{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T03:34:54Z","timestamp":1772681694164,"version":"3.50.1"},"reference-count":143,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["FZ 031A352A"],"award-info":[{"award-number":["FZ 031A352A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.<\/jats:p>","DOI":"10.3390\/rs13142670","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T12:31:25Z","timestamp":1625661085000},"page":"2670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding"],"prefix":"10.3390","volume":"13","author":[{"given":"Paul","family":"Herzig","sequence":"first","affiliation":[{"name":"Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0780-5667","authenticated-orcid":false,"given":"Peter","family":"Borrmann","sequence":"additional","affiliation":[{"name":"Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4423-1164","authenticated-orcid":false,"given":"Uwe","family":"Knauer","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstra\u00dfe 22, 39106 Magdeburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9097-4911","authenticated-orcid":false,"given":"Hans-Christian","family":"Kl\u00fcck","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstra\u00dfe 22, 39106 Magdeburg, Germany"}]},{"given":"David","family":"Kilias","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstra\u00dfe 22, 39106 Magdeburg, Germany"}]},{"given":"Udo","family":"Seiffert","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstra\u00dfe 22, 39106 Magdeburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4646-6351","authenticated-orcid":false,"given":"Klaus","family":"Pillen","sequence":"additional","affiliation":[{"name":"Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2916-7475","authenticated-orcid":false,"given":"Andreas","family":"Maurer","sequence":"additional","affiliation":[{"name":"Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00122-019-03321-4","article-title":"Technological perspectives for plant breeding","volume":"132","author":"Godwin","year":"2019","journal-title":"Theor. Appl. Genet."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.eja.2007.07.001","article-title":"Physiological bases of genetic gains in Mediterranean bread wheat yield in Spain","volume":"28","author":"Acreche","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1071\/CP11060","article-title":"Genetic gain in yield and associated changes in phenotype, trait plasticity and competitive ability of South Australian wheat varieties released between 1958 and 2007","volume":"62","author":"Sadras","year":"2011","journal-title":"Crop Pasture Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1111\/agec.12089","article-title":"The future of food demand: Understanding differences in global economic models","volume":"45","author":"Valin","year":"2014","journal-title":"Agric. Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: The new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.tplants.2014.11.006","article-title":"Physiological phenotyping of plants for crop improvement","volume":"20","author":"Ghanem","year":"2015","journal-title":"Trends Plant Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"R770","DOI":"10.1016\/j.cub.2017.05.055","article-title":"Plant Phenomics, From Sensors to Knowledge","volume":"27","author":"Tardieu","year":"2017","journal-title":"Curr. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saiz-Rubio, V., and Rovira-Mas, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy, 10.","DOI":"10.3390\/agronomy10020207"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, C.Y., Marzougui, A., and Sankaran, S. (2020). High-resolution satellite imagery applications in crop phenotyping: An overview. Comput. Electron. Agric., 175.","DOI":"10.1016\/j.compag.2020.105584"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zeng, L.L., Wardlow, B.D., Xiang, D.X., Hu, S., and Li, D.R. (2020). A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sens. Environ., 237.","DOI":"10.1016\/j.rse.2019.111511"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"B\u00e9gu\u00e9, A., Arvor, D., Bellon, B., Betbeder, J., de Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Sim\u00f5es, M., and Ver\u00f3n, S.R. (2018). Remote Sensing and Cropping Practices: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10010099"},{"key":"ref_12","first-page":"14","article-title":"Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks","volume":"57","author":"Fieuzal","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1071\/FP16163","article-title":"Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring","volume":"44","author":"Virlet","year":"2016","journal-title":"Funct. Plant Biol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kicherer, A., Herzog, K., Bendel, N., Kluck, H.C., Backhaus, A., Wieland, M., Rose, J.C., Klingbeil, L., Labe, T., and Hohl, C. (2017). Phenoliner: A New Field Phenotyping Platform for Grapevine Research. Sensors, 17.","DOI":"10.3390\/s17071625"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, J.C., Huang, Y.B., Pu, R.L., Gonzalez-Moreno, P., Yuan, L., Wu, K.H., and Huang, W.J. (2019). Monitoring plant diseases and pests through remote sensing technology: A review. Comput. Electron. Agric., 165.","DOI":"10.1016\/j.compag.2019.104943"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2442","DOI":"10.1038\/srep02442","article-title":"Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation","volume":"3","author":"Busemeyer","year":"2013","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gruber, S., Kwon, H., York, G., and Pack, D. (2018). Payload Design of Small UAVs. Handbook of Unmanned Aerial Vehicles, Springer.","DOI":"10.1007\/978-3-319-32193-6_84-2"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Han, X., Thomasson, J.A., Bagnall, G.C., Pugh, N.A., Horne, D.W., Rooney, W.L., Jung, J., Chang, A., Malambo, L., and Popescu, S.C. (2018). Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images. Sensors, 18.","DOI":"10.3390\/s18124092"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"593","DOI":"10.3389\/fpls.2020.00593","article-title":"PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits","volume":"11","author":"Aasen","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.3389\/fpls.2019.01749","article-title":"Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm","volume":"10","author":"Anderegg","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s13007-020-00577-6","article-title":"Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data","volume":"16","author":"Brien","year":"2020","journal-title":"Plant Methods"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1006\/anbo.1996.0162","article-title":"Regression smoothers for estimating parameters of growth analyses","volume":"78","author":"Shipley","year":"1996","journal-title":"Ann. Bot."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/BF00024012","article-title":"Defining selection criteria to improve yield under drought","volume":"20","author":"Richards","year":"1996","journal-title":"Plant Growth Regul."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.2135\/cropsci2003.1698","article-title":"Associations among twenty years of international bread wheat yield evaluation environments","volume":"43","author":"Trethowan","year":"2003","journal-title":"Crop Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00035338","article-title":"Changes in Physiological Attributes of the Dry-Matter Economy of Bread Wheat (Triticum-Aestivum) through Genetic-Improvement of Grain-Yield Potential at Different Regions of the World\u2014A Review","volume":"58","author":"Slafer","year":"1991","journal-title":"Euphytica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.3389\/fpls.2017.01733","article-title":"Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley","volume":"8","author":"Kefauver","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mart\u00ednez, H., Flores-Magdaleno, H., Ascencio-Hern\u00e1ndez, R., Khalil-Gardezi, A., Tijerina-Ch\u00e1vez, L., Mancilla-Villa, O.R., and V\u00e1zquez-Pe\u00f1a, M.A. (2020). Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles. Agriculture, 10.","DOI":"10.3390\/agriculture10070277"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X., and Fan, L. (2020). Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images. Sensors, 20.","DOI":"10.3390\/s20041231"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.fcr.2014.05.001","article-title":"Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images","volume":"164","author":"Wang","year":"2014","journal-title":"Field Crop. Res."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.2134\/agronj2012.0393","article-title":"Corn Grain and Stover Yield Prediction at R1 Growth Stage","volume":"105","author":"Mourtzinis","year":"2013","journal-title":"Agron. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.2135\/cropsci1999.3961611x","article-title":"Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand","volume":"39","author":"Reynolds","year":"1999","journal-title":"Crop Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1093\/jxb\/erx421","article-title":"Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat","volume":"69","author":"Molero","year":"2018","journal-title":"J. Exp. Bot."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.2135\/cropsci2005.0211","article-title":"Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat","volume":"46","author":"Babar","year":"2006","journal-title":"Crop Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","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_37","first-page":"963","article-title":"Light-Weight Multispectral Uav Sensors and Their Capabilities for Predicting Grain Yield and Detecting Plant Diseases","volume":"XLI-B1","author":"Nebiker","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13007-016-0134-6","article-title":"Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries","volume":"12","author":"Haghighattalab","year":"2016","journal-title":"Plant Methods"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"197","DOI":"10.2135\/cropsci2009.07.0381","article-title":"Spectral Water Indices for Assessing Yield in Elite Bread Wheat Genotypes under Well-Irrigated, Water-Stressed, and High-Temperature Conditions","volume":"50","author":"Gutierrez","year":"2010","journal-title":"Crop Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13007-019-0389-9","article-title":"Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape","volume":"15","author":"Veys","year":"2019","journal-title":"Plant Methods"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11119-014-9383-4","article-title":"Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status","volume":"16","author":"Raper","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_42","unstructured":"Filella, I., and Penuelas, J. (2021, May 07). The Red Edge Postion and Shape as Indicators of Plant Chlorophyll Content, Biomass and Hydric Status. Available online: https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/01431169408954177."