{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:25:11Z","timestamp":1773267911523,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T00:00:00Z","timestamp":1552003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31601220"],"award-info":[{"award-number":["31601220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["QC2016031"],"award-info":[{"award-number":["QC2016031"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016M601464"],"award-info":[{"award-number":["2016M601464"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Support Program for Natural Science Talent of Heilongjiang Bayi Agricultural University","award":["ZRCQC201806"],"award-info":[{"award-number":["ZRCQC201806"]}]},{"name":"Heilongjiang Bayi Agricultural University Innovative Research Team Foundation","award":["TDJH201807"],"award-info":[{"award-number":["TDJH201807"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R2 were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.<\/jats:p>","DOI":"10.3390\/s19051201","type":"journal-article","created":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T03:49:31Z","timestamp":1552362571000},"page":"1201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies"],"prefix":"10.3390","volume":"19","author":[{"given":"Xiaodan","family":"Ma","sequence":"first","affiliation":[{"name":"College of electrical and information, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of electrical and information, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiou","family":"Guan","sequence":"additional","affiliation":[{"name":"College of electrical and information, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiarui","family":"Feng","sequence":"additional","affiliation":[{"name":"College of electrical and information, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Yu","sequence":"additional","affiliation":[{"name":"Agronomy College of Heilongjiang Bayi Agricultural University, Daqing 163319, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s10681-019-2370-0","article-title":"Progress for testcross performance within the flint heterotic pool of a public maize breeding program since the onset of hybrid breeding","volume":"215","author":"Schipprack","year":"2019","journal-title":"Euphytica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1556\/0806.46.2018.066","article-title":"Genetic diversity among tropical provitamin a maize inbred lines and implications for a biofortification program","volume":"47","author":"Sserumaga","year":"2019","journal-title":"Cereal Res. Commun."},{"key":"ref_3","first-page":"93","article-title":"Improving cultivation of lentil International Center for Agricultural Research in the Dry Areas (ICARDA), India","volume":"Volume 2","author":"Sarker","year":"2018","journal-title":"Achieving Sustainable Cultivation of Grain Legumes"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13007-019-0396-x","article-title":"Evaluating maize phenotype dynamics under drought stress using terrestrial lidar","volume":"15","author":"Su","year":"2019","journal-title":"Plant Methods"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.scitotenv.2018.11.126","article-title":"Optimizing genotype-environment-management interactions to enhance productivity and eco-efficiency for wheat-maize rotation in the North China Plain","volume":"654","author":"Xin","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1111\/1749-4877.12319","article-title":"Phenotypic trait matching predicts the topology of an insular plant\u2013bird pollination network","volume":"13","author":"Biddick","year":"2018","journal-title":"Integr. Zool."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1111\/jeb.13392","article-title":"Herbivores and plant defences affect selection on plant reproductive traits more strongly than pollinators","volume":"32","author":"Santangelo","year":"2019","journal-title":"J. Evol. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1111\/tpj.14179","article-title":"Computational aspects underlying genome to phenome analysis in plants","volume":"97","author":"Bolger","year":"2019","journal-title":"Plant J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Guan, H., Liu, M., Ma, X., and Yu, S. (2018). Three-dimensional reconstruction of soybean canopies using multisource imaging for phenotyping analysis. Remote Sens., 10.","DOI":"10.3390\/rs10081206"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3389\/fpls.2018.00611","article-title":"Multi-locus genome-wide association study reveals the genetic architecture of salk lodging resistance-related traits in maize","volume":"9","author":"Zhang","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.biosystemseng.2018.11.005","article-title":"Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging","volume":"178","author":"Bao","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S2095-3119(18)61905-7","article-title":"Shade stress decreases stem strength of soybean through restraining lignin biosynthesis","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.agwat.2018.09.014","article-title":"Estimating the upper and lower limits of kernel weight under different water regimes in hybrid maize seed production","volume":"213","author":"Wang","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_14","first-page":"256","article-title":"Three-dimensional quantifications of plant growth dynamics in field-grown plants based on machine vision method","volume":"49","author":"Zhu","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sun, S., Li, C., and Paterson, A.H. (2017). In-field high-throughput phenotyping of cotton plant height using LiDAR. Remote Sens., 9.","DOI":"10.3389\/fpls.2018.00016"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.chieco.2018.08.006","article-title":"Agricultural production and food consumption in China: A long-term projection","volume":"53","author":"Sheng","year":"2019","journal-title":"China Econ. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s10681-017-2090-2","article-title":"Genomic prediction and gwas of gibberella ear rot resistance traits in dent and flint lines of a public maize breeding program","volume":"214","author":"Han","year":"2018","journal-title":"Euphytica"},{"key":"ref_18","first-page":"281","article-title":"Overview and current status of remote sensing applications based on Unmanned aerial vehicles (UAVs) photogramm","volume":"81","author":"Pajares","year":"2015","journal-title":"Eng. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5429","DOI":"10.1093\/jxb\/erv345","article-title":"Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap","volume":"66","author":"Grosskinsky","year":"2015","journal-title":"J. Exp. Bot."},{"key":"ref_20","first-page":"406","article-title":"Imaging technologies for plant high-throughput phenotyping: A review","volume":"5","author":"Zhang","year":"2018","journal-title":"Front. Agric. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dreccer, M.F., Molero, G., Rivera-Amado, C., John-Bejai, C., and Wilson, Z. (2018). Yielding to the image: How phenotyping reproductive growth can assist crop improvement and production. Plant Sci.","DOI":"10.1016\/j.plantsci.2018.06.008"},{"key":"ref_22","first-page":"1","article-title":"Sensors for measuring plant phenotyping: A review","volume":"11","author":"Qiu","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.agrformet.2010.01.003","article-title":"Three-dimensional digital model of a maize plant","volume":"150","author":"Frasson","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","first-page":"98","article-title":"Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform","volume":"32","author":"Liu","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng. (Trans. Csae)"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Schmidt, D., and Kahlen, K. (2018). Towards More Realistic Leaf Shapes in functional-structural plant models. Symmetry, 10.","DOI":"10.3390\/sym10070278"},{"key":"ref_26","first-page":"96","article-title":"3D Reconstruction of maize leaves based on virtual visual technology","volume":"32","author":"Li","year":"2016","journal-title":"Bull. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-015-0047-9","article-title":"Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images","volume":"11","author":"Guo","year":"2015","journal-title":"Plant Methods"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Ma, X., Feng, J., Guan, H., and Liu, G. (2018). Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction. Remote Sens., 10.","DOI":"10.3390\/rs10030429"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.agrformet.2018.10.021","article-title":"New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species","volume":"264","author":"Vicari","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","first-page":"129","article-title":"Mapping invasive plant with UAV-derived 3D mesh model in mountain area-A case study in Shenzhen Coast, China","volume":"77","author":"Wu","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.compag.2015.09.001","article-title":"Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand","volume":"118","author":"Sankaran","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s11101-018-9585-x","article-title":"Engineering plants for tomorrow: How high-throughput phenotyping is contributing to the development of better crops","volume":"17","author":"Campbell","year":"2018","journal-title":"Phytochem. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, J., Fu, X., Schumacher, L., and Zhou, J. (2018). Evaluating Geometric Measurement Accuracy Based on 3D Reconstruction of Automated Imagery in a Greenhouse. Sensors, 18.","DOI":"10.3390\/s18072270"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s13007-018-0313-8","article-title":"Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform","volume":"14","author":"Thomas","year":"2018","journal-title":"Plant Methods"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.compag.2018.06.016","article-title":"Development of an automated phenotyping platform for quantifying soybean dynamic responses to salinity stress in greenhouse environment","volume":"151","author":"Zhou","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2014.09.021","article-title":"In-field crop row phenotyping from 3D modeling performed using Structure from Motion","volume":"110","author":"Jay","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mir, R.R., Reynolds, M., Pinto, F., Khan, M.A., and Bhat, M.A. (2019). High-throughput Phenotyping for Crop Improvement in The Genomics Era. Plant Sci.","DOI":"10.1016\/j.plantsci.2019.01.007"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13007-019-0398-8","article-title":"A spatio temporal spectral framework for plant stress phenotyping","volume":"15","author":"Khanna","year":"2019","journal-title":"Plant Methods"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.isprsjprs.2008.09.003","article-title":"Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging","volume":"64","author":"Hosoi","year":"2009","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"168","article-title":"Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor","volume":"34","author":"Xia","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng. (Trans. Csae)"},{"key":"ref_42","first-page":"1455","article-title":"Study on algorithms for local outlier detection","volume":"30","author":"Xue","year":"2007","journal-title":"Chin. J. Comput."},{"key":"ref_43","first-page":"65","article-title":"Automatically sphere target extracting and parameter fitting based on intensity image","volume":"12","author":"Zhang","year":"2014","journal-title":"Geotech. Investig. Surv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.3390\/s130302830","article-title":"BreedVision-A multi-sensor platform for non-destructive field-based phenotyping in plant breeding","volume":"13","author":"Busemeyer","year":"2013","journal-title":"Sensors"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"841","DOI":"10.3920\/9789086866649_101","article-title":"BoniRob: An autonomous field robot platform for individual plant phenotyping","volume":"9","author":"Ruckelshausen","year":"2009","journal-title":"Precis. Agric."},{"key":"ref_46","first-page":"93","article-title":"Usability study of 3D time-of flight cameras for automatic plant phenotyping","volume":"69","author":"Ralph","year":"2009","journal-title":"Bornimer Agrartech. Ber."