{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:59:39Z","timestamp":1775872779403,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11042-021-10544-5","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T18:02:46Z","timestamp":1612029766000},"page":"14887-14914","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["A comprehensive review on soil classification using deep learning and computer vision techniques"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0125-1018","authenticated-orcid":false,"given":"Pallavi","family":"Srivastava","sequence":"first","affiliation":[]},{"given":"Aasheesh","family":"Shukla","sequence":"additional","affiliation":[]},{"given":"Atul","family":"Bansal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"10544_CR1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.still.2016.04.012","volume":"162","author":"FR Ajdadi","year":"2016","unstructured":"Ajdadi FR, Gilandeh YA, Mollazade K, Hasanzadeh RPR (2016) Application of machine vision for classification of soil aggregate size. Soil Tillage Res 162:8\u201317","journal-title":"Soil Tillage Res"},{"key":"10544_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0016-7061(03)00218-0","volume":"119","author":"S Aydemir","year":"2004","unstructured":"Aydemir S, Keskin S, Drees LR (2004) Quantification of soil features using digital image processing (DIP) techniques. Geoderma 119:1\u20138","journal-title":"Geoderma"},{"key":"10544_CR3","first-page":"51","volume":"11","author":"MM Aziz","year":"2016","unstructured":"Aziz MM, Ahmed DR, Ibrahim BF (2016) Determine the Ph. Of soil by using neural network based on Soil\u2019s colour. Int J Advanced Res Comput Sci Software Eng 11:51\u201354","journal-title":"Int J Advanced Res Comput Sci Software Eng"},{"key":"10544_CR4","doi-asserted-by":"publisher","first-page":"104586","DOI":"10.1016\/j.still.2020.104586","volume":"199","author":"A Azizi","year":"2020","unstructured":"Azizi A, Gilandeh YA, Mesri-Gundoshmian T, Saleh-Bigdeli AA, Moghaddam HA (2020) Classification of soil aggregates: a novel approach based on deep learning. Soil Tillage Res 199:104586","journal-title":"Soil Tillage Res"},{"issue":"2","key":"10544_CR5","first-page":"318","volume":"7","author":"U Barman","year":"2020","unstructured":"Barman U, Choudhury RD (2020) Soil texture classification using multi-class support vector machine. Inf Process Agric 7(2):318\u2013332","journal-title":"Inf Process Agric"},{"issue":"2","key":"10544_CR6","doi-asserted-by":"publisher","first-page":"805","DOI":"10.31018\/jans.v10i2.1701","volume":"10","author":"U Barman","year":"2018","unstructured":"Barman U, Choudhury RD, Talukdar N, Deka P, Kalita I, Rahman N (2018) Predication of soil pH using HSI colour image processing and regression over Guwahati, Assam, India. Journal of Applied and Natural Science 10(2):805\u2013809","journal-title":"Journal of Applied and Natural Science"},{"key":"10544_CR7","unstructured":"Barman U, Choudhury RD, Uddin I (2019) Predication of Soil pH using K mean Segmentation and HSV Color Image Processing. 6th Int. Conf. Comput. Sustain. Glob. Dev. INDIACom."},{"key":"10544_CR8","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.biosystemseng.2007.03.025","volume":"97","author":"I Bogrekci","year":"2007","unstructured":"Bogrekci I, Godwin RJ (2007) Development of an image-processing technique for soil tilth sensing. Biosyst Eng 97:323\u2013331","journal-title":"Biosyst Eng"},{"key":"10544_CR9","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1590\/S0103-84782006000400021","volume":"36","author":"M Botelho","year":"2006","unstructured":"Botelho M, Dalmolin R, Pedron F, Azevedo A, Rodrigues R, Miguel M (2006) Color measurement in soils from Rio Grande do Sul state with Munsell charts and by colorimetry. Cienc Rural 36:1179\u20131185","journal-title":"Cienc Rural"},{"key":"10544_CR10","doi-asserted-by":"crossref","unstructured":"Breul P, Gourves R (2006) In field soil characterization: approach based on texture image analysis. Journal of Geotechnical and Geoenvironmental Engineering 132(1):102\u2013107","DOI":"10.1061\/(ASCE)1090-0241(2006)132:1(102)"},{"key":"10544_CR11","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.geoderma.2014.09.019","volume":"239\u2013240","author":"CW Brungard","year":"2015","unstructured":"Brungard CW, Boettinger JL, Duniway MC, Wills SA, Edwards TC (2015) Geoderma machine learning for predicting soil classes in three semi-arid landscapes. Geoderma 239\u2013240:68\u201383","journal-title":"Geoderma"},{"key":"10544_CR12","unstructured":"Chandan Thakur R (2018) Recent Trends Of Machine Learning In Soil Classification: A Review. International Journal of Computational Engineering Research. 