{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:04:08Z","timestamp":1787029448533,"version":"3.56.0"},"reference-count":63,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T00:00:00Z","timestamp":1603670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015925","name":"California Table Grape Commission","doi-asserted-by":"publisher","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"10.13039\/100015925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Assessment of the nitrogen status of grapevines with high spatial, temporal resolution offers benefits in fertilizer use efficiency, crop yield and quality, and vineyard uniformity. The primary objective of this study was to develop a robust predictive model for grapevine nitrogen estimation at bloom stage using high-resolution multispectral images captured by an unmanned aerial vehicle (UAV). Aerial imagery and leaf tissue sampling were conducted from 150 grapevines subjected to five rates of nitrogen applications. Subsequent to appropriate pre-processing steps, pixels representing the canopy were segmented from the background per each vine. First, we defined a binary classification problem using pixels of three vines with the minimum (low-N class) and two vines with the maximum (high-N class) nitrogen concentration. Following optimized hyperparameters configuration, we trained five machine learning classifiers, including support vector machine (SVM), random forest, XGBoost, quadratic discriminant analysis (QDA), and deep neural network (DNN) with fully-connected layers. Among the classifiers, SVM offered the highest F1-score (82.24%) on the test dataset at the cost of a very long training time compared to the other classifiers. Alternatively, QDA and XGBoost required the minimum training time with promising F1-score of 80.85% and 80.27%, respectively. Second, we transformed the classification into a regression problem by averaging the posterior probability of high-N class for all pixels within each of 150 vines. XGBoost exhibited a slightly larger coefficient of determination (R2 = 0.56) and lower root mean square error (RMSE) (0.23%) compared to other learning methods in the prediction of nitrogen concentration of all vines. The proposed approach provides values in (i) leveraging high-resolution imagery, (ii) investigating spatial distribution of nitrogen across a vine\u2019s canopy, and (iii) defining spatial zones for nitrogen application and smart sampling.<\/jats:p>","DOI":"10.3390\/rs12213515","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T09:22:45Z","timestamp":1603790565000},"page":"3515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9249-8365","authenticated-orcid":false,"given":"Ali","family":"Moghimi","sequence":"first","affiliation":[{"name":"Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8237-1985","authenticated-orcid":false,"given":"Alireza","family":"Pourreza","sequence":"additional","affiliation":[{"name":"Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"German","family":"Zuniga-Ramirez","sequence":"additional","affiliation":[{"name":"Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Ave, Davis, CA 95616, USA"},{"name":"Kearney Agricultural Research and Extension Center, 9240 S. Riverbend Avenue, Parlier, CA 93648, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Larry E.","family":"Williams","sequence":"additional","affiliation":[{"name":"Kearney Agricultural Research and Extension Center, 9240 S. Riverbend Avenue, Parlier, CA 93648, USA"},{"name":"Department of Viticulture and Enology, University of California, Davis, 595 Hilgard Ln, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew W.","family":"Fidelibus","sequence":"additional","affiliation":[{"name":"Kearney Agricultural Research and Extension Center, 9240 S. Riverbend Avenue, Parlier, CA 93648, USA"},{"name":"Department of Viticulture and Enology, University of California, Davis, 595 Hilgard Ln, Davis, CA 95616, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,26]]},"reference":[{"key":"ref_1","unstructured":"Angelini, R. (2010). Mondo et mercato: Stati Uniti. L\u2019Uva da Tavola, Bayer CorpScience S.r.l."},{"key":"ref_2","unstructured":"Fidelibus, M., El-kereamy, A., Zhuang, G., Haviland, D., Hembree, K., and Stewart, D. (2018). Sample Costs to Establish and Produce Table Grapes. San Joaquin Valley South. Flame Seedless, Early Maturing, UC Agricultural Issues Center."},{"key":"ref_3","unstructured":"Christensen, L.P. (2000). Mineral nutrition and fertilization. Raisin Production Manual, University of California, Agriculture  Natural Resources, Communication Services."},{"key":"ref_4","first-page":"98660","article-title":"Detection of wine grape nutrient levels using visible and near infrared 1nm spectral resolution remote sensing","volume":"9866","author":"Anderson","year":"2016","journal-title":"Auton. Air Ground Sens. Syst. Agric. Optim. Phenotyping"},{"key":"ref_5","unstructured":"Christensen, L.P., Kasimatis, A.N., and Jensen, F.L. (1978). Grapevine Nutrition and Fertilization in the San Joaquin Valley, University of California."