{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:10:39Z","timestamp":1773821439199,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42101446"],"award-info":[{"award-number":["42101446"]}]},{"name":"National Natural Science Foundation of China","award":["2022T150488"],"award-info":[{"award-number":["2022T150488"]}]},{"name":"National Natural Science Foundation of China","award":["2023RC009"],"award-info":[{"award-number":["2023RC009"]}]},{"name":"National Natural Science Foundation of China","award":["GDMY202308"],"award-info":[{"award-number":["GDMY202308"]}]},{"name":"China Postdoctoral Science Foundation","award":["42101446"],"award-info":[{"award-number":["42101446"]}]},{"name":"China Postdoctoral Science Foundation","award":["2022T150488"],"award-info":[{"award-number":["2022T150488"]}]},{"name":"China Postdoctoral Science Foundation","award":["2023RC009"],"award-info":[{"award-number":["2023RC009"]}]},{"name":"China Postdoctoral Science Foundation","award":["GDMY202308"],"award-info":[{"award-number":["GDMY202308"]}]},{"name":"Special Scientific Research Fund for Doctoral Programs in Colleges and Universities","award":["42101446"],"award-info":[{"award-number":["42101446"]}]},{"name":"Special Scientific Research Fund for Doctoral Programs in Colleges and Universities","award":["2022T150488"],"award-info":[{"award-number":["2022T150488"]}]},{"name":"Special Scientific Research Fund for Doctoral Programs in Colleges and Universities","award":["2023RC009"],"award-info":[{"award-number":["2023RC009"]}]},{"name":"Special Scientific Research Fund for Doctoral Programs in Colleges and Universities","award":["GDMY202308"],"award-info":[{"award-number":["GDMY202308"]}]},{"name":"The Key Laboratory of China-ASEAN Satellite Remote Sensing Applications of the Ministry of Natural Resources","award":["42101446"],"award-info":[{"award-number":["42101446"]}]},{"name":"The Key Laboratory of China-ASEAN Satellite Remote Sensing Applications of the Ministry of Natural Resources","award":["2022T150488"],"award-info":[{"award-number":["2022T150488"]}]},{"name":"The Key Laboratory of China-ASEAN Satellite Remote Sensing Applications of the Ministry of Natural Resources","award":["2023RC009"],"award-info":[{"award-number":["2023RC009"]}]},{"name":"The Key Laboratory of China-ASEAN Satellite Remote Sensing Applications of the Ministry of Natural Resources","award":["GDMY202308"],"award-info":[{"award-number":["GDMY202308"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Seed geometric parameters are important in yielding trait scorers, quantitative trait loci, and species recognition and classification. A novel method for automatic measurement of three-dimensional seed phenotypes is proposed. First, a handheld three-dimensional (3D) laser scanner is employed to obtain the seed point cloud data in batches. Second, a novel point cloud-based phenotyping method is proposed to obtain a single-seed 3D model and extract 33 phenotypes. It is connected by an automatic pipeline, including single-seed segmentation, pose normalization, point cloud completion by an ellipse fitting method, Poisson surface reconstruction, and automatic trait estimation. Finally, two statistical models (one using 11 size-related phenotypes and the other using 22 shape-related phenotypes) based on the principal component analysis method are built. A total of 3400 samples of eight kinds of seeds with different geometrical shapes are tested. Experiments show: (1) a single-seed 3D model can be automatically obtained with 0.017 mm point cloud completion error; (2) 33 phenotypes can be automatically extracted with high correlation compared with manual measurements (correlation coefficient (R2) above 0.9981 for size-related phenotypes and R2 above 0.8421 for shape-related phenotypes); and (3) two statistical models are successfully built to achieve seed shape description and quantification.<\/jats:p>","DOI":"10.3390\/s24186117","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Measurement of Seed Geometric Parameters Using a Handheld Scanner"],"prefix":"10.3390","volume":"24","author":[{"given":"Xia","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Chengdu Technological University, Chengdu 611730, China"},{"name":"Special Robot Application Technology Research Institute, Chengdu 611730, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2701-1458","authenticated-orcid":false,"given":"Fengbo","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiqi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Chengdu Technological University, Chengdu 611730, China"},{"name":"Special Robot Application Technology Research Institute, Chengdu 611730, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1038\/s41598-021-83581-7","article-title":"Genome-wide association study identified candidate genes for seed size and seed composition improvement in M. truncatula","volume":"11","author":"Chen","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hasan, S., Furtado, A., and