{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T05:39:11Z","timestamp":1768714751343,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T00:00:00Z","timestamp":1528934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["LP140100347"],"award-info":[{"award-number":["LP140100347"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["IH130200027"],"award-info":[{"award-number":["IH130200027"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R2 = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R2 = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R2 = 0.57), compared to MGP imagery (RMSE = 0.063, R2 = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.<\/jats:p>","DOI":"10.3390\/rs10060950","type":"journal-article","created":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T11:06:06Z","timestamp":1528974366000},"page":"950","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6294-0688","authenticated-orcid":false,"given":"Zohaib","family":"Khan","sequence":"first","affiliation":[{"name":"Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide 5095, Australia"}]},{"given":"Joshua","family":"Chopin","sequence":"additional","affiliation":[{"name":"Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide 5095, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7298-1579","authenticated-orcid":false,"given":"Jinhai","family":"Cai","sequence":"additional","affiliation":[{"name":"Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide 5095, Australia"}]},{"given":"Vahid-Rahimi","family":"Eichi","sequence":"additional","affiliation":[{"name":"School of Agriculture, Food and Wine, University of Adelaide, Adelaide 5064, Australia"}]},{"given":"Stephan","family":"Haefele","sequence":"additional","affiliation":[{"name":"School of Agriculture, Food and Wine, University of Adelaide, Adelaide 5064, Australia"}]},{"given":"Stanley J.","family":"Miklavcic","sequence":"additional","affiliation":[{"name":"Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide 5095, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,14]]},"reference":[{"key":"ref_1","unstructured":"Salisbury, F.B., and Ross, C.W. (1992). Plant Physiology, Wadsworth Publishing. [4th ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Patrick, A., and Li, C. (2017). High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sens., 9.","DOI":"10.3390\/rs9121250"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gn\u00e4dinger, F., and Schmidhalter, U. (2017). Digital counts of maize plants by unmanned aerial vehicles (UAVs). Remote Sens., 9.","DOI":"10.3390\/rs9060544"},{"key":"ref_4","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":"2017","journal-title":"Funct. Plant Biol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cai, J., Kumar, P., Chopin, J., and Miklavcic, S.J. (2018). Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196671"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"349","DOI":"10.3390\/agronomy4030349","article-title":"Proximal remote sensing buggies and potential applications for field-based phenotyping","volume":"4","author":"Deery","year":"2014","journal-title":"Agronomy"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schirrmann, M., Giebel, A., Gleiniger, F., Pflanz, M., Lentschke, J., and Dammer, K.H. (2016). Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens., 8.","DOI":"10.3390\/rs8090706"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"de Castro, A.I., Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., Jim\u00e9nez-Brenes, F.M., Csillik, O., and L\u00f3pez-Granados, F. (2018). An automatic Random Forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020285"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.eja.2013.08.009","article-title":"High-throughput phenotyping early plant vigour of winter wheat","volume":"52","author":"Kipp","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Di Gennaro, S.F., Rizza, F., Badeck, F.W., Berton, A., Delbono, S., Gioli, B., Toscano, P., Zaldei, A., and Matese, A. (2017). UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices. Int. J. Remote Sens.","DOI":"10.1080\/01431161.2017.1395974"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"421","DOI":"10.3389\/fpls.2017.00421","article-title":"High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling","volume":"8","author":"Watanabe","year":"2017","journal-title":"Front. Plant. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"giy001","DOI":"10.1093\/gigascience\/giy001","article-title":"Predicting plant biomass accumulation from image-derived parameters","volume":"7","author":"Chen","year":"2018","journal-title":"GigaScience"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.biosystemseng.2009.07.001","article-title":"Estimation of leaf area index in cereal crops using red\u2013green images","volume":"104","author":"Kirk","year":"2009","journal-title":"Biosyst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Makanza, R., Zaman-Allah, M., Cairns, J.E., Magorokosho, C., Tarekegne, A., Olsen, M., and Prasanna, B.M. (2018). High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging. Remote Sens., 10.","DOI":"10.3390\/rs10020330"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3389\/fpls.2018.00237","article-title":"High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR","volume":"9","author":"Deery","year":"2018","journal-title":"Front. Plant. