{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:48:38Z","timestamp":1771652918991,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Modeling cotton plant growth is an important aspect of improving cotton yields and fiber quality and optimizing land management strategies. High-throughput phenotyping (HTP) systems, including those using high-resolution imagery from unmanned aerial systems (UAS) combined with sensor technologies, can accurately measure and characterize phenotypic traits such as plant height, canopy cover, and vegetation indices. However, manual assessment of plant characteristics is still widely used in practice. It is time-consuming, labor-intensive, and prone to human error. In this study, we investigated the use of a data-processing pipeline to estimate cotton plant height using UAS-derived visible-spectrum vegetation indices and photogrammetric products. Experiments were conducted at an experimental cotton field in Aliartos, Greece, using a DJI Phantom 4 UAS in five different stages of the 2022 summer cultivation season. Ground Control Points (GCPs) were marked in the field and used for georeferencing and model optimization. The imagery was used to generate dense point clouds, which were then used to create Digital Surface Models (DSMs), while specific Digital Elevation Models (DEMs) were interpolated from RTK GPS measurements. Three (3) vegetation indices were calculated using visible spectrum reflectance data from the generated orthomosaic maps, and ground coverage from the cotton canopy was also calculated by using binary masks. Finally, the correlations between the indices and crop height were examined. The results showed that vegetation indices, especially Green Chromatic Coordinate (GCC) and Normalized Excessive Green (NExG) indices, had high correlations with cotton height in the earlier growth stages and exceeded 0.70, while vegetation cover showed a more consistent trend throughout the season and exceeded 0.90 at the beginning of the season.<\/jats:p>","DOI":"10.3390\/rs15051214","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T01:31:06Z","timestamp":1677115866000},"page":"1214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cotton Growth Modelling Using UAS-Derived DSM and RGB Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2046-7674","authenticated-orcid":false,"given":"Vasilis","family":"Psiroukis","sequence":"first","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6246-3922","authenticated-orcid":false,"given":"George","family":"Papadopoulos","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7620-3782","authenticated-orcid":false,"given":"Aikaterini","family":"Kasimati","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6248-4333","authenticated-orcid":false,"given":"Nikos","family":"Tsoulias","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Geisenheim University, Von-Lade-Str. 1, D-65366 Geisenheim, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Spyros","family":"Fountas","sequence":"additional","affiliation":[{"name":"Laboratory of Agricultural Engineering, Department of Natural Resources Management & Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.3389\/fpls.2017.02233","article-title":"Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera","volume":"8","author":"Jiang","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, S., Li, C., Paterson, A., Jiang, Y., and Robertson, J. (August, January 29). 3D Computer Vision and Machine Learning Based Technique for High Throughput Cotton Boll Mapping under Field Conditions. Proceedings of the 2018 ASABE Annual International Meeting, Detroit, MI, USA.","DOI":"10.13031\/aim.201800677"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.2134\/agronj2002.1305","article-title":"Variability in Cotton Fiber Yield, Fiber Quality, and Soil Properties in a Southeastern Coastal Plain","volume":"94","author":"Johnson","year":"2002","journal-title":"Agron. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.2135\/cropsci2013.08.0550","article-title":"Genetics, Breeding, and Marker-Assisted Selection for Verticillium Wilt Resistance in Cotton","volume":"54","author":"Zhang","year":"2014","journal-title":"Crop Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S-99","DOI":"10.2135\/cropsci2009.09.0525","article-title":"Mobilizing Science to Break Yield Barriers","volume":"50","author":"Phillips","year":"2010","journal-title":"Crop Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Thompson, A., Thorp, K., Conley, M., Elshikha, D., French, A., Andrade-Sanchez, P., and Pauli, D. (2019). Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton. Remote Sens., 11.","DOI":"10.3390\/rs11060700"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ashapure, A., Jung, J., Chang, A., Oh, S., Maeda, M., and Landivar, J. (2019). A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data. Remote Sens., 11.","DOI":"10.3390\/rs11232757"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cois.2015.03.002","article-title":"Applying High-Throughput Phenotyping to Plant\u2013Insect Interactions: Picturing More Resistant Crops","volume":"9","author":"Goggin","year":"2015","journal-title":"Curr. Opin. Insect Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1111\/nph.13529","article-title":"Phenotyping in the Fields: Dissecting the Genetics of Quantitative Traits and Digital Farming","volume":"207","author":"Pieruschka","year":"2015","journal-title":"New Phytol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"619","DOI":"10.3389\/fpls.2015.00619","article-title":"Advanced Phenotyping and Phenotype Data Analysis for the Study of Plant Growth and Development","volume":"6","author":"Rahaman","year":"2015","journal-title":"Front. Plant Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, S., Li, C., and Paterson, A. (2017). In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR. Remote Sens., 9.","DOI":"10.3389\/fpls.2018.00016"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"036018","DOI":"10.1117\/1.JRS.10.036018","article-title":"Cotton Growth Modeling and Assessment Using Unmanned Aircraft System Visual-Band Imagery","volume":"10","author":"Chu","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.biosystemseng.2020.11.010","article-title":"Combining Plant Height, Canopy Coverage and Vegetation Index from UAV-Based RGB Images to Estimate Leaf Nitrogen Concentration of Summer Maize","volume":"202","author":"Lu","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.13031\/2013.32484","article-title":"Weather Simulation for Crop Management Models","volume":"28","author":"Richardson","year":"1985","journal-title":"Trans. ASAE"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"63","DOI":"10.13031\/2013.33877","article-title":"Modeling Soybean Growth for Crop Management","volume":"26","author":"Wilkerson","year":"1983","journal-title":"Trans. ASAE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s42397-019-0035-0","article-title":"High-Throughput Phenotyping in Cotton: A Review","volume":"2","author":"Pabuayon","year":"2019","journal-title":"J. Cotton Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Thapa, S., Zhu, F., Walia, H., Yu, H., and Ge, Y. (2018). A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum. Sensors, 18.","DOI":"10.3390\/s18041187"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.2134\/agronj2012.0206","article-title":"Relationship of Corn Yield, Biomass, and Leaf Nitrogen with Normalized Difference Vegetation Index and Plant Height","volume":"105","author":"Yin","year":"2013","journal-title":"Agron. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree Height Quantification Using Very High Resolution Imagery Acquired from an Unmanned Aerial Vehicle (UAV) and Automatic 3D Photo-Reconstruction Methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1111\/j.1469-8137.2008.02705.x","article-title":"Agriculture and the New Challenges for Photosynthesis Research","volume":"181","author":"Murchie","year":"2009","journal-title":"New Phytol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.2134\/agronj2002.1317","article-title":"Canopy Light Environment and Yield of Narrow-Row Cotton as Affected by Canopy Architecture","volume":"94","author":"Fowler","year":"2002","journal-title":"Agron. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1146\/annurev-arplant-042809-112206","article-title":"Improving Photosynthetic Efficiency for Greater Yield","volume":"61","author":"Zhu","year":"2010","journal-title":"Annu. Rev. Plant Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s11104-009-0252-2","article-title":"Ground-Based Canopy Sensing for Detecting Effects of Water Stress in Cotton","volume":"331","author":"Stamatiadis","year":"2010","journal-title":"Plant Soil"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"083671","DOI":"10.1117\/1.JRS.8.083671","article-title":"Multitemporal Crop Surface Models: Accurate Plant Height Measurement and Biomass Estimation with Terrestrial Laser Scanning in Paddy Rice","volume":"8","author":"Tilly","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_26","unstructured":"Miley, W.N., and Oosterhuis, D.M. (2015). Nitrogen Nutrition of Cotton: Practical Issues, American Society of Agronomy. ASA, CSSA, and SSSA Books."},{"key":"ref_27","first-page":"72","article-title":"Analysis of Cotton Height Spatial Variability Based on UAV-LiDAR","volume":"1","author":"Liu","year":"2018","journal-title":"Int. J. Precis. Agric. Aviat."