Henry, R. (2023). Analysis of domestication loci in wild rice populations. Plants, 12.","DOI":"10.3390\/plants12030489"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5254823","DOI":"10.1155\/2022\/5254823","article-title":"Research on painting image classification based on transfer learning and feature fusion","volume":"2022","author":"Yong","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106269","DOI":"10.1016\/j.compag.2021.106269","article-title":"A novel deep learning based approach for seed image classification and retrieval","volume":"187","author":"Loddo","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","first-page":"55","article-title":"Association of various morphological traits with yield and genetic divergence in rice (Oryza sativa)","volume":"14","author":"Ashfaq","year":"2012","journal-title":"Int. J. Agric. Biol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sanwong, P., Sanitchon, J., Dongsansuk, A., and Jothityangkoon, D. (2023). High temperature alters phenology, seed development and yield in three rice varieties. Plants, 12.","DOI":"10.3390\/plants12030666"},{"key":"ref_7","first-page":"1511","article-title":"Determining morphological traits for selecting wheat (Triticum aestivum L.) with improved early-season forage production","volume":"9","author":"Malinowski","year":"2018","journal-title":"J. Adv. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"601475","DOI":"10.3389\/fpls.2020.601475","article-title":"High-throughput phenotyping of morphological seed and fruit characteristics using X-ray computed tomography","volume":"11","author":"Liu","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.measurement.2016.05.015","article-title":"A high-throughput maize kernel traits scorer based on line-scan imaging","volume":"90","author":"Liang","year":"2016","journal-title":"Measurement"},{"key":"ref_10","first-page":"122","article-title":"Rice seeds identification based on back propagation neural network model","volume":"12","author":"Feng","year":"2019","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20953","DOI":"10.1038\/s41598-021-00081-4","article-title":"Image processing techniques to estimate weight and morphological parameters for selected wheat refractions","volume":"11","author":"Sharma","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s11032-019-1082-4","article-title":"Major seed size QTL on chromosome A05 of peanut (Arachis hypogaea) is conserved in the US mini core germplasm collection","volume":"40","author":"Chu","year":"2020","journal-title":"Mol. Breed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e175","DOI":"10.1002\/leg3.175","article-title":"A review of the opportunities for spectral-based technologies in post-harvest testing of pulse grains","volume":"5","author":"McDonald","year":"2023","journal-title":"Legume Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/taxonomy2010001","article-title":"New techniques for seed shape description in silene species","volume":"2","author":"Juan","year":"2022","journal-title":"Taxonomy"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cervantes, E., and Mart\u00edn G\u00f3mez, J.J. (2019). Seed shape description and quantification by comparison with geometric models. Horticulturae, 5.","DOI":"10.3390\/horticulturae5030060"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1038\/s41598-020-79115-2","article-title":"ScreenSeed as a novel high throughput seed germination phenotyping method","volume":"11","author":"Merieux","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Song, B., Guo, Y., Wang, B., Xu, C., Zhu, H., Lizhu, E., Lai, J., Song, W., and Zhao, H. (2023). QTL analysis reveals conserved and differential genetic regulation of maize lateral angles above the ear. Plants, 12.","DOI":"10.3390\/plants12030680"},{"key":"ref_18","first-page":"1","article-title":"Seed shape quantification in the order Cucurbitales","volume":"12","author":"Cervantes","year":"2018","journal-title":"Mod. Phytomorphol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1017\/S0960258516000234","article-title":"Assessment of seed quality using non-destructive measurement techniques: A review","volume":"26","author":"Rahman","year":"2016","journal-title":"Seed Sci. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5721","DOI":"10.1080\/01431161.2021.1930271","article-title":"A method for calculating the leaf inclination of soybean canopy based on 3D point clouds","volume":"42","author":"Zhang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.compag.2017.09.009","article-title":"Calculation method of surface shape feature of rice seed based on point cloud","volume":"142","author":"Li","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"181","DOI":"10.13031\/aea.32.11187","article-title":"3D scanner-based corn seed modeling","volume":"32","author":"Yan","year":"2016","journal-title":"Appl. Eng. Agric."