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.rse.2009.02.016","article-title":"Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.agrformet.2013.09.007","article-title":"Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras","volume":"184","author":"Nijland","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"259","DOI":"10.13031\/2013.27838","article-title":"Color Indexes for Weed Identification under Various Soil, Residue, and Lighting Conditions","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1098\/rspb.1979.0006","article-title":"The interpretation of structure from motion","volume":"203","author":"Ullman","year":"1979","journal-title":"Proc. R Soc. Lond. B Biol. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"10395","DOI":"10.3390\/rs61110395","article-title":"Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging","volume":"6","author":"Bendig","year":"2014","journal-title":"Remote Sens."},{"key":"ref_49","first-page":"5330","article-title":"UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices","volume":"39","author":"Rizza","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","first-page":"1","article-title":"A Nondestructive Method to Estimate Plant Height, Stem Diameter and Biomass of Rice under Field Conditions Using Digital Image Analysis","volume":"10","author":"Igawa","year":"2017","journal-title":"J. Environ. Sci. Nat. Resour."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1017\/S2040470017000498","article-title":"The use of RGB cameras in defining crop development in legumes","volume":"8","author":"Travlos","year":"2017","journal-title":"Adv. Anim. Biosci."},{"key":"ref_52","first-page":"420","article-title":"Estimation of vegetation fraction using RGB and multispectral images from UAV","volume":"40","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agwat.2015.09.016","article-title":"Comparative performance of remote sensing methods in assessing wheat performance under Mediterranean conditions","volume":"164","author":"Yousfi","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_54","first-page":"650","article-title":"Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease","volume":"172","author":"Mutka","year":"2016","journal-title":"Plant Physiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compag.2015.05.017","article-title":"Low-cost assessment of wheat resistance to yellow rust through conventional RGB images","volume":"116","author":"Zhou","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/0034-4257(95)00238-3","article-title":"Estimating leaf biochemistry using the PROSPECT leaf optical properties model","volume":"56","author":"Jacquemoud","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11119-005-2324-5","article-title":"Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status","volume":"6","author":"Hunt","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Torres-Sanchez, J., Lopez-Granados, F., De Castro, A.I., and Pena-Barragan, J.M. (2013). Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0058210"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"5265","DOI":"10.1080\/01431161.2017.1363441","article-title":"Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery","volume":"39","author":"Lu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1111\/tpj.14799","article-title":"Automatic wheat ear counting using machine learning based on RGB UAV imagery","volume":"103","author":"Lootens","year":"2020","journal-title":"Plant J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yuan, H.H., Yang, G.J., Li, C.C., Wang, Y.J., Liu, J.G., Yu, H.Y., Feng, H.K., Xu, B., Zhao, X.Q., and Yang, X.D. (2017). Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Remote Sens., 9.","DOI":"10.3390\/rs9040309"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization Paths for Generalized Linear Models via Coordinate Descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breimann","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"110396","DOI":"10.1016\/j.plantsci.2019.110396","article-title":"Breeder friendly phenotyping","volume":"295","author":"Reynolds","year":"2020","journal-title":"Plant Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1534\/g3.111.000182","article-title":"High-Resolution Genotyping of Wild Barley Introgression Lines and Fine-Mapping of the Threshability Locus thresh-1 Using the Illumina GoldenGate Assay","volume":"1","author":"Schmalenbach","year":"2011","journal-title":"G3 (Bethesda)"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Honsdorf, N., March, T.J., and Pillen, K. (2017). QTL controlling grain filling under terminal drought stress in a set of wild barley introgression lines. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0185983"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1007\/s00122-004-1818-2","article-title":"Development of candidate introgression lines using an exotic barley accession (Hordeum vulgare ssp. spontaneum) as donor","volume":"109","author":"Wang","year":"2004","journal-title":"Theor. Appl. Genet."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1007\/s00122-008-0847-7","article-title":"Selecting a set of wild barley introgression lines and verification of QTL effects for resistance to powdery mildew and leaf rust","volume":"117","author":"Schmalenbach","year":"2008","journal-title":"Theor. Appl. Genet."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1007\/s00122-010-1276-y","article-title":"Association of barley photoperiod and vernalization genes with QTLs for flowering time and agronomic traits in a BC2DH population and a set of wild barley introgression lines","volume":"120","author":"Wang","year":"2010","journal-title":"Theor. Appl. Genet."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s00122-008-0915-z","article-title":"Identification and verification of QTLs for agronomic traits using wild barley introgression lines","volume":"118","author":"Schmalenbach","year":"2009","journal-title":"Theor. Appl. Genet."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hoffmann, A., Maurer, A., and Pillen, K. (2012). Detection of nitrogen deficiency QTL in juvenile wild barley introgression lines growing in a hydroponic system. BMC Genet., 13.","DOI":"10.1186\/1471-2156-13-88"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1111\/pbr.12540","article-title":"Genetic regulation of growth and nutrient content under phosphorus deficiency in the wild barley introgression library S42IL","volume":"136","author":"Soleimani","year":"2017","journal-title":"Plant Breed."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s11032-014-0131-2","article-title":"Evaluation of juvenile drought stress tolerance and genotyping by sequencing with wild barley introgression lines","volume":"34","author":"Honsdorf","year":"2014","journal-title":"Mol. Breed."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Honsdorf, N., March, T.J., Berger, B., Tester, M., and Pillen, K. (2014). High-throughput phenotyping to detect drought tolerance QTL in wild barley introgression lines. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0097047"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1104\/pp.18.00169","article-title":"An Ancestral Allele of Pyrroline-5-carboxylate synthase1 Promotes Proline Accumulation and Drought Adaptation in Cultivated Barley","volume":"178","author":"Muzammil","year":"2018","journal-title":"Plant Physiol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zahn, S., Koblenz, B., Christen, O., Pillen, K., and Maurer, A. (2020). Evaluation of wild barley introgression lines for agronomic traits related to nitrogen fertilization. Euphytica, 216.","DOI":"10.1007\/s10681-020-2571-6"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1744-7348.1991.tb04895.x","article-title":"A Uniform Decimal Code for Growth-Stages of Crops and Weeds","volume":"119","author":"Lancashire","year":"1991","journal-title":"Ann. Appl. Biol."},{"key":"ref_82","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical computing, Foundation for Statistical Computing, European Environment Agency."},{"key":"ref_83","unstructured":"Hijmans, R.J. (2020, May 01). \u2018Raster\u2019\u2014Geographic Data Analysis and Modeling. Available online: https:\/\/CRAN.R-project.org\/package=raster."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_85","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Remote Sensing Center, Texas A&M Univ.. Prog. Rep. RSC 1978-1, NTIS No. E73-106393."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1080\/014311697217396","article-title":"Estimation of plant water concentration by the reflectance water index WI (R900\/R970)","volume":"18","author":"Penuelas","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves","volume":"19","author":"Blackburn","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","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_89","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative Estimation of Chlorophyll-a Using Reflectance Spectra\u2014Experiments with Autumn Chestnut and Maple Leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photoch Photobiol. B"},{"key":"ref_90","unstructured":"Guyot, G., Baret, F., and Major, D.J. (1988, January 1\u201310). High spectral resolution: Determination of spectral shifts between the red and the near infrared. Proceedings of the ISPRS Congress, Kyoto, Japan."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/01431168308948546","article-title":"Red edge measurements for remotely sensing plant chlorophyll content","volume":"4","author":"Horler","year":"1983","journal-title":"Int. J. Remote. Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/01431169308953986","article-title":"Red edge spectral measurements from sugar maple leaves","volume":"14","author":"Vogelmann","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/36.934080","article-title":"Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data","volume":"39","author":"Miller","year":"2001","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_94","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_95","first-page":"103","article-title":"A visible band index for remote sensing leaf chlorophyll content at the canopy scale","volume":"21","author":"Hunt","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_96","unstructured":"Gitelson, A.A., Merzlyak, M.N., Zur, Y., Stark, R., and Gritz, U. (2001, January 18\u201320). Non-destructive and remote sensing techniques for estimation of vegetation status. Proceedings of the 3rd European Conference on Precision Agriculture, Montpelier, France."