{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:24:21Z","timestamp":1780727061500,"version":"3.54.1"},"reference-count":79,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Washington State University\u2019s Center for Sustaining Agriculture and Natural Resources BioAg Program","award":["1014919"],"award-info":[{"award-number":["1014919"]}]},{"DOI":"10.13039\/100005825","name":"US Department of Agriculture\u2019s National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["1014919"],"award-info":[{"award-number":["1014919"]}],"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>Forage and field peas provide essential nutrients for livestock diets, and high-quality field peas can influence livestock health and reduce greenhouse gas emissions. Above-ground biomass (AGBM) is one of the vital traits and the primary component of yield in forage pea breeding programs. However, a standard method of AGBM measurement is a destructive and labor-intensive process. This study utilized an unmanned aerial vehicle (UAV) equipped with a true-color RGB and a five-band multispectral camera to estimate the AGBM of winter pea in three breeding trials (two seed yields and one cover crop). Three processing techniques\u2014vegetation index (VI), digital surface model (DSM), and 3D reconstruction model from point clouds\u2014were used to extract the digital traits (height and volume) associated with AGBM. The digital traits were compared with the ground reference data (measured plant height and harvested AGBM). The results showed that the canopy volume estimated from the 3D model (alpha shape, \u03b1 = 1.5) developed from UAV-based RGB imagery\u2019s point clouds provided consistent and high correlation with fresh AGBM (r = 0.78\u20130.81, p &lt; 0.001) and dry AGBM (r = 0.70\u20130.81, p &lt; 0.001), compared with other techniques across the three trials. The DSM-based approach (height at 95th percentile) had consistent and high correlation (r = 0.71\u20130.95, p &lt; 0.001) with canopy height estimation. Using the UAV imagery, the proposed approaches demonstrated the potential for estimating the crop AGBM across winter pea breeding trials.<\/jats:p>","DOI":"10.3390\/rs14102396","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T04:04:19Z","timestamp":1652760259000},"page":"2396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Optimization of UAV-Based Imaging and Image Processing Orthomosaic and Point Cloud Approaches for Estimating Biomass in a Forage Crop"],"prefix":"10.3390","volume":"14","author":[{"given":"Worasit","family":"Sangjan","sequence":"first","affiliation":[{"name":"Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca J.","family":"McGee","sequence":"additional","affiliation":[{"name":"United States Department of Agriculture-Agricultural Research Service, Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA 99164, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sindhuja","family":"Sankaran","sequence":"additional","affiliation":[{"name":"Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1046\/j.1365-2494.2001.00268.x","article-title":"The effect of harvest date and inoculation on the yield, fermentation characteristics and feeding value of forage pea and field bean silages","volume":"56","author":"Fraser","year":"2001","journal-title":"Grass Forage Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.2134\/agronj2006.0085","article-title":"Winter pea and lentil response to seeding date and micro-and macro-environments","volume":"98","author":"Chen","year":"2006","journal-title":"Agron. J."},{"key":"ref_3","unstructured":"Clark, A. (2008). Managing Cover Crops Profitably, Sustainable Agriculture Network. [3rd ed.]."},{"key":"ref_4","unstructured":"Nadathur, S.R., Wanasundara, J.P.D., and Scanlin, L. (2017). Pea: A Sustainable Vegetable Protein Crop. Sustainable Protein Sources, Elsevier Inc."},{"key":"ref_5","unstructured":"Steinfeld, H., Gerder, P., Wassenaar, T.D., Castel, V., Rosales, M., and de Haan, C. (2006). Livestock\u2019s Long Shadow: Environmental Issues and Options, Food and Agriculture Organization."},{"key":"ref_6","unstructured":"Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., Falcucci, A., and Tempio, G. (2013). Tackling Climate Change through Livestock: A Global Assessment of Emissions and Mitigation Opportunities, Food and Agriculture Organization."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.fcr.2018.11.001","article-title":"Farmer-participatory vs. conventional market-oriented breeding of inbred crops using phenotypic and genome-enabled approaches: A pea case study","volume":"232","author":"Annicchiarico","year":"2019","journal-title":"Field Crops Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Insua, J.R., Utsumi, S.A., and Basso, B. (2019). Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212773"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ligoski, B., Gon\u00e7alves, L.F., Claudio, F.L., Alves, E.M., Kr\u00fcger, A.M., Bizzuti, B.E., Lima, P.D.M.T., Abdalla, A.L., and Paim, T.D.P. (2020). Silage of intercropping corn, palisade grass, and pigeon pea increases protein content and reduces in vitro methane production. Agronomy, 10.","DOI":"10.3390\/agronomy10111784"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Quir\u00f3s Vargas, J.J., Zhang, C., Smitchger, J.A., McGee, R.J., and Sankaran, S. (2019). Phenotyping of plant biomass and performance traits using remote sensing techniques in pea (Pisum sativum, L.). Sensors, 19.","DOI":"10.3390\/s19092031"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","article-title":"Phenomics\u2013technologies to relieve the phenotyping bottleneck","volume":"16","author":"Furbank","year":"2011","journal-title":"Trends Plant Sci."},{"key":"ref_12","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":"Theor. Appl. Genet."