25\u201332."},{"key":"10544_CR13","doi-asserted-by":"publisher","first-page":"19959","DOI":"10.1109\/ACCESS.2018.2815149","volume":"6","author":"J Chu","year":"2018","unstructured":"Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:19959\u201319967","journal-title":"IEEE Access"},{"key":"10544_CR14","doi-asserted-by":"publisher","first-page":"393","DOI":"10.5109\/25196","volume":"57","author":"SO Chung","year":"2012","unstructured":"Chung SO, Cho KH, Cho JW, Jung KY, Yamakawa T (2012) Soil texture classification algorithm using RGB characteristics of soil images. J Fac Agric Kyushu Univ 57:393\u2013397","journal-title":"J Fac Agric Kyushu Univ"},{"key":"10544_CR15","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/j.microc.2019.01.009","volume":"146","author":"PA de O. Morais","year":"2019","unstructured":"de O. Morais PA, Souza DM, de M. Carvalho MT, Madari BE, de Oliveira AE (2019) Predicting soil texture using image analysis. Microchem. J. 146:455\u2013463","journal-title":"Microchem. J."},{"key":"10544_CR16","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1016\/S1002-0160(17)60377-1","volume":"28","author":"A DORNIK","year":"2018","unstructured":"DORNIK A, DR\u0102GU\u0162 L, URDEA P (2018) Classification of soil types using geographic object-based image analysis and random forests. Pedosphere 28:913\u2013925","journal-title":"Pedosphere"},{"issue":"1","key":"10544_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.geoderma.2009.09.008","volume":"160","author":"B Ehret","year":"2010","unstructured":"Ehret B (2010) Pattern recognition of geophysical data. Geoderma 160(1):111\u2013125","journal-title":"Geoderma"},{"key":"10544_CR18","unstructured":"Gurubasava, Mahantesh SD (2018) Analysis of Agricultural soil pH using Digital Image Processing. 6, 1812\u20131816"},{"key":"10544_CR19","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.compag.2016.02.024","volume":"123","author":"P Han","year":"2016","unstructured":"Han P, Dong D, Zhao X, Jiao L, Lang Y (2016) A smartphone-based soil color sensor: for soil type classification. Comput Electron Agric 123:232\u2013241","journal-title":"Comput Electron Agric"},{"key":"10544_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.geoderma.2015.11.014","volume":"265","author":"B Heung","year":"2016","unstructured":"Heung B, Chak H, Zhang J, Knudby A, Bulmer CE, Schmidt MG (2016) Geoderma an overview and comparison of machine-learning techniques for classi fi cation purposes in digital soil mapping. Geoderma 265:62\u201377","journal-title":"Geoderma"},{"key":"10544_CR21","unstructured":"Honawad PSK, Chinchali PSS, Pawar PK, Deshpande PP (2017) Soil Classification and Suitable Crop Prediction. 25\u201329"},{"key":"10544_CR22","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.geoderma.2009.11.005","volume":"154","author":"M Kova\u010devi\u0107","year":"2010","unstructured":"Kova\u010devi\u0107 M, Bajat B, Gaji\u0107 B (2010) Soil type classification and estimation of soil properties using support vector machines. Geoderma 154:340\u2013347","journal-title":"Geoderma"},{"key":"10544_CR23","unstructured":"Leng L, Zhang J (2012) Palmhash code for palmprint verification and protection. 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE"},{"key":"10544_CR24","doi-asserted-by":"crossref","unstructured":"Leng L, Zhang J (2013) Palmhash code vs palmphasor code. Neurocomput 108(1\u20132)","DOI":"10.1016\/j.neucom.2012.08.028"},{"key":"10544_CR25","doi-asserted-by":"crossref","unstructured":"Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. International conference on information and communication technology convergence (ICTC). 