},{"key":"ref_23","unstructured":"Serna-Saldivar, S.O. (2019). Development and structure of the Corn Kernel. Corn, AACC International Press. [3rd ed.]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/cgf.14077","article-title":"Poisson surface reconstruction with envelope constraints","volume":"39","author":"Kazhdan","year":"2020","journal-title":"Proc. Comput. Graph. Forum"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, W., and Xiao, C. (2019, January 15\u201320). PCAN: 3D attention map learning using contextual information for point cloud based retrieval. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01272"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, X., Cai, Z., Pan, L., Zhao, H., Yi, S., Yeo, C.K., Dai, B., and Loy, C.C. (2021, January 19\u201325). Unsupervised 3D shape completion through GAN inversion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00181"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1016\/j.jplph.2012.05.019","article-title":"Seed shape in model legumes: Approximation by a cardioid reveals differences in ethylene insensitive mutants of Lotus japonicus and Medicago truncatula","volume":"169","author":"Cervantes","year":"2012","journal-title":"J. Plant Physiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s11032-021-01223-2","article-title":"Exploring genetic architecture for pod-related traits in soybean using image-based phenotyping","volume":"41","author":"Chang","year":"2021","journal-title":"Mol. Breed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"155805","DOI":"10.1109\/ACCESS.2021.3129097","article-title":"A statistical model of spine shape and material for population-oriented biomechanical simulation","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-G\u00f3mez, J.J., Rewicz, A., Rodr\u00edguez-Lorenzo, J.L., Janou\u0161ek, B., and Cervantes, E. (2020). Seed morphology in silene based on geometric models. Plants, 9.","DOI":"10.3390\/plants9121787"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13007-019-0476-y","article-title":"Hyperspectral imaging for seed quality and safety inspection: A review","volume":"15","author":"Feng","year":"2019","journal-title":"Plant Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/TMI.2002.804426","article-title":"Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling","volume":"21","author":"Frangi","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, X., Zheng, S., and Zhu, N. (2022). High-throughput legume seed phenotyping using a handheld 3D laser scanner. Remote Sens., 14.","DOI":"10.3390\/rs14020431"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/j.1467-8659.2007.01016.x","article-title":"Efficient RANSAC for point-cloud shape detection","volume":"26","author":"Schnabel","year":"2007","journal-title":"Proc. Comput. Graph. Forum"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, D., Yan, C., Tang, X.S., Yan, S., and Xin, C. (2018). Leaf segmentation on dense plant point clouds with facet region growing. Sensors, 18.","DOI":"10.3390\/s18113625"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104642","DOI":"10.1016\/j.autcon.2022.104642","article-title":"Automated extraction of geometric primitives with solid lines from unstructured point clouds for creating digital buildings models","volume":"145","author":"Kim","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5691825","DOI":"10.1155\/2016\/5691825","article-title":"Updated methods for seed shape analysis","volume":"2016","author":"Cervantes","year":"2016","journal-title":"Scientifica"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1109\/34.765658","article-title":"Direct least square fitting of ellipses","volume":"21","author":"Fitzgibbon","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.jneumeth.2014.07.012","article-title":"Application of 3-D imaging sensor for tracking minipigs in the open field test","volume":"235","author":"Kulikov","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2487228.2487237","article-title":"Screened poisson surface reconstruction","volume":"32","author":"Kazhdan","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_41","first-page":"3414926","article-title":"Nondestructive 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography","volume":"3","author":"Hu","year":"2020","journal-title":"Plant Phenomics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1016\/j.jfoodeng.2006.02.039","article-title":"Physical properties of pea (Pisum sativum) seed","volume":"79","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tateyama, T., Foruzan, H., and Chen, Y. (2009, January 12\u201314). 2D-PCA based statistical shape model from few medical samples. Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, Japan.","DOI":"10.1109\/IIH-MSP.2009.246"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:59Z","timestamp":1760112119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186117"],"URL":"https:\/\/doi.org\/10.3390\/s24186117","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,22]]}}}