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.copbio.2020.09.003","article-title":"The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems","volume":"70","author":"Jung","year":"2021","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.3389\/fbioe.2020.623705","article-title":"High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field","volume":"8","author":"Li","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ortiz, M.V., Sangjan, W., Selvaraj, M.G., McGee, R.J., and Sankaran, S. (2021). Effect of the solar zenith angles at different latitudes on estimated crop vegetation indices. Drones, 5.","DOI":"10.3390\/drones5030080"},{"key":"ref_17","unstructured":"Zhang, C., Serra, S., Quir\u00f3s Vargas, J., Sangjan, W., Musacchi, S., and Sankaran, S. (Inf. Process. Agric., 2021). Non-invasive sensing techniques to phenotype multiple apple tree architectures, Inf. Process. Agric., in press."},{"key":"ref_18","first-page":"139","article-title":"Development and validation of methodology for estimating potato canopy structure for field crop phenotyping and improved breeding","volume":"12","author":"Souter","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9840192","DOI":"10.34133\/2021\/9840192","article-title":"UAS-based plant phenotyping for research and breeding applications","volume":"2021","author":"Guo","year":"2021","journal-title":"Plant Phenom."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.13031\/trans.14419","article-title":"Phenotyping architecture traits of tree species using remote sensing techniques","volume":"64","author":"Sangjan","year":"2021","journal-title":"Trans. ASABE"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Tian, T., and Yin, J. (2021). A review of unmanned aerial vehicle low-altitude remote sensing (UAV-LARS) use in agricultural monitoring in China. Remote Sens., 13.","DOI":"10.3390\/rs13061221"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11449","DOI":"10.3390\/rs70911449","article-title":"Fusion of plant height and vegetation indices for the estimation of barley biomass","volume":"7","author":"Tilly","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wengert, M., Piepho, H.P., Astor, T., Gra\u00df, R., Wijesingha, J., and Wachendorf, M. (2021). Assessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensing. Remote Sens., 13.","DOI":"10.3390\/rs13142751"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compag.2018.05.034","article-title":"High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery","volume":"151","author":"Sankaran","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11119-018-9600-7","article-title":"Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery","volume":"20","author":"Zheng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106304","DOI":"10.1016\/j.compag.2021.106304","article-title":"Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model","volume":"187","author":"Wan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Roy Choudhury, M., Das, S., Christopher, J., Apan, A., Chapman, S., Menzies, N.W., and Dang, Y.P. (2021). Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniques. Remote Sens., 13.","DOI":"10.3390\/rs13173482"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J. (2016, January 27\u201330). Structure-from-Motion revisited. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Thompson, A.L., Thorp, K.R., Conley, M.M., Elshikha, D.M., French, A.N., 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_31","doi-asserted-by":"crossref","unstructured":"Niu, Y., Zhang, L., Zhang, H., Han, W., and Peng, X. (2019). Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery. Remote Sens., 11.","DOI":"10.3390\/rs11111261"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11119-020-09764-w","article-title":"An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: A tool for monitoring agronomic field experiments","volume":"22","author":"Gilliot","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Acorsi, M.G., das Dores Abati Miranda, F., Martello, M., Smaniotto, D.A., and Sartor, L.R. (2019). Estimating biomass of black oat using UAV-based RGB imaging. Agronomy, 9.","DOI":"10.3390\/agronomy9070344"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Peprah, C.O., Yamashita, M., Yamaguchi, T., Sekino, R., Takano, K., and Katsura, K. (2021). Spatio-temporal estimation of biomass growth in rice using canopy surface model from unmanned aerial vehicle images. Remote Sens., 13.","DOI":"10.3390\/rs13122388"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e20157","DOI":"10.1002\/tpg2.20157","article-title":"Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters","volume":"14","author":"Toda","year":"2021","journal-title":"Plant Genome"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., 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_38","doi-asserted-by":"crossref","unstructured":"Banerjee, B.P., Spangenberg, G., and Kant, S. (2020). Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sens., 12.","DOI":"10.3390\/rs12193164"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13007-020-00632-2","article-title":"Evaluation of novel precision viticulture tool for canopy biomass estimation and missing plant detection based on 2.5D and 3D approaches using RGB images acquired by UAV platform","volume":"16","author":"Matese","year":"2020","journal-title":"Plant Methods"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.compag.2014.10.011","article-title":"A multi-sensor approach for predicting biomass of extensively managed grassland","volume":"109","author":"Reddersen","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Rogers, S.R., Manning, I., and Livingstone, W. (2020). Comparing the spatial accuracy of digital surface models from four unoccupied aerial systems: Photogrammetry versus LiDAR. Remote Sens., 12.","DOI":"10.3390\/rs12172806"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1007\/s42853-021-00120-y","article-title":"Drone-based three-dimensional photogrammetry and concave hull by slices algorithm for apple tree volume mapping","volume":"46","author":"Dong","year":"2021","journal-title":"J. Biosyst. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kothawade, G.S., Chandel, A.K., Schrader, M.J., Rathnayake, A.P., and Khot, L.R. (2021, January 3\u20135). High throughput canopy characterization of a commercial apple orchard using aerial RGB imagery. Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento-Bolzano, Italy.","DOI":"10.1109\/MetroAgriFor52389.2021.9628564"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Qi, Y., Dong, X., Chen, P., Lee, K.H., Lan, Y., Lu, X., Jia, R., Deng, J., and Zhang, Y. (2021). Canopy volume extraction of Citrus reticulate Blanco cv. Shatangju trees using UAV image-based point cloud deep learning. Remote Sens., 13.","DOI":"10.3390\/rs13173437"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Fang, S., Peng, Y., Gong, Y., Zhu, R., Wu, X., Ma, Y., Duan, B., and Liu, J. (2019). UAV-based biomass estimation for rice-combining spectral, TIN-based structural and meteorological features. Remote Sens., 11.","DOI":"10.3390\/rs11070890"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"108407","DOI":"10.1016\/j.fcr.2021.108407","article-title":"Estimating early season growth and biomass of field pea for selection of divergent ideotypes using proximal sensing","volume":"277","author":"Tefera","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote sensing of chlorophyll concentration in higher plant leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_53","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: Experiments with autumn chestnut and maple leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_54","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","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_59","doi-asserted-by":"crossref","unstructured":"Duarte, L., Silva, P., and Teodoro, A.C. (2018). Development of a QGIS plugin to obtain parameters and elements of plantation trees and vineyards with aerial photographs. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030109"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, C., Luo, B., Hong, H., Su, X., Wang, Y., Liu, J., Wang, C., Zhang, J., and Wei, L. (2020). Object detection based on global-local saliency constraint in aerial images. Remote Sens., 12.","DOI":"10.3390\/rs12091435"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"114219","DOI":"10.1016\/j.eswa.2020.114219","article-title":"Visual saliency detection by integrating spatial position prior of object with background cues","volume":"168","author":"Jian","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Huyan, L., Bai, Y., Li, Y., Jiang, D., Zhang, Y., Zhou, Q., Wei, J., Liu, J., Zhang, Y., and Cui, T. (2021). A Lightweight Object Detection Framework for Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13040683"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.compag.2019.02.012","article-title":"Evaluation of mobile 3D light detection and ranging based canopy mapping system for tree fruit crops","volume":"158","author":"Chakraborty","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1002\/ece3.7216","article-title":"Understanding 3D structural complexity of individual Scots pine trees with different management history","volume":"11","author":"Saarinen","year":"2021","journal-title":"Ecol. Evol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yan, Z., Liu, R., Cheng, L., Zhou, X., Ruan, X., and Xiao, Y. (2019). A concave hull methodology for calculating the crown volume of individual trees based on vehicle-borne LiDAR data. Remote Sens., 11.","DOI":"10.3390\/rs11060623"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhang, F., Hassanzadeh, A., Kikkert, J., Pethybridge, S.J., and van Aardt, J. (2021). Comparison of UAS-based structure-from-motion and LiDAR for structural characterization of short broadacre crops. Remote Sens., 13.","DOI":"10.3390\/rs13193975"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Processes Landf."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Jensen, J.L., and Mathews, A.J. (2016). Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sens., 8.","DOI":"10.3390\/rs8010050"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Salach, A., Baku\u0142a, K., Pilarska, M., Ostrowski, W., G\u00f3rski, K., and Kurczy\u0144ski, Z. (2018). Accuracy assessment of point clouds from LiDAR and dense image matching acquired using the UAV platform for DTM creation. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7090342"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s11119-016-9433-1","article-title":"Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields","volume":"17","author":"Kanke","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Cheng, T., Song, R., Li, D., Zhou, K., Zheng, H., Yao, X., Tian, Y., Cao, W., and Zhu, Y. (2017). Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sens., 9.","DOI":"10.3390\/rs9040319"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.3390\/s150305609","article-title":"Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution","volume":"15","year":"2015","journal-title":"Sensors"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s11119-013-9335-4","article-title":"Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat","volume":"15","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"12793","DOI":"10.3390\/rs71012793","article-title":"Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by UAV to support site-specific crop management","volume":"7","year":"2015","journal-title":"Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/s41438-019-0123-9","article-title":"3D point cloud data to quantitatively characterize size and shape of shrub crops","volume":"6","author":"Jiang","year":"2019","journal-title":"Hortic. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ku\u017eelka, K., Slav\u00edk, M., and Surov\u00fd, P. (2020). Very high density point clouds from UAV laser scanning for automatic tree stem detection and direct diameter measurement. Remote Sens., 12.","DOI":"10.3390\/rs12081236"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Moreira, B.M., Goyanes, G., Pina, P., Vassilev, O., and Heleno, S. (2021). Assessment of the influence of survey design and processing choices on the accuracy of tree diameter at breast height (DBH) measurements using UAV-based photogrammetry. Drones, 5.","DOI":"10.3390\/drones5020043"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:11:35Z","timestamp":1760137895000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,17]]},"references-count":79,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102396"],"URL":"https:\/\/doi.org\/10.3390\/rs14102396","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,17]]}}}