467\u2013471","DOI":"10.1109\/ICTC.2010.5674791"},{"key":"10544_CR26","doi-asserted-by":"crossref","unstructured":"Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) Two-directional two-dimensional random projection and its variations for face and palmprint recognition. Int Confer Computation Sci Appl:458\u2013470","DOI":"10.1007\/978-3-642-21934-4_37"},{"key":"10544_CR27","doi-asserted-by":"crossref","unstructured":"Leng L, Zhang S, Bi X, Khan MK (2012) Two-dimensional cancelable biometric scheme. International Conference on Wavelet Analysis and Pattern Recognition. IEEE. pp. 164\u2013169","DOI":"10.1109\/ICWAPR.2012.6294772"},{"key":"10544_CR28","doi-asserted-by":"crossref","unstructured":"Leng L, Li M, Teoh ABJ (2013) Conjugate 2DPalmHash code for secure palm-print-vein verification. International congress on image and signal processing (CISP) IEEE. pp. 705\u20131710","DOI":"10.1109\/CISP.2013.6743951"},{"key":"10544_CR29","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s11042-015-3058-7","volume":"76","author":"L Leng","year":"2017","unstructured":"Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76:333\u2013354","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"10544_CR30","doi-asserted-by":"publisher","first-page":"2644","DOI":"10.3390\/s20092644","volume":"20","author":"L Leng","year":"2020","unstructured":"Leng L, Yang Z, Kim C, Zhang Y (2020) A Light-Weight Practical Framework for Feces Detection and Trait Recognition. Sensors 20(9):2644","journal-title":"Sensors"},{"key":"10544_CR31","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.geoderma.2011.10.010","volume":"170","author":"M Lie\u00df","year":"2012","unstructured":"Lie\u00df M, Glaser B, Huwe B (2012) Geoderma uncertainty in the spatial prediction of soil texture comparison of regression tree and random Forest models. Geoderma 170:70\u201379","journal-title":"Geoderma"},{"key":"10544_CR32","first-page":"52","volume":"2018","author":"SR Maniyath","year":"2018","unstructured":"Maniyath SR, Hebbar R, Akshatha KN, Architha LS, Rama Subramoniam S (2018) Soil color detection using Knn classifier. Proc. - 2018 Int. Conf. Des. Innov. 3Cs Comput. Commun. Control. ICDI3C 2018:52\u201355","journal-title":"Proc. - 2018 Int. Conf. Des. Innov. 3Cs Comput. Commun. Control. ICDI3C"},{"key":"10544_CR33","first-page":"989","volume":"8","author":"AD Mengistu","year":"2018","unstructured":"Mengistu AD, Alemayehu DM (2018) Soil characterization and classification: a hybrid approach of computer vision and sensor network. Int J Electr Comput Eng 8:989\u2013995","journal-title":"Int J Electr Comput Eng"},{"key":"10544_CR34","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/0016-7061(71)90013-9","volume":"5","author":"GSRK Murti","year":"1971","unstructured":"Murti GSRK, Satyanarayana KVS (1971) Influence of chemical characteristics in the development of soil colour. Geoderma 5:243\u2013248","journal-title":"Geoderma"},{"key":"10544_CR35","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.geoderma.2010.03.019","volume":"157","author":"TK O\u2019Donnell","year":"2010","unstructured":"O\u2019Donnell TK, Goyne KW, Miles RJ, Baffaut C, Anderson SH, Sudduth KA (2010) Identification and quantification of soil redoximorphic features by digital image processing. Geoderma 157:86\u201396","journal-title":"Geoderma"},{"key":"10544_CR36","doi-asserted-by":"crossref","unstructured":"Ok S, Hyun K, Youl K (2012) Texture Classification Algorithm Using RGB Characteristics of Soil Images 57, 393\u2013397","DOI":"10.5109\/25196"},{"key":"10544_CR37","doi-asserted-by":"publisher","first-page":"819","DOI":"10.22214\/ijraset.2018.7138","volume":"6","author":"S Pethkar","year":"2018","unstructured":"Pethkar S (2018) Classification of soil image using feature extraction. Int J Res Appl Sci Eng Technol 6:819\u2013823","journal-title":"Int J Res Appl Sci Eng Technol"},{"key":"10544_CR38","first-page":"275","volume":"53","author":"R Protz","year":"1992","unstructured":"Protz R, Sweeney SJ, Fox CA (1992) An application of spectral