{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T08:35:01Z","timestamp":1768638901093,"version":"3.49.0"},"reference-count":70,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,30]],"date-time":"2019-06-30T00:00:00Z","timestamp":1561852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2017YFD0201501"],"award-info":[{"award-number":["2017YFD0201501"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009558","name":"University Natural Science Research Project of Anhui Province","doi-asserted-by":"publisher","award":["KJ2018A0009"],"award-info":[{"award-number":["KJ2018A0009"]}],"id":[{"id":"10.13039\/501100009558","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.<\/jats:p>","DOI":"10.3390\/s19132898","type":"journal-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T03:23:59Z","timestamp":1561951439000},"page":"2898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables"],"prefix":"10.3390","volume":"19","author":[{"given":"Lingling","family":"Fan","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"},{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-7689","authenticated-orcid":false,"given":"Jinling","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8473-5631","authenticated-orcid":false,"given":"Xingang","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"given":"Yulong","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis &amp; Application, Anhui University, Hefei 230601, China"},{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"given":"Guo","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]},{"given":"Pengfei","family":"Wei","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.rse.2004.03.013","article-title":"Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications","volume":"91","author":"Thenkabail","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_2","first-page":"52","article-title":"Increasing accuracy of hyper\u00b7spectral remote sensing for total nitrogen of winter wheat canopy by use of SPA and PLS methods","volume":"24","author":"Bai","year":"2018","journal-title":"J. Plant. Nutr. Fertil."},{"key":"ref_3","unstructured":"Peng, H., Xingang, X., Baolei, Z., Zhenhai, L., Haikuan, F., Guijun, Y., and Yongfeng, Z. (2015). Estimation of leaf chlorophyll content in winter wheat using variable importance for projection (VIP) with hyperspectral data. Remote Sens. Agric. Ecosyst. Hydrol. XVII, 12."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00299-1","article-title":"Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance","volume":"80","author":"Strachan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_5","unstructured":"Dandan, W., Xiaobing, L., Hong, W., Han, W., and Wanyu, W. (2012, January 22\u201327). Comparative study on estimation of nitrogen content in the heterogenious typical steppe using various red edge position extraction techniques. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1016\/j.rse.2010.04.006","article-title":"New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat","volume":"114","author":"Chen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_8","first-page":"57","article-title":"High spectral inversion of winter wheat LAI based on new vegetation index","volume":"51","author":"Shu","year":"2018","journal-title":"Sci. Agric. Sin."},{"key":"ref_9","unstructured":"Tan, C., Huang, Y., Huang, W., Wang, J., Zhao, C., and Liu, L. (2004). Study on colony leaf area index of summer maize by remote sensing vegetation indexes method. J. Anh. Agric. Univ., 31."},{"key":"ref_10","first-page":"5","article-title":"Quantitative analysis of near infrared spectroscopy based on wavelet coefficients and partial least-squares regression","volume":"28","author":"Li","year":"2018","journal-title":"J. Changchun Univ."},{"key":"ref_11","first-page":"8","article-title":"Measurements and analysis of marginal effect of water use efficiency by scientific and technological innovation based on PLS-PATH method","volume":"38","author":"Zhang","year":"2018","journal-title":"Adv. Sci. Technol. Water Res."},{"key":"ref_12","first-page":"7","article-title":"Using canopy hyperspectral reflectance to predict growth traits and seed yield of soybeans from middle and lower yangtze valleys through partial least squares regression","volume":"34","author":"Qi","year":"2015","journal-title":"Soybean Sci."},{"key":"ref_13","first-page":"3","article-title":"The basic principle of random forest and its applications in ecology: A case study of Pinus yunnanensis","volume":"34","author":"Zhang","year":"2014","journal-title":"Acta Ecol. Sin."},{"key":"ref_14","first-page":"3","article-title":"Prediction on firmness of strawberry based on hyperspectral imaging","volume":"17","author":"Lu","year":"2018","journal-title":"Softw. Guide"},{"key":"ref_15","first-page":"178","article-title":"CARS-ABC-SVR model for predicting leaf moisture of leaf-used lettuce based on hyperspectral","volume":"33","author":"Sun","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1029\/JB089iB07p06329","article-title":"Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications","volume":"89","author":"Clark","year":"1984","journal-title":"J. Geophys. Res."},{"key":"ref_17","first-page":"87","article-title":"Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions","volume":"5","author":"Mutanga","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00084-4","article-title":"Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression","volume":"67","author":"Kokaly","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Wang, L. (2017). Study on Nutrition Diagnosis of Nitrogen Content in Maize Leaves of Cold Region Based on Hyperspectral Imaging. [Master\u2019s Thesis, Northeast Agricultural University]. (In Chinese)."},{"key":"ref_20","first-page":"31","article-title":"Estimation of chlorophyll content in maize leaves based on hyperspectral under the action of microorganism","volume":"3","author":"Xie","year":"2018","journal-title":"West. Dev. Land Dev. Eng. Res."},{"key":"ref_21","unstructured":"He, T. (2016). Hyperspectral remote sensing estimation models for nitrogen nutrition monitoring of maize. [Master\u2019s Thesis, Shenyang Agricultural University]. (In Chinese)."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6221","DOI":"10.3390\/rs6076221","article-title":"Exploring the best hyperspectral features for LAI estimation using partial least squares regression","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","first-page":"226","article-title":"Selection of characteristic wavelengths using SPA and qualitative discrimination of mildew degree of corn kernels based on SVM","volume":"36","author":"Yuan","year":"2016","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_24","first-page":"5","article-title":"Determination of anthocyanin content in grape skins using hyperspectral imaging technique and successive projections algorithm","volume":"35","author":"Wu","year":"2014","journal-title":"Food Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/01431160600791650","article-title":"A linear model to predict with a multi-spectral radiometer the amount of nitrogen in winter wheat","volume":"27","author":"Reyniers","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"1","article-title":"Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice","volume":"10","author":"Zhu","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2134\/agronj2004.1350","article-title":"Monitoring leaf nitrogen status in rice with canopy spectral reflectance","volume":"96","author":"Xue","year":"2004","journal-title":"Agron. