{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:52:52Z","timestamp":1760151172044,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T00:00:00Z","timestamp":1645833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004955","name":"Austrian Research Promotion Agency","doi-asserted-by":"publisher","award":["854182"],"award-info":[{"award-number":["854182"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved site-specific management and livestock feeding. A systematic approach was developed to analyze vegetation of managed permanent grassland using hyperspectral imaging in a laboratory setting. In the first step, hyperspectral images of typical grassland plants were recorded, annotated, and classified according to species group and plant parts, that is, flowers, leaves, and stems. In the second step, three different machine learning model types\u2014multilayer perceptron (MLP), random forest (RF), and partial least squares discriminant analysis (PLS-DA)\u2014were trained with pixel-wise spectral information to discriminate different species groups and plant parts in individual models. The influence of radiometric data calibration and specific data preprocessing steps on the overall model performance was also investigated. While the influence of proper radiometric calibration was negligible in our setting, specific preprocessing variants, including smoothening and derivation of the spectrum, were found to be beneficial for classification accuracy. Compared to extensively preprocessed data, raw spectral data yielded no statistically decreased performance in most cases. Overall, the MLP models outperformed the PLS-DA and RF models and reached cross-validation accuracies of 96.8% for species group and 88.6% for plant part classification. The obtained insights provide an essential basis for future data acquisition and data analysis of grassland vegetation.<\/jats:p>","DOI":"10.3390\/rs14051154","type":"journal-article","created":{"date-parts":[[2022,2,27]],"date-time":"2022-02-27T20:48:33Z","timestamp":1645994913000},"page":"1154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9981-7293","authenticated-orcid":false,"given":"Roland","family":"Britz","sequence":"first","affiliation":[{"name":"FFoQSI GmbH, Technopark 1D, 3430 Tulln, Austria"},{"name":"Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0960-3564","authenticated-orcid":false,"given":"Norbert","family":"Barta","sequence":"additional","affiliation":[{"name":"Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3713-3048","authenticated-orcid":false,"given":"Andreas","family":"Schaumberger","sequence":"additional","affiliation":[{"name":"Agricultural Research and Education Centre Raumberg-Gumpenstein, Raumberg 38, 8952 Irdning, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3892-831X","authenticated-orcid":false,"given":"Andreas","family":"Klingler","sequence":"additional","affiliation":[{"name":"Agricultural Research and Education Centre Raumberg-Gumpenstein, Raumberg 38, 8952 Irdning, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6954-3845","authenticated-orcid":false,"given":"Alexander","family":"Bauer","sequence":"additional","affiliation":[{"name":"Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0564-8135","authenticated-orcid":false,"given":"Erich M.","family":"P\u00f6tsch","sequence":"additional","affiliation":[{"name":"Agricultural Research and Education Centre Raumberg-Gumpenstein, Raumberg 38, 8952 Irdning, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6957-6815","authenticated-orcid":false,"given":"Andreas","family":"Gronauer","sequence":"additional","affiliation":[{"name":"Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4860-4247","authenticated-orcid":false,"given":"Viktoria","family":"Motsch","sequence":"additional","affiliation":[{"name":"Department of Sustainable Agricultural Systems, Institute of Agricultural Engineering, University of Natural Resources and Life Sciences, Vienna, Peter-Jordan-Stra\u00dfe 82, 1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,26]]},"reference":[{"key":"ref_1","unstructured":"Nelson, C.J., Moore, K.J., and Collins, M. (2017). Forages\u2014An Introduction to Grassland Agriculture, John Wiley & Sons. Chapter Forages and Grasslands in a Changing World."},{"key":"ref_2","unstructured":"Buchgraber, K., Schaumberger, A., and P\u00f6tsch, E.M. (2011, January 29\u201331). Grassland Farming in Austria\u2014Status quo and future prospective. Proceedings of the 16th Symposium of the European Grassland Federation \u201cGrassland Farming and Land Management Systems in Mountainous Regions\u201d, Grassland Science in Europe, Gumpenstein, Austria."},{"key":"ref_3","unstructured":"P\u00f6tsch, E.M., Blaschka, A., and Resch, R. (2005, January 29\u201331). Impact of different management systems and location parameters on floristic diversity of mountainous grassland. Proceedings of the 13th International Occasional Symposium of the European Grassland Federation (EGF): \u201cIntegrating Efficient Grassland Farming and Biodiversity\u201d, Grassland Science in Europe, Tartu, Estonia."