"Breaking with trends in pre-processing?","volume":"50","author":"Engel","year":"2013","journal-title":"TrAC Trends Anal. Chem."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7078\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:43:18Z","timestamp":1760179398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/24\/7078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":43,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20247078"],"URL":"https:\/\/doi.org\/10.3390\/s20247078","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,10]]}}}