{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:18:57Z","timestamp":1783178337818,"version":"3.54.6"},"reference-count":48,"publisher":"Fuji Technology Press Ltd.","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJAT","Int. J. Automation Technol."],"published-print":{"date-parts":[[2026,7,5]]},"abstract":"<jats:p>Practical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging, and systematic evidence comparing both accuracy and acquisition efficiency under outdoor conditions remains limited. This study presents a field-deployable evaluation framework and implements it in a 2-ha commercial Japanese pear orchard trained under a joint V-trellis system. Using a terrestrial laser scanner (TLS) as the reference, we evaluated two handheld LiDAR systems (a low-cost SLAM-based system and a high-performance system), structure from motion \/ multi-view stereo (SfM\/MVS) reconstructions from three camera platforms (a digital camera, an action camera, and a 360\u00b0 camera), and 3D Gaussian splatting (3DGS) constructed from action-camera video. Measurements were taken at two spatial scales to capture scale-dependent effects. In the span-scale survey (4 m), location error was derived from TLS-referenced target coordinate differences, and reconstruction error was quantified using cloud-to-mesh distances with cubic targets. In the row-scale survey (one tree row), positional stability during continuous mapping was evaluated as location error. Operational metrics (acquisition time, data volume, and processing effort) were also documented. The results demonstrate clear trade-offs among the methods: LiDAR enables rapid wide-area acquisition but is susceptible to cumulative drift in row-structured environments, whereas SfM\/MVS provides superior geometric fidelity at the cost of increased time and data volume. Although 3DGS is less suitable for precise quantitative measurement, it demonstrates strong potential for intuitive visualization of orchard structure and fruit distribution. These findings highlight the need for staged, purpose-specific, and seasonally adaptive strategies for orchard-scale digital twin development.<\/jats:p>","DOI":"10.20965\/ijat.2026.p0308","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:02:06Z","timestamp":1783177326000},"page":"308-319","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of 3D Point Cloud Acquisition Accuracy in a Large-Scale Japanese Pear Tree Orchard"],"prefix":"10.20965","volume":"20","author":[{"given":"Kohei","family":"Shibata","sequence":"first","affiliation":[{"name":"The United Graduate School of Agricultural Science, Ehime University, 3-5-7 Tarumi, Matsuyama, Ehime 790-8566, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuo","family":"Kochi","sequence":"additional","affiliation":[{"name":"National Agriculture and Food Research Organization, Tokyo, Japan"},{"name":"R&D Initiative, Chuo University, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazutoshi","family":"Hamada","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture and Marine Science, Kochi University, Nankoku, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"8550","published-online":{"date-parts":[[2026,7,5]]},"reference":[{"key":"key-10.20965\/ijat.2026.p0308-1","doi-asserted-by":"crossref","unstructured":"S. A. Bound, \u201cCrop load management in nashi pear\u2014A review,\u201d Horticulturae, Vol.8, No.10, Article No.923, 2022. https:\/\/doi.org\/10.3390\/horticulturae8100923","DOI":"10.3390\/horticulturae8100923"},{"key":"key-10.20965\/ijat.2026.p0308-2","doi-asserted-by":"crossref","unstructured":"J. E. Kim et