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-14-238","article-title":"Surface feature-based classification of plant organs from 3D laser scanned point clouds for plant phenotyping","volume":"14","author":"Paulus","year":"2013","journal-title":"Bmc Bioinform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2014.01.010","article-title":"High-precision laser scanning system for capturing 3D plant architecture and analyzing growth of cereal plants","volume":"121","author":"Paulus","year":"2014","journal-title":"Biosyst. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12670","DOI":"10.3390\/s140712670","article-title":"Automated analysis of barley organs using 3D laser scanning: An approach for high throughput phenotyping","volume":"14","author":"Paulus","year":"2014","journal-title":"Sensors"},{"key":"ref_50","first-page":"83","article-title":"Cylindrical fitting method of laser scanner point cloud data","volume":"43","author":"Yan","year":"2018","journal-title":"Sci. Surv. Mapp."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agrformet.2018.11.014","article-title":"Finite element analysis of trees in the wind based on terrestrial laser scanning data","volume":"265","author":"Jackson","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_52","first-page":"396","article-title":"Cylinder-based Efficient and Robust Registration and Model Fitting of Laser-scanned Point Clouds for As-built Modeling of Piping Systems","volume":"16","author":"Moritani","year":"2019","journal-title":"Proc. Cad"},{"key":"ref_53","first-page":"175","article-title":"Apple tree canopy geometric parameters acquirement based on 3D point clouds","volume":"33","author":"Guo","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_54","unstructured":"Feng, J., Ma, X., Guan, H., Zhu, K., and Yu, S. (2019, March 01). Calculation method of soybean plant height based on depth information. Available online: http:\/\/kns.cnki.net\/kcms\/detail\/31.1252.O4. 20190225.0920.030.html."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.autcon.2018.02.019","article-title":"Reverse engineering techniques to optimize facility location of satellite ground stations on building roofs","volume":"90","author":"Farjas","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.3390\/s130202384","article-title":"Rapid characterization of vegetation structure with a Microsoft Kinect sensor","volume":"13","author":"Azzari","year":"2013","journal-title":"Sensors"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"20463","DOI":"10.3390\/s150820463","article-title":"In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation","volume":"15","author":"Xia","year":"2015","journal-title":"Sensors"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rueda-Ayala, V.P., Pe\u00f1a, J.M., H\u00f6glind, M., Bengochea-Guevara, J.M., and And\u00fajar, D. (2019). Comparing UAV-Based technologies and RGB-D reconstruction methods for plant height and biomass monitoring on grass ley. Sensors, 19.","DOI":"10.3390\/s19030535"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1109\/JSEN.2015.2416651","article-title":"Evaluating and improving the depth accuracy of Kinect for Windows v2","volume":"15","author":"Yang","year":"2015","journal-title":"Ieee Sens. J."},{"key":"ref_60","first-page":"82","article-title":"Multi-source image registration for canopy organ of apple trees in mature period","volume":"45","author":"Ma","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_61","first-page":"177","article-title":"Study on multi-image registration of apple tree at different growth stages","volume":"34","author":"Zhou","year":"2014","journal-title":"Acta Opt. Sin."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fukuda, T., Ji, Y., and Umeda, K. (2018, January 10\u201312). Accurate range image generation using sensor fusion of TOF and Stereo-based Measurement. Proceedings of the 12th France-Japan and 10th Europe-Asia Congress on Mechatronics, Tsu, Japan.","DOI":"10.1109\/MECATRONICS.2018.8495739"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hu, C., Pan, Z., and Li, P. (2019). A 3D point cloud filtering method for leaves based on manifold distance and normal estimation. Remote Sens., 11.","DOI":"10.3390\/rs11020198"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Itakura, K., and Hosoi, F. (2019). Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar. Remote Sens., 11.","DOI":"10.3390\/rs11030344"},{"key":"ref_65","first-page":"14","article-title":"Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data\u2014Potential of unmanned aerial vehicle imagery","volume":"66","author":"Roosjen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zheng, H., Cheng, T., Li, D., Zhou, X., Yao, X., Tian, Y., Cao, W., and Zhu, Y. (2018). Evaluation of RGB, color-Infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sens., 10.","DOI":"10.3390\/rs10060824"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agrformet.2013.09.010","article-title":"Photographic measurement of leaf angles ind fiel crops","volume":"184","author":"Zou","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_68","first-page":"83","article-title":"Individual modelling of leaf area in cress and radish using leaf dimensions and weight","volume":"2","author":"Aminifard","year":"2019","journal-title":"J. Hortic. Postharvest Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2018.12.032","article-title":"Assessment of red-edge vegetation indices for crop leaf area index estimation","volume":"222","author":"Dong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Larrinaga, A., and Brotons, L. (2019). Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery. Drones, 3.","DOI":"10.3390\/drones3010006"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.agrformet.2018.06.017","article-title":"LIDAR and non-LIDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard","volume":"260\u2013261","author":"Sanz","year":"2018","journal-title":"Agric. For. Meteorol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/5\/1201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:37:32Z","timestamp":1760186252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/5\/1201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,8]]},"references-count":71,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["s19051201"],"URL":"https:\/\/doi.org\/10.3390\/s19051201","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,8]]}}}