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/0034-4257(93)90113-C","article-title":"On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna","volume":"45","author":"Benedetti","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of vegetation indices for agricultural crop yield prediction using neural network techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.eja.2006.10.007","article-title":"A simple model of regional wheat yield based on NDVI data","volume":"26","author":"Moriondo","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2134\/agronj2001.931131x","article-title":"In-season prediction of potential grain yield in winter wheat using canopy reflectance","volume":"93","author":"Raun","year":"2001","journal-title":"Agron. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.agrformet.2015.11.009","article-title":"Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality","volume":"217","author":"Magney","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A survey of image processing techniques for plant extraction and segmentation in the field","volume":"125","author":"Hamuda","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","unstructured":"Mao, W., Wang, Y., and Wang, Y. (2003, January 27\u201330). Real-time detection of between-row weeds using machine vision. Proceedings of the ASAE Annual Meeting. American Society of Agricultural and Biological Engineers, Las Vegas, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of green-red vegetation index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eja.2015.11.026","article-title":"Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?","volume":"74","author":"Rasmussen","year":"2016","journal-title":"Eur. J. Agron."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"6545","DOI":"10.5194\/bg-13-6545-2016","article-title":"Crop water stress maps for an entire growing season from visible and thermal UAV imagery","volume":"13","author":"Hoffmann","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.fcr.2010.12.017","article-title":"High-throughput non-destructive biomass determination during early plant development in maize under field conditions","volume":"121","author":"Montes","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.fcr.2007.11.002","article-title":"Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic","volume":"106","author":"Mistele","year":"2008","journal-title":"Field Crops Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"499","DOI":"10.2134\/agronj2009.0282","article-title":"Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat","volume":"102","author":"Mistele","year":"2010","journal-title":"Agron. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1071\/FP13126","article-title":"Development and evaluation of a field-based high-throughput phenotyping platform","volume":"41","author":"Gore","year":"2014","journal-title":"Funct. Plant Biol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"530","DOI":"10.2134\/agronj2006.0135","article-title":"By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height","volume":"99","author":"Freeman","year":"2007","journal-title":"Agron. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.2135\/cropsci2013.01.0054","article-title":"A flexible, low-cost cart for proximal sensing","volume":"53","author":"White","year":"2013","journal-title":"Crop Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating multispectral images and vegetation indices for precision farming applications from UAV images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.3389\/fpls.2017.02004","article-title":"Comparative performance of ground versus aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization","volume":"8","author":"Kefauver","year":"2017","journal-title":"Front. Plant. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vergara-D\u00edaz, O., Zaman-Allah, M.A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B.M., Cairns, J.E., and Araus, J.L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Front. Plant Sci., 7.","DOI":"10.3389\/fpls.2016.00666"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.3389\/fpls.2017.02002","article-title":"High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR estimates","volume":"8","author":"Madec","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13007-018-0308-5","article-title":"Land-based crop phenotyping by image analysis: Consistent canopy characterization from inconsistent field illumination","volume":"14","author":"Chopin","year":"2018","journal-title":"Plant Methods"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmueller","year":"2008","journal-title":"Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1364\/JOSAA.8.000377","article-title":"Affine structure from motion","volume":"8","author":"Koenderink","year":"1991","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_44","unstructured":"Heikkila, J., and Silven, O. (1997, January 17\u201319). A four-step camera calibration procedure with implicit image correction. Proceedings of the Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s13007-018-0287-6","article-title":"Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging","volume":"14","author":"Khan","year":"2018","journal-title":"Plant Methods"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ni, J., Yao, L., Zhang, J., Cao, W., Zhu, Y., and Tai, X. (2017). Development of an unmanned aerial vehicle-borne crop-growth monitoring system. Sensors, 17.","DOI":"10.3390\/s17030502"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/950\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:08:46Z","timestamp":1760195326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,14]]},"references-count":48,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060950"],"URL":"https:\/\/doi.org\/10.3390\/rs10060950","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,14]]}}}