{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T08:46:24Z","timestamp":1770453984662,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T00:00:00Z","timestamp":1534464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"13th five-year plan National key research program","award":["No.2017YFD0700400-2017YFD0700404"],"award-info":[{"award-number":["No.2017YFD0700400-2017YFD0700404"]}]},{"name":"the Science and Technology Planning Project of Guangdong Province","award":["No.2016B020205003"],"award-info":[{"award-number":["No.2016B020205003"]}]},{"name":"the Graduate Student Overseas Study Program of South China Agricultural University","award":["Grant NO.2017LHPY001"],"award-info":[{"award-number":["Grant NO.2017LHPY001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral sensors, especially the close-range hyperspectral camera, have been widely introduced to detect biological processes of plants in the high-throughput phenotyping platform, to support the identification of biotic and abiotic stress reactions at an early stage. However, the complex geometry of plants and their interaction with the illumination, severely affects the spectral information obtained. Furthermore, plant structure, leaf area, and leaf inclination distribution are critical indexes which have been widely used in multiple plant models. Therefore, the process of combination between hyperspectral images and 3D point clouds is a promising approach to solve these problems and improve the high-throughput phenotyping technique. We proposed a novel approach fusing a low-cost depth sensor and a close-range hyperspectral camera, which extended hyperspectral camera ability with 3D information as a potential tool for high-throughput phenotyping. An exemplary new calibration and analysis method was shown in soybean leaf experiments. The results showed that a 0.99 pixel resolution for the hyperspectral camera and a 3.3 millimeter accuracy for the depth sensor, could be achieved in a controlled environment using the method proposed in this paper. We also discussed the new capabilities gained using this new method, to quantify and model the effects of plant geometry and sensor configuration. The possibility of 3D reflectance models can be used to minimize the geometry-related effects in hyperspectral images, and to significantly improve high-throughput phenotyping. Overall results of this research, indicated that the proposed method provided more accurate spatial and spectral plant information, which helped to enhance the precision of biological processes in high-throughput phenotyping.<\/jats:p>","DOI":"10.3390\/s18082711","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T10:54:25Z","timestamp":1534503265000},"page":"2711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Improving High-Throughput Phenotyping Using Fusion of Close-Range Hyperspectral Camera and Low-Cost Depth Sensor"],"prefix":"10.3390","volume":"18","author":[{"given":"Peikui","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Xiwen","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Jian","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}]},{"given":"Liangju","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}]},{"given":"Libo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Zhigang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.compag.2016.08.021","article-title":"A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding","volume":"128","author":"Bai","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20078","DOI":"10.3390\/s141120078","article-title":"A review of imaging techniques for plant phenotyping","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A. (2012). Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands. Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/b11222-3"},{"key":"ref_4","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":"Gonzalezdugo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41348-017-0124-6","article-title":"Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective","volume":"125","author":"Thomas","year":"2018","journal-title":"J. Plant Dis. Prot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.scitotenv.2016.08.014","article-title":"Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance","volume":"578","author":"Sytar","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2012.08.005","article-title":"The spectral response of Buxus sempervirens to different types of environmental stress\u2014A laboratory experiment","volume":"74","author":"Addink","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10658-011-9878-z","article-title":"Recent advances in sensing plant diseases for precision crop protection","volume":"133","author":"Mahlein","year":"2012","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/bit.22931","article-title":"Understanding tissue specific compositions of bioenergy feedstocks through hyperspectral Raman imaging","volume":"108","author":"Sun","year":"2011","journal-title":"Biotechnol. Bioeng."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s00138-015-0716-8","article-title":"Generation and application of hyperspectral 3D plant models: methods and challenges","volume":"27","author":"Behmann","year":"2016","journal-title":"Mach. Vis. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT+ SAIL models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2016.02.029","article-title":"A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy","volume":"177","author":"Jay","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.cageo.2012.08.002","article-title":"Py6S: A Python interface to the 6S radiative transfer model","volume":"51","author":"Wilson","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.isprsjprs.2015.05.010","article-title":"Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping","volume":"106","author":"Behmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0924-2716(99)00037-4","article-title":"Geometric in-flight calibration of the stereoscopic line-CCD scanner MOMS-2P","volume":"55","author":"Kornus","year":"2000","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1111\/j.1477-9730.2011.00665.x","article-title":"Review of developments in geometric modelling for high resolution satellite pushbroom sensors","volume":"27","author":"Poli","year":"2012","journal-title":"Photogramm. Rec."