},{"key":"ref_28","first-page":"23","article-title":"Cotton Yield Assessment Using Plant Height Mapping System","volume":"5","author":"Sui","year":"2012","journal-title":"J. Agric. Sci."},{"key":"ref_29","unstructured":"Oosterhuis, D.M., Kosmidou, K.K., and Cothren, J.T. (1998, January 6\u201312). Managing Cotton Growth and Development with Plant Growth Regulators. Proceedings of the World Cotton Research Conference-2, Athens, Greece."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.agrformet.2012.11.013","article-title":"Relationship between Tree Row LIDAR-Volume and Leaf Area Density for Fruit Orchards and Vineyards Obtained with a LIDAR 3D Dynamic Measurement System","volume":"171\u2013172","author":"Sanz","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Martinez-Guanter, J., Ribeiro, \u00c1., Peteinatos, G.G., P\u00e9rez-Ruiz, M., Gerhards, R., Bengochea-Guevara, J.M., Machleb, J., and And\u00fajar, D. (2019). Low-Cost Three-Dimensional Modeling of Crop Plants. Sensors, 19.","DOI":"10.3390\/s19132883"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.compag.2016.01.017","article-title":"Development of a Field-Based High-Throughput Mobile Phenotyping Platform","volume":"122","author":"Barker","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"958","DOI":"10.2135\/cropsci2014.04.0310","article-title":"High-Throughput Phenotyping of Cotton in Multiple Irrigation Environments","volume":"55","author":"Sharma","year":"2015","journal-title":"Crop Sci."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.plantsci.2015.07.010","article-title":"Next Generation Breeding","volume":"242","author":"Barabaschi","year":"2016","journal-title":"Plant Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s00122-013-2066-0","article-title":"Next-Generation Phenotyping: Requirements and Strategies for Enhancing Our Understanding of Genotype\u2013Phenotype Relationships and Its Relevance to Crop Improvement","volume":"126","author":"Cobb","year":"2013","journal-title":"Appl. Genet."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.fcr.2012.04.003","article-title":"Field-Based Phenomics for Plant Genetics Research","volume":"133","author":"White","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"106310","DOI":"10.1016\/j.compag.2021.106310","article-title":"Automatic Stem-Leaf Segmentation of Maize Shoots Using Three-Dimensional Point Cloud","volume":"187","author":"Miao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Campos, J., Garc\u00eda-Ru\u00edz, F., and Gil, E. (2021). Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery. Sensors, 21.","DOI":"10.3390\/s21072363"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11119-019-09699-x","article-title":"Leaf Area Index Evaluation in Vineyards Using 3D Point Clouds from UAV Imagery","volume":"21","author":"Comba","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tao, H., Feng, H., Xu, L., Miao, M., Long, H., Yue, J., Li, Z., Yang, G., Yang, X., and Fan, L. (2020). Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data. Sensors, 20.","DOI":"10.3390\/s20051296"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A Review on UAV-Based Applications for Precision Agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_44","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":"2018","journal-title":"Precis. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5078","DOI":"10.1080\/01431161.2017.1420941","article-title":"A Meta-Analysis and Review of Unmanned Aircraft System (UAS) Imagery for Terrestrial Applications","volume":"39","author":"Singh","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.compag.2017.07.008","article-title":"Crop Height Monitoring with Digital Imagery from Unmanned Aerial System (UAS)","volume":"141","author":"Chang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019). Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sens., 11.","DOI":"10.3390\/rs11131548"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.compag.2018.06.051","article-title":"Unmanned Aerial System Assisted Framework for the Selection of High Yielding Cotton Genotypes","volume":"152","author":"Jung","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bendig, J., Bolten, A., and Bareth, G. (2013). Unmanned Aerial Vehicles (UAVs) for Multi-Temporal Crop Surface Modelling, Universit\u00e4t zu K\u00f6ln.","DOI":"10.1127\/1432-8364\/2013\/0200"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chang, A., Jung, J., Yeom, J., and Landivar, J. (2021). 