},{"key":"ref_4","unstructured":"Schellberg, J., and da Pontes, L.S. (2011, January 29\u201331). Plant functional traits and nutrient gradients on grassland. Proceedings of the 16th Symposium of the European Grassland Federation \u201cGrassland Farming and Land Management Systems in Mountainous Regions\u201d, Grassland Science in Europe, Gumpenstein, Austria."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1111\/1365-2664.12991","article-title":"Weed suppression greatly increased by plant diversity in intensively managed grasslands: A continental-scale experiment","volume":"55","author":"Connolly","year":"2018","journal-title":"J. Appl. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15047","DOI":"10.1038\/s41598-018-33262-9","article-title":"Higher species richness enhances yield stability in intensively managed grasslands with experimental disturbance","volume":"8","author":"Haughey","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1111\/gfs.12124","article-title":"Potential of legume-based grassland\u2013livestock systems in Europe: A review","volume":"69","author":"Soussana","year":"2014","journal-title":"Grass Forage Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1111\/gfs.12270","article-title":"Associative effects between fresh perennial ryegrass and white clover on dynamics of intake and digestion in sheep","volume":"72","author":"Niderkorn","year":"2017","journal-title":"Grass Forage Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1111\/gcb.12880","article-title":"Nitrogen yield advantage from grass\u2013legume mixtures is robust over a wide range of legume proportions and environmental conditions","volume":"21","author":"Suter","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.eja.2011.09.003","article-title":"N2-fixation and residual N effect of four legume species and four companion grass species","volume":"36","author":"Rasmussen","year":"2012","journal-title":"Eur. J. Agron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s13593-011-0056-7","article-title":"Legumes for mitigation of climate change and the provision of feedstock for biofuels and biorefineries: A review","volume":"32","author":"Jensen","year":"2012","journal-title":"Agron. Sustain. Dev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2478\/boku-2019-0001","article-title":"Methods to describe the botanical composition of vegetation in grassland research","volume":"70","author":"Peratoner","year":"2019","journal-title":"Die Bodenkultur J. Land Manag. Food Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/gfs.12312","article-title":"Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands","volume":"73","author":"Wachendorf","year":"2018","journal-title":"Grass Forage Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7732","DOI":"10.3390\/rs6087732","article-title":"Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery","volume":"6","author":"Dalmayne","year":"2014","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wijesingha, J., Astor, T., Schulze-Bruninghoff, D., Wengert, M., and Wachendorf, M. (2020). Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy. Remote Sens., 12.","DOI":"10.3390\/rs12010126"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.compag.2013.10.004","article-title":"Combining ultrasonic sward height and spectral signatures to assess the biomass of legume-grass swards","volume":"99","author":"Fricke","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gr\u00fcner, E., Wachendorf, M., and Astor, T. (2020). The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0234703"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107227","DOI":"10.1016\/j.ecolind.2020.107227","article-title":"A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows","volume":"122","author":"Bergamo","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1046\/j.1365-2494.2003.00379.x","article-title":"Monitoring grass swards using imaging spectroscopy","volume":"58","author":"Schut","year":"2003","journal-title":"Grass Forage Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"024517","DOI":"10.1117\/1.JRS.14.024517","article-title":"Spectrometric proximally sensed data for estimating chlorophyll content of grasslands treated with complex fertilizer combinations","volume":"14","author":"Sibanda","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e12600","DOI":"10.1111\/avsc.12600","article-title":"The relationship between species and spectral diversity in grassland communities is mediated by their vertical complexity","volume":"24","author":"Conti","year":"2021","journal-title":"Appl. Veg. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"M\u00f6ckel, T., Dalmayne, J., Schmid, B.C., Prentice, H.C., and Hall, K. (2016). Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands. Remote Sens., 8.","DOI":"10.3390\/rs8020133"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.rse.2007.12.003","article-title":"Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland","volume":"112","author":"Darvishzadeh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"369","article-title":"Comparison of Direct and Indirect Determination of Leaf Area Index in Permanent Grassland","volume":"88","author":"Klingler","year":"2020","journal-title":"PFG\u2013J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/10106049.2019.1704070","article-title":"Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image","volume":"37","author":"Adagbasa","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, Y., Yang, J., and Guo, X. (2020). Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014. Remote Sens., 12.","DOI":"10.3390\/rs12223826"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Melville, B., Lucieer, A., and Aryal, J. (2018). Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia. Remote Sens., 10.","DOI":"10.3390\/rs10020308"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pfitzner, K., Bartolo, R., Whiteside, T., Loewensteiner, D., and Esparon, A. (2021). Hyperspectral Monitoring of Non-Native Tropical Grasses over Phenological Seasons. Remote Sens., 13.","DOI":"10.3390\/rs13040738"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1744-697X.2011.00239.x","article-title":"Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging","volume":"58","author":"Suzuki","year":"2012","journal-title":"Grassl. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1071\/RJ17084","article-title":"Hyperspectral database prediction of ecological characteristics for grass species of alpine grasslands","volume":"40","author":"Yu","year":"2018","journal-title":"Rangel. J."},{"key":"ref_31","first-page":"76","article-title":"Evaluating an image analysis system for mapping white clover pastures","volume":"54","author":"Bonesmo","year":"2004","journal-title":"Acta Agric. Scand. Sect.-Soil Plant Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.3389\/fpls.2020.00159","article-title":"Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks","volume":"11","author":"Bateman","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Skovsen, S.K., Laursen, M.S., Kristensen, R.K., Rasmussen, J., Dyrmann, M., Eriksen, J., Gislum, R., J\u00f8rgensen, R.N., and Karstoft, H. (2020). Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks. Sensors, 21.","DOI":"10.3390\/s21010175"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"622429","DOI":"10.3389\/fpls.2021.622429","article-title":"Estimation of Botanical Composition in Mixed Clover\u2013Grass Fields Using Machine Learning-Based Image Analysis","volume":"12","author":"Sun","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hancock, J.T., and Khoshgoftaar, T.M. (2020). CatBoost for big data: An interdisciplinary review. J. Big Data, 7.","DOI":"10.1186\/s40537-020-00369-8"},{"key":"ref_36","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13536971","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6459","DOI":"10.1080\/01431161.2012.690082","article-title":"RLQ and fourth-corner analysis of plant species traits and spectral indices derived from HyMap and CHRIS-PROBA imagery","volume":"33","author":"Oldeland","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow band vegetation indices overcome the saturation problem in biomass estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http:\/\/www.deeplearningbook.org."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, H., Samuels, D.C., yong Zhao, Y., and Guo, Y. (2019). Architectures and accuracy of artificial neural network for disease classification from omics data. BMC Genom., 20.","DOI":"10.1186\/s12864-019-5546-z"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"611622","DOI":"10.3389\/fpls.2020.611622","article-title":"Identification of Weeds Based on Hyperspectral Imaging and Machine Learning","volume":"11","author":"Li","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"163","DOI":"10.5194\/bg-12-163-2015","article-title":"Deploying four optical UAV-based sensors over grassland: Challenges and limitations","volume":"12","author":"Burkart","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mortensen, A.K., Karstoft, H., S\u00f8egaard, K., Gislum, R., and J\u00f8rgensen, R.N. (2017). Preliminary Results of Clover and Grass Coverage and Total Dry Matter Estimation in Clover-Grass Crops Using Image Analysis. J. Imaging, 3.","DOI":"10.3390\/jimaging3040059"},{"key":"ref_45","first-page":"140","article-title":"Maximizing the quantitative utility of airborne hyperspectral imagery for studying plant physiology: An optimal sensor exposure setting procedure and empirical line method for atmospheric correction","volume":"77","author":"Dao","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.2134\/agronj2005.0011","article-title":"Development of Near Infrared Reflectance Spectroscopy Calibrations to Estimate Legume Content of Multispecies Legume-Grass Mixtures","volume":"97","author":"Locher","year":"2005","journal-title":"Agron. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0034-4257(90)90055-Q","article-title":"High resolution derivative spectra in remote sensing","volume":"33","author":"Steven","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.2134\/agronj1991.00021962008300060027x","article-title":"Describing and Quantifying Growth Stages of Perennial Forage Grasses","volume":"83","author":"Moore","year":"1991","journal-title":"Agron. J."},{"key":"ref_49","unstructured":"Sekachev, B., Manovich, N., Zhiltsov, M., Zhavoronkov, A., Kalinin, D., Hoff, B., TOsmanov, Kruchinin, D., Zankevich, A., and DmitriySidnev (2021, December 12). opencv\/cvat: v1.1.0. Available online: https:\/\/zenodo.org\/record\/4009388#.Yhwz-pYRVPY."},{"key":"ref_50","unstructured":"Todorov, V. (2021, December 12). rrcov: Scalable Robust Estimators with High Breakdown Point; R Package Version 1.5\u20135; 2020. Available online: https:\/\/cran.r-project.org\/."},{"key":"ref_51","first-page":"1","article-title":"fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python","volume":"53","year":"2013","journal-title":"J. Stat. Softw."},{"key":"ref_52","unstructured":"Borchers, H.W. (2021, December 12). Pracma: Practical Numerical Math Functions; R Package Version 2.3.3. Available online: https:\/\/cran.r-project.org\/."},{"key":"ref_53","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_54","unstructured":"Dowle, M., and Srinivasan, A. (2021, December 12). Data.Table: Extension of \u2018Data.Frame\u2019; R Package Version 1.14.0. Available online: https:\/\/cran.r-project.org\/."},{"key":"ref_55","unstructured":"Wickham, H. (2021, December 12). Dtplyr: Data Table Back-End for \u2018Dplyr\u2019, R Package Version 1.1.0. Available online: https:\/\/cran.r-project.org\/."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the tidyverse","volume":"4","author":"Wickham","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_57","unstructured":"de Mendiburu, F. (2021, December 12). Agricolae: Statistical Procedures for Agricultural Research; R Package Version 1-3.5. Available online: https:\/\/cran.r-project.org\/."},{"key":"ref_58","unstructured":"Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J.E., and Stoica, I. (2018). Tune: A Research Platform for Distributed Model Selection and Training. arXiv."},{"key":"ref_59","unstructured":"Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., and Jordan, M.I. (2017). Ray: A Distributed Framework for Emerging AI Applications. arXiv."},{"key":"ref_60","first-page":"115","article-title":"Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures","volume":"Volume 28","author":"Dasgupta","year":"2013","journal-title":"Proceedings of the 30th International Conference on Machine Learning"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v018.i02","article-title":"The pls Package: Principal Component and Partial Least Squares Regression in R","volume":"18","author":"Mevik","year":"2007","journal-title":"J. Stat. Softw."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_64","first-page":"630390","article-title":"Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops","volume":"2012","year":"2012","journal-title":"Sci. World J."},{"key":"ref_65","first-page":"1","article-title":"Tunability: Importance of Hyperparameters of Machine Learning Algorithms","volume":"20","author":"Probst","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1109\/72.165592","article-title":"Avoiding false local minima by proper initialization of connections","volume":"3","author":"Wessels","year":"1992","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical Sources of Variability in Canopy Reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1017\/wet.2020.92","article-title":"Phenology affects differentiation of crop and weed species using hyperspectral remote sensing","volume":"34","author":"Basinger","year":"2020","journal-title":"Weed Technol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"389","DOI":"10.5721\/EuJRS20144723","article-title":"A Review of Remote Sensing Image Classification Techniques: The Role of Spatio-contextual Information","volume":"47","author":"Li","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Farzindar, A., and Ke\u0161elj, V. (2010). Robustness of Classifiers to Changing Environments. Advances in Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-642-13059-5"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1154\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:27:59Z","timestamp":1760135279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,26]]},"references-count":70,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051154"],"URL":"https:\/\/doi.org\/10.3390\/rs14051154","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,2,26]]}}}