al., \u201cA pruning criterion for dormant \u2018Niitaka\u2019 pear trees that uses a decision tree model based on the basal diameter of bearing branches,\u201d Horticultural Science and Technology, Vol.43, No.6, pp. 673-684, 2025. https:\/\/doi.org\/10.7235\/HORT.20250059","DOI":"10.7235\/HORT.20250059"},{"key":"key-10.20965\/ijat.2026.p0308-3","doi-asserted-by":"crossref","unstructured":"Y. Huang, Z. Ren, D. Li, and X. Liu, \u201cPhenotypic techniques and applications in fruit trees: A review,\u201d Plant Methods, Vol.16, Article No.107, 2020. https:\/\/doi.org\/10.1186\/s13007-020-00649-7","DOI":"10.1186\/s13007-020-00649-7"},{"key":"key-10.20965\/ijat.2026.p0308-4","unstructured":"T. L. Robinson, S. A. Hoying, and G. H. Reginato, \u201cThe tall spindle apple production system,\u201d New York Fruit Quarterly, Vol.14, No.2, pp. 21-28, 2006."},{"key":"key-10.20965\/ijat.2026.p0308-5","doi-asserted-by":"crossref","unstructured":"K. Shibata, K. Koizumi, T. Seki, I. Kitao, and K. Matsushita, \u201cA \u2018joint tree\u2019 training system enables early returns on Japanese pear orchards,\u201d Acta Horticulturae, Vol.800, pp. 769-776, 2008. https:\/\/doi.org\/10.17660\/ActaHortic.2008.800.105","DOI":"10.17660\/ActaHortic.2008.800.105"},{"key":"key-10.20965\/ijat.2026.p0308-6","doi-asserted-by":"crossref","unstructured":"T. Seki, K. Hirose, and K. Shibata, \u201cYield and fruit quality of Japanese pear in \u2018joint V-shaped trellis\u2019,\u201d Acta Horticulturae, Vol.1303, pp. 171-176, 2021. https:\/\/doi.org\/10.17660\/ActaHortic.2021.1303.25","DOI":"10.17660\/ActaHortic.2021.1303.25"},{"key":"key-10.20965\/ijat.2026.p0308-7","doi-asserted-by":"crossref","unstructured":"T. Seki, K. Katayama, T. Soneda, and K. Shibata, \u201cProductivity and labor saving effect of Japanese pear in \u2018joint V-shaped trellis\u2019,\u201d Acta Horticulturae, Vol.1404, pp. 47-54, 2024. https:\/\/doi.org\/10.17660\/ActaHortic.2024.1404.7","DOI":"10.17660\/ActaHortic.2024.1404.7"},{"key":"key-10.20965\/ijat.2026.p0308-8","unstructured":"Japan Fruit Association, \u201cReport on the dissemination and adoption of labor-saving training systems in 2023,\u201d 2024. https:\/\/www.japanfruit.jp\/Portals\/0\/resources\/JFF\/kokunai\/r05chosa_siryo\/R5Shouryoku.pdf [Accessed March 9, 2026]"},{"key":"key-10.20965\/ijat.2026.p0308-9","doi-asserted-by":"crossref","unstructured":"N. Yan et al., \u201cEstimation of pear tree leaf area index using fused UAV multispectral and RGB imagery,\u201d Smart Agricultural Technology, Vol.13, Article No.101717, 2026. https:\/\/doi.org\/10.1016\/j.atech.2025.101717","DOI":"10.1016\/j.atech.2025.101717"},{"key":"key-10.20965\/ijat.2026.p0308-10","doi-asserted-by":"crossref","unstructured":"A. I. B. Parico and T. Ahamed, \u201cReal time pear fruit detection and counting using YOLOv4 models and Deep SORT,\u201d Sensors, Vol.21, No.14, Article No.4803, 2021. https:\/\/doi.org\/10.3390\/s21144803","DOI":"10.3390\/s21144803"},{"key":"key-10.20965\/ijat.2026.p0308-11","doi-asserted-by":"crossref","unstructured":"K. Itakura, Y. Narita, S. Noaki, and F. Hosoi, \u201cAutomatic pear and apple detection by videos using deep learning and a Kalman filter,\u201d OSA Continuum, Vol.4, No.5, pp. 1688-1695, 2021. https:\/\/doi.org\/10.1364\/OSAC.424583","DOI":"10.1364\/OSAC.424583"},{"key":"key-10.20965\/ijat.2026.p0308-12","doi-asserted-by":"crossref","unstructured":"B. Lavaquiol, R. Sanz, J. Llorens, J. Arn\u00f3, and A. Escol\u00e0, \u201cA photogrammetry-based methodology to obtain accurate digital ground-truth of leafless fruit trees,\u201d Computers and Electronics