image analysis to soil micromorphology, 1. Methods Anal 53:275\u2013287","journal-title":"Methods Anal"},{"key":"10544_CR39","doi-asserted-by":"crossref","unstructured":"Rahman SAZ, Mitra KC, Islam SM (2018) Soil classification using machine learning methods and crop suggestion based on soil series. International Conference of Computer and Information Technology (ICCIT). IEEE. pp. 1\u20134","DOI":"10.1109\/ICCITECHN.2018.8631943"},{"key":"10544_CR40","first-page":"792","volume":"4","author":"A Rao","year":"2016","unstructured":"Rao A, Abhishek JU, Manjunatha GNS, Beham R (2016) Machine Learn Soil Classific Crop Detect 4:792\u2013794","journal-title":"Machine Learn Soil Classific Crop Detect"},{"key":"10544_CR41","doi-asserted-by":"crossref","unstructured":"Sharma HK, Kumar S (2018) Soil Classification & Characterization Using Image Processing.\u00a02018 Second International Conference on Computing Methodologies and Communication (ICCMC). pp. 885\u2013890","DOI":"10.1109\/ICCMC.2018.8488103"},{"key":"10544_CR42","doi-asserted-by":"publisher","first-page":"15","DOI":"10.9756\/BIJAIP.1004","volume":"1","author":"R Shenbagavalli","year":"2011","unstructured":"Shenbagavalli R, Ramar K (2011) Classification of soil textures based on Laws features extracted from preprocessing images on sequential and random windows. Bonfring Int J Adv Image Process 1:15\u201318","journal-title":"Bonfring Int J Adv Image Process"},{"key":"10544_CR43","first-page":"8","volume":"88","author":"R Shenbagavalli","year":"2014","unstructured":"Shenbagavalli R, Ramar K (2014) Feature extraction of soil images for retrieval based on statistics. Int J Comput Appl 88:8\u201312","journal-title":"Int J Comput Appl"},{"key":"10544_CR44","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1080\/01431161.2018.1430399","volume":"39","author":"G Shukla","year":"2018","unstructured":"Shukla G, Garg RD, Srivastava HS, Garg PK (2018) An effective implementation and assessment of a random forest classifier as a soil spatial predictive model. Int J Remote Sens 39:2637\u20132669","journal-title":"Int J Remote Sens"},{"issue":"4","key":"10544_CR45","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/LGRS.2005.851752","volume":"2","author":"A Sofou","year":"2005","unstructured":"Sofou A, Evangelopoulos G, Maragos P (2005) Soil image segmentation and texture analysis: a computer vision approach. Geoscience and Remote Sensing Letters 2(4):394\u2013398","journal-title":"Geoscience and Remote Sensing Letters"},{"key":"10544_CR46","doi-asserted-by":"crossref","unstructured":"Srunitha K, Padmavathi S (2016) Performance of SVM classifier for image based soil classification. Int. Conf. on Signal Processing, Communication, Power and Embedded System. SCOPES: 411\u2013415","DOI":"10.1109\/SCOPES.2016.7955863"},{"issue":"1","key":"10544_CR47","first-page":"72","volume":"7","author":"MS Suchithra","year":"2020","unstructured":"Suchithra MS, Pai ML (2020) Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf Process Agric 7(1):72\u201382","journal-title":"Inf Process Agric"},{"key":"10544_CR48","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.compag.2018.03.019","volume":"148","author":"B Sudarsan","year":"2018","unstructured":"Sudarsan B, Ji W, Adamchuk V, Biswas A (2018) Characterizing soil particle sizes using wavelet analysis of microscope images. Comput Electron Agric 148:217\u2013225","journal-title":"Comput Electron Agric"},{"key":"10544_CR49","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.geoderma.2015.04.008","volume":"253","author":"R Taghizadeh-Mehrjardi","year":"2015","unstructured":"Taghizadeh-Mehrjardi R, Nabiollahi K, Minasny B, Triantafilis J (2015) Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region. Geoderma 253:67\u201377","journal-title":"Geoderma"},{"key":"10544_CR50","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/0016-7061(92)90011-U","volume":"55","author":"F Terribile","year":"1992","unstructured":"Terribile F, FitzPatrick EA (1992) The application of multilayer digital image processing techniques to the description of soil thin sections. Geoderma 