3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13020282"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11119-017-9508-7","article-title":"Monitoring Cotton (Gossypium hirsutum L.) Germination Using Ultrahigh-Resolution UAS Images","volume":"19","author":"Chen","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1080\/2150704X.2018.1475771","article-title":"Characterizing Canopy Height with UAS Structure-from-Motion Photogrammetry\u2014Results Analysis of a Maize Field Trial with Respect to Multiple Factors","volume":"9","author":"Chu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"026035","DOI":"10.1117\/1.JRS.11.026035","article-title":"Unmanned Aircraft System-Derived Crop Height and Normalized Difference Vegetation Index Metrics for Sorghum Yield and Aphid Stress Assessment","volume":"11","author":"Stanton","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., Serrano, N., Arquero, O., and Pe\u00f1a, J.M. (2015). High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130479"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 Photogrammetry: A Low-Cost, Effective Tool for Geoscience Applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"78400E","DOI":"10.1117\/12.872315","article-title":"High-Resolution Crop Surface Models (CSM) and Crop Volume Models (CVM) on Field Level by Terrestrial Laser Scanning","volume":"7840","author":"Hoffmeister","year":"2010","journal-title":"Proc. SPIE"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sassu, A., Ghiani, L., Salvati, L., Mercenaro, L., Deidda, A., and Gambella, F. (2021). Integrating UAVs and Canopy Height Models in Vineyard Management: A Time-Space Approach. Remote Sens., 14.","DOI":"10.3390\/rs14010130"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2019.04.003","article-title":"A Novel Framework to Detect Conventional Tillage and No-Tillage Cropping System Effect on Cotton Growth and Development Using Multi-Temporal UAS Data","volume":"152","author":"Ashapure","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","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_61","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_62","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1117\/12.144030","article-title":"Plant species identification, size, and enumeration using machine vision techniques on near-binary images","volume":"1836","author":"Woebbecke","year":"1993","journal-title":"Opt. Agric. For."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1186\/s13007-022-00966-z","article-title":"Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography","volume":"18","author":"Wu","year":"2022","journal-title":"Plant Methods"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"9651","DOI":"10.3390\/s150509651","article-title":"Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level","volume":"15","author":"Rose","year":"2015","journal-title":"Sensors"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"669909","DOI":"10.3389\/fpls.2021.669909","article-title":"Modelling the tree-individual fruit bearing capacity aimed at optimising fruit quality of Malus domestica BORKH. \u2018Brookfield Gala\u2019","volume":"13","author":"Penzel","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.1016\/j.rse.2010.04.025","article-title":"Simultaneous measurements of plant structure and chlorophyll content in broadleaf saplings with a terrestrial laser scanner","volume":"114","author":"Eitel","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"107611","DOI":"10.1016\/j.compag.2022.107611","article-title":"In-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)","volume":"205","author":"Tsoulias","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Barbedo, J.G.A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 3.","DOI":"10.3390\/drones3020040"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ballester, C., Hornbuckle, J., Brinkhoff, J., Smith, J., and Quayle, W. (2017). Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery. Remote Sens., 9.","DOI":"10.3390\/rs9111149"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Mart\u00ednez, H., Flores-Magdaleno, H., Khalil-Gardezi, A., Ascencio-Hern\u00e1ndez, R., Tijerina-Ch\u00e1vez, L., V\u00e1zquez-Pe\u00f1a, M.A., and Mancilla-Villa, O.R. (2020). Digital count of corn plants using images taken by unmanned aerial vehicles and cross correlation of templates. Agronomy, 10.","DOI":"10.3390\/agronomy10040469"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:39:35Z","timestamp":1760121575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,22]]},"references-count":72,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051214"],"URL":"https:\/\/doi.org\/10.3390\/rs15051214","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,22]]}}}