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. ManCybern."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1111\/jmi.12474","article-title":"Automatic thresholding from the gradients of region boundaries","volume":"265","author":"Landini","year":"2017","journal-title":"J. Microsc."},{"key":"ref_99","first-page":"23","article-title":"Statistical Geocomputing Combining R and SAGA: The Example of Landslide Susceptibility Analysis with Generalized Additive Models","volume":"19","author":"Brenning","year":"2008","journal-title":"Hamburger Beitr\u00e4ge zur Physischen Geographie und Landschafts\u00f6kologie"},{"key":"ref_100","unstructured":"Kuhn, M., and Wickham, H. (2020, May 01). Tidymodels: Easily Install and Load the \u2019Tidymodels\u2019 Packages. R package version 0.1.2. Available online: https:\/\/CRAN.R-project.or\/ackage=tidymodels."},{"key":"ref_101","unstructured":"Bates, D., Kliegl, R., Vasishth, S., and Baayen, H. (2018). Parsimonious Mixed Models. arXiv."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1080\/01621459.1955.10501294","article-title":"A Multiple Comparison Procedure for Comparing Several Treatments with a Control","volume":"50","author":"Dunnett","year":"1955","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1002\/bimj.200810425","article-title":"Simultaneous inference in general parametric models","volume":"50","author":"Hothorn","year":"2008","journal-title":"Biom J."},{"key":"ref_104","unstructured":"Bretz, F., Hothorn, T., and Westfall, P. (2010). Multiple Comparisons Using R, CRC Press."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Bareth, G., Bendig, J., Tilly, N., Hoffmeister, D., Aasen, H., and Bolten, A. (2016). A Comparison of UAV- and TLS-derived Plant Height for Crop Monitoring: Using Polygon Grids for the Analysis of Crop Surface Models (CSMs). Photogramm Fernerkun.","DOI":"10.1127\/pfg\/2016\/0289"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Holman, F.H., Riche, A.B., Michalski, A., Castle, M., Wooster, M.J., and Hawkesford, M.J. (2016). High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing. Remote Sens., 8.","DOI":"10.3390\/rs8121031"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_108","unstructured":"Pask, A., Pietragalla, J., Mullan, D.M., and Reynolds, M.P. (2012). Physiological Breeding II: A Field Guide to Wheat Phenotyping, CIMMYT."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"135","DOI":"10.5194\/isprsarchives-XL-1-135-2014","article-title":"Crop height determination with UAS point clouds","volume":"XL-1","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10681-010-0175-2","article-title":"Identification and molecular mapping of a dwarfing gene in barley (Hordeum vulgare L.) and its correlation with other agronomic traits","volume":"175","author":"Wang","year":"2010","journal-title":"Euphytica"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Resop, J.P., Lehmann, L., and Hession, W.C. (2019). Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar. Drones, 3.","DOI":"10.3390\/drones3020035"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2015.10.011","article-title":"LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics?","volume":"119","author":"Lin","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2017.04.007","article-title":"UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA","volume":"195","author":"Sankey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Zhou, L.F., Gu, X.H., Cheng, S., Yang, G.J., Shu, M.Y., and Sun, Q. (2020). Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data. Agriculture, 10.","DOI":"10.3390\/agriculture10050146"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"ten Harkel, J., Bartholomeus, H., and Kooistra, L. (2019). Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar. Remote Sens., 12.","DOI":"10.3390\/rs12010017"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Maesano, M., Khoury, S., Nakhle, F., Firrincieli, A., Gay, A., Tauro, F., and Harfouche, A. (2020). UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo donax. Remote Sens., 12.","DOI":"10.3390\/rs12203464"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"L\u00f3opez-Casta\u00f1eda, C., Richards, R.A., and Farquhar, G.D. (1995). Variation in Early Vigor between Wheat and Barley. Crop Sci., 35.","DOI":"10.2135\/cropsci1995.0011183X003500020032x"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1071\/FP09277","article-title":"Quantifying genetic effects of ground cover on soil water evaporation using digital imaging","volume":"37","author":"Mullan","year":"2010","journal-title":"Funct. Plant Biol."