55:159\u2013174","journal-title":"Geoderma"},{"issue":"3\u20134","key":"10544_CR51","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/S0016-7061(98)00089-5","volume":"89","author":"AJ VandenBygaart","year":"1999","unstructured":"VandenBygaart AJ, Protz R (1999) The representative elementary area (REA) in studies of quantitative soil micromorphology. Geoderma 89(3\u20134):333\u2013346","journal-title":"Geoderma"},{"key":"10544_CR52","doi-asserted-by":"crossref","unstructured":"Wei PG, Sheng WQ (2013). Prediction of soil organic matter using artificial neural network and topographic indicators in hilly areas. Nutr Cycling Agroecosyst. pp. 333\u2013344.","DOI":"10.1007\/s10705-013-9566-9"},{"key":"10544_CR53","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.compag.2017.11.037","volume":"144","author":"W Wu","year":"2018","unstructured":"Wu W, Di Li A, He XH, Ma R, Liu H, Bin Liv JK (2018) A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Comput Electron Agric 144:86\u201393","journal-title":"Comput Electron Agric"},{"issue":"12","key":"10544_CR54","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.3390\/electronics8121464","volume":"8","author":"Z Yang","year":"2019","unstructured":"Yang Z, Leng L, Kim BG (2019) StoolNet for Color Classification of Stool Medical Images. Electronics 8(12):1464","journal-title":"Electronics"},{"key":"10544_CR55","doi-asserted-by":"publisher","first-page":"23029","DOI":"10.1364\/OE.27.023029","volume":"27","author":"Y Yu","year":"2019","unstructured":"Yu Y, Xu T, Shen Z, Zhang Y, Wang X (2019) Compressive spectral imaging system for soil classification with three-dimensional convolutional neural network. Opt Express 27:23029\u201323048","journal-title":"Opt Express"},{"key":"10544_CR56","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.geoderma.2015.04.012","volume":"254","author":"T Z\u00e1dorov\u00e1","year":"2015","unstructured":"Z\u00e1dorov\u00e1 T, Pen\u00ed V, Va R, Daniel \u017d (2015) Colluvial soils as a soil organic carbon pool in different soil regions. Geoderma 254:122\u2013134","journal-title":"Geoderma."},{"key":"10544_CR57","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/ejss.12699","volume":"70","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Hartemink AE (2019) Digital mapping of a soil profile. Eur J Soil Sci 70:27\u201341","journal-title":"Eur J Soil Sci"},{"key":"10544_CR58","unstructured":"Zhang X, Younan NH, King RL (2003) Soil texture classification using wavelet transform and Maximum Likelihood Approach. 7929\u20137931"},{"key":"10544_CR59","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1109\/TGRS.2004.841476","volume":"43","author":"X Zhang","year":"2005","unstructured":"Zhang X, Younan NH, O\u2019Hara CG (2005) Wavelet domain statistical hyperspectral soil texture classification. IEEE Trans Geosci Remote Sens 43:615\u2013618","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"10544_CR60","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/s20041010","volume":"20","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Chu J, Leng L, Miao J (2020) Mask-refined R-CNN: a network for refining object details in instance segmentation. Sensors 20(4):1010","journal-title":"Sensors"},{"key":"10544_CR61","doi-asserted-by":"crossref","unstructured":"Zhao Z, Lien T, Rees HW, Yang Q, Xing Z, Meng F (2008) Predict soil texture distributions using an artificial neural network model 5, 36\u201348","DOI":"10.1016\/j.compag.2008.07.008"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10544-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10544-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10544-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T21:12:14Z","timestamp":1674940334000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10544-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,30]]},"references-count":61,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["10544"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10544-5","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,30]]},"assertion":[{"value":"16 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of interest"}}]}}