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"180","DOI":"10.5344\/ajev.1991.42.3.180","article-title":"Distribution and Translocation of Nitrogen Absorbed During Early Summer by Two-Year-Old Grapevines Grown in Sand Culture","volume":"42","author":"Conradie","year":"1991","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.envexpbot.2005.11.002","article-title":"Effect of light and nitrogen supply on internal C: N balance and control of root-to-shoot biomass allocation in grapevine","volume":"59","author":"Grechi","year":"2007","journal-title":"Environ. Exp. Bot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1111\/j.1755-0238.2001.tb00187.x","article-title":"Soil nitrogen utilisation for growth and gas exchange by grapevines in response to nitrogen supply and rootstock","volume":"7","author":"Keller","year":"2001","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.3389\/fpls.2018.01374","article-title":"Nitrogen Distribution in Annual Growth of \u2018Italia\u2019 Table Grape Vines","volume":"9","author":"Ferrara","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_10","unstructured":"Harter, T., Lund, J.R., Darby, J., Fogg, G.E., Howitt, R., Jessoe, K., Pettygrove, S.G., Quinn, J.F., Viers, J.H., and Boyle, D.B. (2012). Addressing Nitrate in California\u2019s Drinking Water with a Focus on Tulare Lake Basin and Salinas Valley Groundwater, UC Davis Center for Watershed Sciences. Report for the State Water Resources Control Board Report to the Legislature."},{"key":"ref_11","unstructured":"Mills, H.A., and Jones, J.B. (1996). Plant Analysis Handbook II, MicroMacro."},{"key":"ref_12","unstructured":"Iland, P., Dry, P., Proffitt, T., and Tyerman, S. (2011). The Grapevine: From the Science to the Practice of Growing Vines for Wine, Patrick Iland Wine Promotions Pty Ltd."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/ajgw.12424","article-title":"Performance of reflectance indices and of a handheld device for estimating in-field the nitrogen status of grapevine leaves","volume":"26","author":"Friedel","year":"2020","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11119-019-09661-x","article-title":"Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging","volume":"21","author":"Ye","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"455","DOI":"10.13031\/2013.18308","article-title":"Determination of Significant Wavelengths and Prediction of Nitrogen Content for Citrus","volume":"48","author":"Min","year":"2005","journal-title":"Trans. ASAE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2015.02.004","article-title":"Light interception, leaf nitrogen and yield prediction in almonds: A case study","volume":"66","author":"Muhammad","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105299","DOI":"10.1016\/j.compag.2020.105299","article-title":"Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat","volume":"172","author":"Moghimi","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_19","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_20","first-page":"105","article-title":"Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy","volume":"26","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","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_22","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_23","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.fcr.2010.11.002","article-title":"Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance","volume":"120","author":"Tian","year":"2011","journal-title":"Field Crop. Res."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.1080\/01431160500181044","article-title":"Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species","volume":"26","author":"Ferwerda","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105162","DOI":"10.1016\/j.compag.2019.105162","article-title":"Deep learning for classification and severity estimation of coffee leaf biotic stress","volume":"169","author":"Esgario","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Qiu, R., Yang, C., Moghimi, A., Zhang, M., Steffenson, B.J., and Hirsch, C.D. (2019). Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging. Remote Sens., 11.","DOI":"10.20944\/preprints201910.0056.v1"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.pbi.2020.05.006","article-title":"Computational solutions for modeling and controlling plant response to abiotic stresses: A review with focus on iron deficiency","volume":"57","author":"Tong","year":"2020","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.3389\/fpls.2018.01182","article-title":"A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging","volume":"9","author":"Moghimi","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_30","first-page":"102174","article-title":"Retrieval of aboveground crop nitrogen content with a hybrid machine learning method","volume":"92","author":"Berger","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nigon, T.J., Yang, C., Paiao, G.D., Mulla, D.J., Knight, J.F., and Fern\u00e1ndez, F.G. (2020). Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12081234"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"56870","DOI":"10.1109\/ACCESS.2018.2872801","article-title":"Ensemble Feature Selection for Plant Phenotyping: A Journey from Hyperspectral to Multispectral Imaging","volume":"6","author":"Moghimi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105306","DOI":"10.1016\/j.compag.2020.105306","article-title":"Towards weeds identification assistance through transfer learning","volume":"171","author":"Mylonas","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.compag.2017.05.026","article-title":"Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery","volume":"139","author":"Pantazi","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_36","unstructured":"Gavlak, R., Horneck, D., and Miller, R.O. (2005). Soil, Plant and Water Reference Methods for the Western Region 1, Western Rural Development Center (WREP-125). [3rd ed.]