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1071\/FP14226","article-title":"Image based phenotyping during winter: A powerful tool to assess wheat genetic variation in growth response to temperature","volume":"42","author":"Grieder","year":"2015","journal-title":"Funct. Plant Biol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1007\/s11119-018-9560-y","article-title":"Onion biomass monitoring using UAV-based RGB imaging","volume":"19","author":"Ballesteros","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Kim, S.L., Chung, Y.S., Ji, H., Lee, H., Choi, I., Kim, N., Lee, E., Oh, J., Kang, D.Y., and Baek, J. (2019). New Parameters for Seedling Vigor Developed via Phenomics. Appl. Sci., 9.","DOI":"10.3390\/app9091752"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1016\/j.agrformet.2010.08.002","article-title":"Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops","volume":"150","author":"Liu","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13007-015-0048-8","article-title":"Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach","volume":"11","author":"Liebisch","year":"2015","journal-title":"Plant Methods"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13007-017-0168-4","article-title":"An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping","volume":"13","author":"Yu","year":"2017","journal-title":"Plant Methods"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.isprsjprs.2019.09.017","article-title":"Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing","volume":"158","author":"Yan","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2014.02.009","article-title":"Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV","volume":"103","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3389\/fpls.2017.01111","article-title":"Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives","volume":"8","author":"Yang","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Oehlschlager, J., Schmidhalter, U., and Noack, P.O. (2018, January 23\u201326). UAV-Based Hyperspectral Sensing for Yield Prediction in Winter Barley. Proceedings of the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2018.8747260"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"10335","DOI":"10.3390\/rs61110335","article-title":"Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System","volume":"6","author":"Geipel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.eja.2016.04.013","article-title":"Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley","volume":"78","author":"Rischbeck","year":"2016","journal-title":"Eur. J. Agron."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Yang, G.J., Li, C.C., Li, Z.H., Wang, Y.J., Feng, H.K., and Xu, B. (2017). Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Feng, H.K., Jin, X.L., Yuan, H.H., Li, Z.H., Zhou, C.Q., Yang, G.J., and Tian, Q.J. (2018). A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.plantsci.2019.05.008","article-title":"Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population","volume":"285","author":"Herzig","year":"2019","journal-title":"Plant Sci."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.plantsci.2018.10.022","article-title":"A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform","volume":"282","author":"Hassan","year":"2019","journal-title":"Plant Sci."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11119-017-9501-1","article-title":"Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: An applied photogrammetric approach","volume":"19","author":"Roth","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"578","DOI":"10.2135\/cropsci2005.0059","article-title":"Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation","volume":"46","author":"Babar","year":"2006","journal-title":"Crop Sci."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.isprsjprs.2011.08.001","article-title":"An investigation into robust spectral indices for leaf chlorophyll estimation","volume":"66","author":"Main","year":"2011","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.2135\/cropsci2014.08.0533","article-title":"Evaluating Grain Yield in Spring Wheat with Canopy Spectral Reflectance","volume":"55","author":"Bowman","year":"2015","journal-title":"Crop Sci."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2004.06.002","article-title":"Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks","volume":"92","author":"Smith","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1080\/01431160110114529","article-title":"Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations","volume":"23","author":"Lamb","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/01431169008955127","article-title":"Shape of the red edge as vitality indicator for plants","volume":"11","author":"Boochs","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Wiegmann, M., Backhaus, A., Seiffert, U., Thomas, W.T.B., Flavell, A.J., Pillen, K., and Maurer, A. (2019). Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224491"},{"key":"ref_143","first-page":"35","article-title":"Agricultural UAVs in the U.S.: Potential, policy, and hype","volume":"2","author":"Freeman","year":"2015","journal-title":"Remote Sens. Appl. Soc. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:26Z","timestamp":1760164046000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/14\/2670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":143,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13142670"],"URL":"https:\/\/doi.org\/10.3390\/rs13142670","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,7]]}}}