in Agriculture, Vol.191, Article No.106553, 2021. https:\/\/doi.org\/10.1016\/j.compag.2021.106553","DOI":"10.1016\/j.compag.2021.106553"},{"key":"key-10.20965\/ijat.2026.p0308-13","doi-asserted-by":"crossref","unstructured":"H. F. Murcia, S. Tilaguy, and S. Ouazaa, \u201cDevelopment of a low-cost system for 3D orchard mapping integrating UGV and LiDAR,\u201d Plants, Vol.10, No.12, Article No.2804, 2021. https:\/\/doi.org\/10.3390\/plants10122804","DOI":"10.3390\/plants10122804"},{"key":"key-10.20965\/ijat.2026.p0308-14","doi-asserted-by":"crossref","unstructured":"A. Hayashi, N. Kochi, K. Kodama, S. Isobe, and T. Tanabata, \u201cCLCFM3: A 3D reconstruction algorithm based on photogrammetry for high-precision whole plant sensing using all-around images,\u201d Sensors, Vol.25, No.18, Article No.5829, 2025. https:\/\/doi.org\/10.3390\/s25185829","DOI":"10.3390\/s25185829"},{"key":"key-10.20965\/ijat.2026.p0308-15","doi-asserted-by":"crossref","unstructured":"J. Gen\u00e9-Mola, R. Sanz-Cortiella, J. R. Rosell-Polo, A. Escol\u00e0, and E. Gregorio, \u201cIn-field apple size estimation using photogrammetry-derived 3D point clouds: Comparison of 4 different methods considering fruit occlusions,\u201d Computers and Electronics in Agriculture, Vol.188, Article No.106343, 2021. https:\/\/doi.org\/10.1016\/j.compag.2021.106343","DOI":"10.1016\/j.compag.2021.106343"},{"key":"key-10.20965\/ijat.2026.p0308-16","doi-asserted-by":"crossref","unstructured":"Q. Bing, R. Zhang, L. Zhang, L. Li, and L. Chen, \u201cUAV-SfM photogrammetry for canopy characterization toward unmanned aerial spraying systems precision pesticide application in an orchard,\u201d Drones, Vol.9, No.2, Article No.151, 2025. https:\/\/doi.org\/10.3390\/drones9020151","DOI":"10.3390\/drones9020151"},{"key":"key-10.20965\/ijat.2026.p0308-17","doi-asserted-by":"crossref","unstructured":"N. Kochi, S. Isobe, A. Hayashi, K. Kodama, and T. Tanabata, \u201cIntroduction of all-around 3D modeling methods for investigation of plants,\u201d Int. J. 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Su et al., \u201cDesign of an autonomous orchard navigation system based on multi-sensor fusion,\u201d Agronomy, Vol.14, No.12, Article No.2825, 2024. https:\/\/doi.org\/10.3390\/agronomy14122825","DOI":"10.3390\/agronomy14122825"},{"key":"key-10.20965\/ijat.2026.p0308-23","doi-asserted-by":"crossref","unstructured":"D. Tiozzo Fasiolo, L. Scalera, E. Maset, and A. Gasparetto, \u201cTowards autonomous mapping in agriculture: A review of supportive technologies for ground robotics,\u201d Robotics and Autonomous Systems, Vol.169, Article No.104514, 2023. https:\/\/doi.org\/10.1016\/j.robot.2023.104514","DOI":"10.1016\/j.robot.2023.104514"},{"key":"key-10.20965\/ijat.2026.p0308-24","doi-asserted-by":"crossref","unstructured":"S. Rusinkiewicz and M. Levoy, \u201cEfficient variants of the ICP algorithm,\u201d Proc. 3rd Int. Conf. on 3-D Digital Imaging and Modeling, pp. 145-152, 2001. https:\/\/doi.org\/10.1109\/IM.2001.924423","DOI":"10.1109\/IM.2001.924423"},{"key":"key-10.20965\/ijat.2026.p0308-25","doi-asserted-by":"crossref","unstructured":"P. Cignoni, C. Rocchini, and R. Scopigno, \u201c\n                      Metro\n                      : Measuring error on simplified surfaces,\u201d Computer Graphics Forum, Vol.17, No.2, pp. 167-174, 1998. https:\/\/doi.org\/10.1111\/1467-8659.00236","DOI":"10.1111\/1467-8659.00236"},{"key":"key-10.20965\/ijat.2026.p0308-26","doi-asserted-by":"crossref","unstructured":"N. Aspert, D. Santa-Cruz, and T. Ebrahimi, \u201cMESH: Measuring errors between surfaces using the Hausdorff distance,\u201d Proc. of 2002 IEEE Int. Conf. on Multimedia and Expo, Vol.1, pp. 705-708, 2002. https:\/\/doi.org\/10.1109\/ICME.2002.1035879","DOI":"10.1109\/ICME.2002.1035879"},{"key":"key-10.20965\/ijat.2026.p0308-27","doi-asserted-by":"crossref","unstructured":"M. Ingman, J.