."},{"key":"ref_37","unstructured":"Moghimi, A. (2020, June 20). Micasense_Preprocessing (Version 1.0.0). Available online: https:\/\/doi.org\/10.5281\/zenodo.3988680."},{"key":"ref_38","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_40","unstructured":"Abadi, M., Agarwal, A., Paul Barham, E.B., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., and Ghemawat, S. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. arXiv."},{"key":"ref_41","unstructured":"Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Ben-tzur, J., Hardt, M., Recht, B., and Talwalkar, A. (2020, January 2\u20134). A System for Massively Parallel Hyperparameter Tuning. Proceedings of the Machine Learning and Systems, Austin, TX, USA."},{"key":"ref_42","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","unstructured":"Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., and Weinberger, K.Q. (2011). Algorithms for Hyper-Parameter Optimization. Advances in Neural Information Processing Systems 24, Curran Associates, Inc."},{"key":"ref_44","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Proceedings of the 25th International Conference on Neural Information Processing Systems\u2014Volume 2, Curran Associates Inc."},{"key":"ref_45","unstructured":"Bergstra, J., Yamins, D., and Cox, D.D. (2013, January 16\u201321). Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_46","unstructured":"Franceschi, L., Donini, M., Frasconi, P., and Pontil, M. (2017, January 6\u201311). Forward and Reverse Gradient-Based Hyperparameter Optimization. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 25). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_48","unstructured":"Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, Addison-Wesley Longman Publishing Co., Inc.. [1st ed.]."},{"key":"ref_49","first-page":"61","article-title":"Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods","volume":"10","author":"Platt","year":"1999","journal-title":"Adv. Large Margin Classif."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S1360-1385(98)01213-8","article-title":"Visible and near-infrared reflectance techniques for diagnosing plant physiological status","volume":"3","author":"Filella","year":"1998","journal-title":"Trends Plant Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.eja.2004.06.005","article-title":"Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum","volume":"22","author":"Zhao","year":"2005","journal-title":"Eur. J. Agron."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.asr.2004.09.008","article-title":"Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency","volume":"35","author":"Beyl","year":"2005","journal-title":"Adv. Space Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169408954177","article-title":"The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status","volume":"15","author":"Filella","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1364\/AO.4.000011","article-title":"Spectral Properties of Plants","volume":"4","author":"Gates","year":"1965","journal-title":"Appl. Opt."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0034-4257(70)80021-9","article-title":"Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation","volume":"1","author":"Knipling","year":"1970","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"278","DOI":"10.2307\/2657019","article-title":"Estimating near-infrared leaf reflectance from leaf structural characteristics","volume":"88","author":"Slaton","year":"2001","journal-title":"Am. J. Bot."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1093\/aob\/mcw099","article-title":"A meta-analysis of leaf nitrogen distribution within plant canopies","volume":"118","author":"Hikosaka","year":"2016","journal-title":"Ann. Bot."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"278","DOI":"10.5344\/ajev.1994.45.3.278","article-title":"Interactions of LeafAge, Fruiting, and Exogenous Cytokinins in Sangiovese Grapevines under Non-Irrigated Conditions. II. Chlorophyll and Nitrogen Content","volume":"45","author":"Poni","year":"1994","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"663","DOI":"10.3389\/fpls.2020.00663","article-title":"Early Diagnosis and Management of Nitrogen Deficiency in Plants Utilizing Raman Spectroscopy","volume":"11","author":"Huang","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.rse.2013.07.024","article-title":"A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index","volume":"138","author":"Williams","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Omidi, R., Moghimi, A., Pourreza, A., Aly, M.E.-H., and Eddin, A.S. (2020). Ensemble Hyperspectral Band Selection for Detecting Nitrogen Status in Grape Leaves. arXiv.","DOI":"10.1109\/ICMLA51294.2020.00054"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3515\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:37Z","timestamp":1760178517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3515"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,26]]},"references-count":63,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213515"],"URL":"https:\/\/doi.org\/10.3390\/rs12213515","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,26]]}}}