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.1080\/01431160701422213","article-title":"Using in-situ mea-surements to evaluate the new RapidEye\u2122 satellite series for prediction of wheat nitrogen status","volume":"28","author":"Eitel","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3663","DOI":"10.1080\/014311699211264","article-title":"Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation","volume":"20","author":"Adams","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0034-4257(02)00011-1","article-title":"Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals","volume":"81","author":"Serrano","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11119-006-9011-z","article-title":"Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments","volume":"7","author":"Fitzgerald","year":"2006","journal-title":"Precis Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"Feedback based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Geosci. Remote Sens. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TGRS.2003.812910","article-title":"Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data","volume":"41","author":"Gong","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of vegetation indices and a modified simple ratio for boreal applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_42","first-page":"309","article-title":"Monitoring vegetation systems in the great plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"Nasa Spec. Publ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/02757259409532252","article-title":"Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation","volume":"10","author":"Goel","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"9","article-title":"Theoretical bases and application of three gradient difference vegetation index","volume":"33","author":"Tang","year":"2003","journal-title":"Sci. China Ser. D"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Phys."},{"key":"ref_50","first-page":"1248","article-title":"Remote estimation of leaf area index and green leaf biomass in maize canopies","volume":"30","author":"Gitelson","year":"2003","journal-title":"Geophys. Res. Lett. Banner"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/01431169608949012","article-title":"Cell wall elasticity and water index (R970 nm\/R900 nm) in wheat under different nitrogen availabilities","volume":"17","author":"Penuelas","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/0034-4257(95)00039-4","article-title":"Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data","volume":"52","author":"Gao","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_54","first-page":"77","article-title":"The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of spartina alterniflora canopies","volume":"48","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/01431160310001618031","article-title":"Detecting sugarcane \u2018orange rust\u2019 disease using EO-1 Hyperion hyperspectral imagery","volume":"25","author":"Apan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1016\/j.rse.2008.05.003","article-title":"A near-infrared narrow-waveband ratio to determine Leaf Area Index in orchards","volume":"112","author":"Delalieux","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/S0003-2670(01)01271-5","article-title":"Comparison of different methods for variable selection","volume":"446","author":"Xu","year":"2001","journal-title":"Anal. Chim. Acta"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1137\/0905052","article-title":"The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses","volume":"5","author":"Wold","year":"1984","journal-title":"Siam J. Sci. Stat. Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aca.2011.11.037","article-title":"Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis","volume":"714","author":"Kamruzzaman","year":"2012","journal-title":"Anal. Chim. Acta"},{"key":"ref_60","first-page":"265","article-title":"Inversion model for copper content in farmland of tailing area based on visible-near infrared reflectance spectroscopy","volume":"6","author":"Hao","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_61","first-page":"6","article-title":"Hyperspectral estimation of apple tree canopy LAI based on SVM and RF regression","volume":"36","author":"Han","year":"2016","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6199","DOI":"10.1080\/01431160902842342","article-title":"Leaf area index derivation from hyperspectral vegetation indicesand the red edge position","volume":"30","author":"Darvishzadeh","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Feng, H.K., Yang, G.J., and Li, Z.H. (2018). A Comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_64","unstructured":"Xu, X. (2006). Remote Sensing Physics, Peking University Press."},{"key":"ref_65","first-page":"1868","article-title":"Monitoring models of the plant nitrogen content based on cotton canopy hyperspectral reflectance","volume":"31","author":"Wang","year":"2011","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_66","first-page":"1032","article-title":"Diagnosis of nitrogen content in upper and lower corn leaves based on hyperspectral data","volume":"33","author":"Liang","year":"2013","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_67","first-page":"783","article-title":"Modeling construction based on partial least-squares regression","volume":"35","author":"Luo","year":"2002","journal-title":"J. Tianjin Univ."},{"key":"ref_68","first-page":"1489","article-title":"Leaf area index estimation of spring maize with canopy hyperspectral data based on linear regression algorithm","volume":"37","author":"Wang","year":"2017","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn"},{"key":"ref_70","first-page":"9","article-title":"Associating new spectral features from visible and near infrared regions with optimal combination principle to monitor leaf nitrogen concentration in barley","volume":"32","author":"Xu","year":"2013","journal-title":"Int. J. Infrared Millimeter Waves"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:02:40Z","timestamp":1760187760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,30]]},"references-count":70,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19132898"],"URL":"https:\/\/doi.org\/10.3390\/s19132898","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,30]]}}}