-P. Virtanen, M. T. Vaaja, and H. Hyypp\u00e4, \u201cA comparison of low-cost sensor systems in automatic cloud-based indoor 3D modeling,\u201d Remote Sensing, Vol.12, No.16, Article No.2624, 2020. https:\/\/doi.org\/10.3390\/rs12162624","DOI":"10.3390\/rs12162624"},{"key":"key-10.20965\/ijat.2026.p0308-28","doi-asserted-by":"crossref","unstructured":"N. Kochi, T. Tanabata, A. Hayashi, and S. Isobe, \u201cA 3D shape-measuring system for assessing strawberry fruits,\u201d Int. J. Automation Technol., Vol.12, No.3, pp. 395-404, 2018. https:\/\/doi.org\/10.20965\/ijat.2018.p0395","DOI":"10.20965\/ijat.2018.p0395"},{"key":"key-10.20965\/ijat.2026.p0308-29","doi-asserted-by":"crossref","unstructured":"J. Li et al., \u201cStructural parameter determination and pruning pattern analysis of pear tree shoots for dormant pruning,\u201d Plant Phenomics, Vol.7, No.4, Article No.100136, 2025. https:\/\/doi.org\/10.1016\/j.plaphe.2025.100136","DOI":"10.1016\/j.plaphe.2025.100136"},{"key":"key-10.20965\/ijat.2026.p0308-30","unstructured":"T. Kawai et al., \u201cEstimation of the amount of pruning in peach using 3D point cloud data,\u201d Horticultural Research (Japan) Supplement, Vol.23, No.1, p. 69, 2024 (in Japanese)."},{"key":"key-10.20965\/ijat.2026.p0308-31","unstructured":"J. Lee, E. Morimoto, K. Nonami, A. Tanino, and T. Yamaguchi, \u201cDevelopment of tree vigor evaluation method for Japanese pear by 3D point cloud data,\u201d J. of Japanese Society of Agricultural Technology Management, Vol.29, No.2, pp. 41-46, 2022 (in Japanese)."},{"key":"key-10.20965\/ijat.2026.p0308-32","doi-asserted-by":"crossref","unstructured":"C. Zhang et al., \u201cApple tree branch information extraction from terrestrial laser scanning and backpack-LiDAR,\u201d Remote Sensing, Vol.12, No.21, Article No.3592, 2020. https:\/\/doi.org\/10.3390\/rs12213592","DOI":"10.3390\/rs12213592"},{"key":"key-10.20965\/ijat.2026.p0308-33","doi-asserted-by":"crossref","unstructured":"L. Ferreira et al., \u201cComparative analysis of TLS and UAV sensors for estimation of grapevine geometric parameters,\u201d Sensors, Vol.24, No.16, Article No.5183, 2024. https:\/\/doi.org\/10.3390\/s24165183","DOI":"10.3390\/s24165183"},{"key":"key-10.20965\/ijat.2026.p0308-34","unstructured":"R. de Silva et al., \u201cSemantic-aware particle filter for reliable vineyard robot localisation,\u201d arXiv:2509.18342, 2025. https:\/\/doi.org\/10.48550\/arXiv.2509.18342"},{"key":"key-10.20965\/ijat.2026.p0308-35","doi-asserted-by":"crossref","unstructured":"Y. Li, Q. Feng, C. Ji, J. Sun, and Y. Sun, \u201cGNSS and LiDAR integrated navigation method in orchards with intermittent GNSS dropout,\u201d Applied Sciences, Vol.14, No.8, Article No.3231, 2024. https:\/\/doi.org\/10.3390\/app14083231","DOI":"10.3390\/app14083231"},{"key":"key-10.20965\/ijat.2026.p0308-36","doi-asserted-by":"crossref","unstructured":"M. Hrdina et al., \u201cObtaining the highest quality from a low-cost mobile scanner: A comparison of several pipelines with a new scanning device,\u201d Remote Sensing, Vol.17, No.15, Article No.2564, 2025. https:\/\/doi.org\/10.3390\/rs17152564","DOI":"10.3390\/rs17152564"},{"key":"key-10.20965\/ijat.2026.p0308-37","doi-asserted-by":"crossref","unstructured":"M. Balestra et al., \u201cAdvancing forest inventory: A comparative study of low-cost