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.1364\/AO.49.002813","article-title":"Geometric calibration of a hyperspectral imaging system","volume":"49","author":"Likar","year":"2010","journal-title":"Appl. Opt."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"513","DOI":"10.13031\/2013.12940","article-title":"Calibration of a pushbroom hyperspectral imaging system for agricultural inspection","volume":"46","author":"Lawrence","year":"2003","journal-title":"Trans. ASAE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/34.615446","article-title":"Linear pushbroom cameras","volume":"19","author":"Gupta","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11104-011-0797-8","article-title":"A tool to model 3D coarse-root development with annual resolution","volume":"346","author":"Wagner","year":"2011","journal-title":"Plant Soil"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2166","DOI":"10.3390\/s110202166","article-title":"3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information","volume":"11","author":"Hosoi","year":"2011","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1093\/jxb\/erl142","article-title":"3D lidar imaging for detecting and understanding plant responses and canopy structure","volume":"58","author":"Omasa","year":"2006","journal-title":"J. Exp. Bot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1111\/j.1365-3040.2007.01702.x","article-title":"A stereo imaging system for measuring structural parameters of plant canopies","volume":"30","author":"Biskup","year":"2007","journal-title":"Plant Cell Environ."},{"key":"ref_26","first-page":"b7","article-title":"Evaluation of terrestrial laser scanning for rice growth monitoring","volume":"39","author":"Tilly","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.3390\/s120101052","article-title":"Computer reconstruction of plant growth and chlorophyll fluorescence emission in three spatial dimensions","volume":"12","author":"Bellasio","year":"2012","journal-title":"Sensors"},{"key":"ref_28","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 analysing growth of cereal plants","volume":"121","author":"Paulus","year":"2014","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liang, J., Zia, A., Zhou, J., and Sirault, X. (2013, January 1\u20138). 3D plant modelling via hyperspectral imaging. Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.29"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bareth, G., Aasen, H., Bendig, J., Gnyp, M.L., Bolten, A., Jung, A., and Soukkam\u00e4ki, J. (2015). Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements. Photogramm. Fernerkun, 69\u201379.","DOI":"10.1127\/pfg\/2015\/0256"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"725","DOI":"10.3390\/rs70100725","article-title":"Angular dependency of hyperspectral measurements over wheat characterized by a novel UAV based goniometer","volume":"7","author":"Burkart","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"053602","DOI":"10.1117\/1.OE.51.5.053602","article-title":"Line-scan camera calibration in close-range photogrammetry","volume":"51","author":"Hui","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s11263-010-0349-3","article-title":"Plane-based calibration for linear cameras","volume":"91","author":"Roy","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"637","DOI":"10.2514\/1.60211","article-title":"Blazing gyros: The evolution of strapdown inertial navigation technology for aircraft","volume":"36","author":"Savage","year":"2013","journal-title":"J. Guid. Control Dyn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1023\/A:1007941100561","article-title":"Determining the epipolar geometry and its uncertainty: A review","volume":"27","author":"Zhang","year":"1998","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2015.05.006","article-title":"Kinect range sensing: Structured-light versus time-of-flight kinect","volume":"139","author":"Sarbolandi","year":"2015","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/0042-6989(90)90126-6","article-title":"Theory and measurement of ocular chromatic aberration","volume":"30","author":"Thibos","year":"1990","journal-title":"Vis. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Paulus, S., Dupuis, J., Mahlein, A.-K., and Kuhlmann, H. (2013). Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinf., 14.","DOI":"10.1186\/1471-2105-14-238"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.compag.2011.12.007","article-title":"On the use of depth camera for 3D phenotyping of entire plants","volume":"82","author":"Rousseau","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1093\/jxb\/erp345","article-title":"Functional\u2013structural plant modelling: A new versatile tool in crop science","volume":"61","author":"Vos","year":"2010","journal-title":"J. Exp. Bot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1093\/aob\/mcq264","article-title":"A functional\u2013structural model of rice linking quantitative genetic information with morphological development and physiological processes","volume":"107","author":"Xu","year":"2011","journal-title":"Ann. Bot."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s13007-015-0073-7","article-title":"Hyperspectral phenotyping on the microscopic scale: Towards automated characterization of plant\u2013pathogen interactions","volume":"11","author":"Kuska","year":"2015","journal-title":"Plant Methods"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2711\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:19:22Z","timestamp":1760195962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,17]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["s18082711"],"URL":"https:\/\/doi.org\/10.3390\/s18082711","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,17]]}}}