{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:37:20Z","timestamp":1775759840186,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T00:00:00Z","timestamp":1630713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2017-67021-25928"],"award-info":[{"award-number":["2017-67021-25928"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 \u00b0C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.<\/jats:p>","DOI":"10.3390\/rs13173517","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"3517","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture"],"prefix":"10.3390","volume":"13","author":[{"given":"Rui","family":"Xu","sequence":"first","affiliation":[{"name":"Bio-Sensing and Instrumentation Laboratory, College of Engineering, The University of Georgia, Athens, GA WJXF 63, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2590-4797","authenticated-orcid":false,"given":"Changying","family":"Li","sequence":"additional","affiliation":[{"name":"Bio-Sensing and Instrumentation Laboratory, College of Engineering, The University of Georgia, Athens, GA WJXF 63, USA"},{"name":"Phenomics and Plant Robotics Center, The University of Georgia, Athens, GA WJXF 63, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-1939","authenticated-orcid":false,"given":"Sergio","family":"Bernardes","sequence":"additional","affiliation":[{"name":"Phenomics and Plant Robotics Center, The University of Georgia, Athens, GA WJXF 63, USA"},{"name":"Center for Geospatial Research, The University of Georgia, Athens, GA WJXF 63, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,4]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.3390\/s130302830","article-title":"BreedVision\u2014A multi-sensor platform for non-destructive field-based phenotyping in plant breeding","volume":"13","author":"Busemeyer","year":"2013","journal-title":"Sensors"},{"key":"ref_3","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_4","first-page":"1","article-title":"GPhenoVision: A ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton","volume":"8","author":"Jiang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.eja.2015.07.004","article-title":"Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review","volume":"70","author":"Sankaran","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.compag.2021.106033","article-title":"A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping","volume":"182","author":"Feng","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.compag.2020.105731","article-title":"A review on plant high-throughput phenotyping traits using UAV-based sensors","volume":"178","author":"Xie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-UI-Karim, S.T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13007-019-0399-7","article-title":"The estimation of crop emergence in potatoes by UAV RGB imagery","volume":"15","author":"Li","year":"2019","journal-title":"Plant Methods"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.3389\/fpls.2017.02235","article-title":"Aerial images and convolutional neural network for cotton bloom detection","volume":"8","author":"Xu","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.3390\/rs70404213","article-title":"High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: Application in breeding trials","volume":"7","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"290","DOI":"10.3390\/rs2010290","article-title":"Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring","volume":"2","author":"Hunt","year":"2010","journal-title":"Remote Sens."},{"key":"ref_18","first-page":"1","article-title":"Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize","volume":"11","author":"Vergara","year":"2015","journal-title":"Plant Methods"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MIM.2017.7951684","article-title":"High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging","volume":"20","author":"Patrick","year":"2017","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, R., Li, C., and Paterson, A.H. (2019). Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0205083"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3389\/fpls.2020.00927","article-title":"Assessment of water and nitrogen use efficiencies through UAV-based multispectral phenotyping in winter wheat","volume":"11","author":"Yang","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1029\/WR013i003p00651","article-title":"Wheat canopy temperature: A practical tool for evaluating water requirements","volume":"13","author":"Jackson","year":"1977","journal-title":"Water Resour. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1029\/WR017i004p01133","article-title":"Canopy temperature as a crop water stress indicator","volume":"17","author":"Jackson","year":"1981","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1007\/s11119-013-9322-9","article-title":"Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard","volume":"14","author":"Nortes","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1007\/s11119-016-9449-6","article-title":"Field phenotyping of water stress at tree scale by UAV-sensed imagery: New insights for thermal acquisition and calibration","volume":"17","author":"Virlet","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.3389\/fpls.2019.01270","article-title":"Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13586","DOI":"10.3390\/rs71013586","article-title":"Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping","volume":"7","author":"Hernandez","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.isprsjprs.2013.10.002","article-title":"Assessing canopy PRI from airborne imagery to map water stress in maize","volume":"86","author":"Rossini","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s11119-019-09703-4","article-title":"Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques","volume":"21","author":"Abdulridha","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deng, X., Zhu, Z., Yang, J., Zheng, Z., Huang, Z., Yin, X., Wei, S., and Lan, Y. (2020). Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing. Remote Sens., 12.","DOI":"10.3390\/rs12172678"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Matese, A., and Di Gennaro, S.F. (2018). Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture. Agriculture, 8.","DOI":"10.3390\/agriculture8070116"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kelly, J., Kljun, N., Olsson, P.O., Mihai, L., Liljeblad, B., Weslien, P., Klemedtsson, L., and Eklundh, L. (2019). Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera. Remote Sens., 11.","DOI":"10.3390\/rs11050567"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TPAMI.2010.55","article-title":"Vignette and exposure calibration and compensation","volume":"32","author":"Goldman","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mamaghani, B., and Salvaggio, C. (2019). Multispectral sensor calibration and characterization for sUAS remote sensing. Sensors, 19.","DOI":"10.3390\/s19204453"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12305","DOI":"10.3390\/s140712305","article-title":"Infrared Thermography for Temperature Measurement and Non-Destructive Testing","volume":"14","author":"Usamentiaga","year":"2014","journal-title":"Sensors"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Minkina, W., and Klecha, D. (2015, January 19\u201321). 1.4-Modeling of Atmospheric Transmission Coefficient in Infrared for Thermovision Measurements. Proceedings of the IRS2 2015, Dresden, Germany.","DOI":"10.5162\/irs2015\/1.4"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Messina, G., and Modica, G. (2020). Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook. Remote Sens., 12.","DOI":"10.3390\/rs12091491"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11387","DOI":"10.3390\/s150511387","article-title":"Determining the leaf emissivity of three crops by infrared thermometry","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1117\/12.342307","article-title":"Emissivity measurement and temperature correction accuracy considerations","volume":"Volume 3700","author":"Madding","year":"1999","journal-title":"Thermosense XXI"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Heinemann, S., Siegmann, B., Thonfeld, F., Muro, J., Jedmowski, C., Kemna, A., Kraska, T., Muller, O., Schultz, J., and Udelhoven, T. (2020). Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor. Remote Sens., 12.","DOI":"10.3390\/rs12071075"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Christiansen, M.P., Laursen, M.S., J\u00f8rgensen, R.N., Skovsen, S., and Gislum, R. (2017). Designing and testing a UAV mapping system for agricultural field surveying. Sensors, 17.","DOI":"10.3390\/s17122703"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1006\/cviu.1999.0832","article-title":"MLESAC: A new robust estimator with application to estimating image geometry","volume":"78","author":"Torr","year":"2000","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1127\/pfg\/2016\/0289","article-title":"A comparison of UAV-and TLS-derived plant height for crop monitoring: Using polygon grids for the analysis of crop surface models (CSMs)","volume":"2016","author":"Bareth","year":"2016","journal-title":"Photogramm.-Fernerkund.-Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3389\/fpls.2018.00016","article-title":"In-field high throughput phenotyping and cotton plant growth analysis using LiDAR","volume":"9","author":"Sun","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.2134\/agronj13.0578","article-title":"Evaluation of vegetation indices for early assessment of corn status and yield potential in the Southeastern United States","volume":"106","author":"Torino","year":"2014","journal-title":"Agron. J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shi, Y., Thomasson, J.A., Murray, S.C., Pugh, N.A., Rooney, W.L., Shafian, S., Rajan, N., Rouze, G., Morgan, C.L., and Neely, H.L. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0159781"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Seifert, E., Seifert, S., Vogt, H., Drew, D., Van Aardt, J., Kunneke, A., and Seifert, T. (2019). Influence of drone altitude, image overlap, and optical sensor resolution on multi-view reconstruction of forest images. Remote Sens., 11.","DOI":"10.3390\/rs11101252"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Aragon, B., Johansen, K., Parkes, S., Malbeteau, Y., Al-Mashharawi, S., Al-Amoudi, T., Andrade, C.F., Turner, D., Lucieer, A., and McCabe, M.F. (2020). A calibration procedure for field and UAV-based uncooled thermal infrared instruments. Sensors, 20.","DOI":"10.3390\/s20113316"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2019.01.016","article-title":"Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols","volume":"149","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.biosystemseng.2020.02.014","article-title":"Yield estimation in cotton using UAV-based multi-sensor imagery","volume":"193","author":"Feng","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_55","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3517\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:56:36Z","timestamp":1760165796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,4]]},"references-count":55,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173517"],"URL":"https:\/\/doi.org\/10.3390\/rs13173517","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,4]]}}}