MLS lidar device with professional laser scanners,\u201d The Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XLVIII-2\/W8-2024, pp. 9-15, 2024. https:\/\/doi.org\/10.5194\/isprs-archives-XLVIII-2-W8-2024-9-2024","DOI":"10.5194\/isprs-archives-XLVIII-2-W8-2024-9-2024"},{"key":"key-10.20965\/ijat.2026.p0308-38","doi-asserted-by":"crossref","unstructured":"M. R. James and S. Robson, \u201cStraightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application,\u201d J. of Geophysical Research: Earth Surface, Vol.117, No.F3, Article No.F03017, 2012. https:\/\/doi.org\/10.1029\/2011JF002289","DOI":"10.1029\/2011JF002289"},{"key":"key-10.20965\/ijat.2026.p0308-39","doi-asserted-by":"crossref","unstructured":"S. Jiang, K. You, Y. Li, D. Weng, and W. Chen, \u201c3D reconstruction of spherical images: A review of techniques, applications, and prospects,\u201d Geo-spatial Information Science, Vol.27, No.6, pp. 1959-1988, 2024. https:\/\/doi.org\/10.1080\/10095020.2024.2313328","DOI":"10.1080\/10095020.2024.2313328"},{"key":"key-10.20965\/ijat.2026.p0308-40","doi-asserted-by":"crossref","unstructured":"G. Kafataris, D. Skarlatos, M. Vlachos, and A. Agapiou, \u201cInvestigating the accuracy of a 360\u00b0 camera for 3D modeling in confined spaces: 360\u00b0 panorama vs 25-rig compared to TLS,\u201d ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.X-M-2-2025, pp. 139-146, 2025. https:\/\/doi.org\/10.5194\/isprs-annals-X-M-2-2025-139-2025","DOI":"10.5194\/isprs-annals-X-M-2-2025-139-2025"},{"key":"key-10.20965\/ijat.2026.p0308-41","doi-asserted-by":"crossref","unstructured":"I. Petrovska and B. Jutzi, \u201c3D Gaussian splatting methods for real-world scenarios,\u201d ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.X-G-2025, pp. 641-648, 2025. https:\/\/doi.org\/10.5194\/isprs-annals-X-G-2025-641-2025","DOI":"10.5194\/isprs-annals-X-G-2025-641-2025"},{"key":"key-10.20965\/ijat.2026.p0308-42","doi-asserted-by":"crossref","unstructured":"I. Petrovska and B. Jutzi, \u201cImpact of rain on 3D reconstruction with multi-view stereo, neural radiance fields and Gaussian splatting,\u201d ISPRS Annals of the photogrammetry, remote sensing and spatial information sciences, Vol.X-4\/W6-2025, pp. 169-176, 2025. https:\/\/doi.org\/10.5194\/isprs-annals-X-4-W6-2025-169-2025","DOI":"10.5194\/isprs-annals-X-4-W6-2025-169-2025"},{"key":"key-10.20965\/ijat.2026.p0308-43","unstructured":"F. 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Vol.18, No.8, pp. 1070-1083, 2009. https:\/\/doi.org\/10.1016\/j.autcon.2009.07.001","DOI":"10.1016\/j.autcon.2009.07.001"},{"key":"key-10.20965\/ijat.2026.p0308-46","doi-asserted-by":"crossref","unstructured":"M. S. Nielsen, I. Nikolov, E. K. Kruse, J. Garn\u00e6s, and C. B. Madsen, \u201cQuantifying the influence of surface texture and shape on structure from motion 3D reconstructions,\u201d Sensors. Vol.23, No.1, Article No.178, 2023. https:\/\/doi.org\/10.3390\/s23010178","DOI":"10.3390\/s23010178"},{"key":"key-10.20965\/ijat.2026.p0308-47","doi-asserted-by":"crossref","unstructured":"J. Torres-S\u00e1nchez et al., \u201cConfiguration and specifications of an autonomous quadruped robot with an embedded LiDAR sensor for characterizing woody crop canopies,\u201d Precision Agriculture \u201925 (Proc. of the 15th European Conf. on Precision Agriculture), pp. 481-487, 2025. https:\/\/doi.org\/10.1163\/9789004725232_062","DOI":"10.1163\/9789004725232_062"},{"key":"key-10.20965\/ijat.2026.p0308-48","doi-asserted-by":"crossref","unstructured":"E. Wetzel, J. Liu, T. Leathem, and A. 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