{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T06:15:45Z","timestamp":1788156945226,"version":"build-2803163510"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem\u2013leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow\u2013deep feature fusion, and a combined cross-entropy\u2013Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L\n                    <jats:sup>\u22121<\/jats:sup>\n                    ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training\u2013validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors\u2014plant height, projected area, voxel occupancy volume, and leaf point ratio\u2014were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R\n                    <jats:sup>2<\/jats:sup>\n                    values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1883203","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:40:23Z","timestamp":1788154823000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Three-dimensional organ segmentation and structural phenotyping of salt-stressed coriander seedlings using the optimized point transformer-based model PTV-SegCo"],"prefix":"10.3389","volume":"17","author":[{"given":"Shihan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujuan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erpeng","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun\u2019ao","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyu","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuqing","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"201","DOI":"10.5455\/egyjebb.20231220081755","article-title":"Physiological response of coriander plant - grown in Duba, Saudi Arabia - to NaCl stress","volume":"19","author":"Alghabban","year":"2023","journal-title":"Egypt. J. Exp. Biol. (Bot.)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.838190","article-title":"Improved point-cloud segmentation for plant phenotyping through class-dependent sampling of training data to battle class imbalance","volume":"13","author":"Boogaard","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B3","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1186\/s12870-024-05517-3","article-title":"Promoting salt tolerance, growth, and phytochemical responses in coriander (Coriandrum sativum L. cv. Balady) via eco-friendly Bacillus subtilis and cobalt","volume":"24","author":"Brengi","year":"2024","journal-title":"BMC Plant Biol."},{"key":"B4","first-page":"77","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","author":"Charles","year":"2017"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3390\/plants13162300","article-title":"A precise segmentation algorithm of pumpkin seedling point cloud stem based on CPHNet","volume":"13","author":"Deng","year":"2024","journal-title":"Plants"},{"key":"B6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.pbi.2015.02.006","article-title":"Lights, camera, action: High-throughput plant phenotyping is ready for a close-up","volume":"24","author":"Fahlgren","year":"2015","journal-title":"Curr. Opin. Plant Biol."},{"key":"B7","volume-title":"Global Map of Salt-Affected Soils (Gsasmap V1.0)","year":"2021"},{"key":"B8","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1007\/s43657-022-00048-z","article-title":"A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping","volume":"2","author":"Gill","year":"2022","journal-title":"Phenomics"},{"key":"B9","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s13007-023-01031-z","article-title":"How to make sense of 3D representations for plant phenotyping: A compendium of processing and analysis techniques","volume":"19","author":"Harandi","year":"2023","journal-title":"Plant Methods"},{"key":"B10","doi-asserted-by":"publisher","first-page":"963","DOI":"10.2307\/2529876","article-title":"Random-effects models for longitudinal data","volume":"38","author":"Laird","year":"1982","journal-title":"Biometrics"},{"key":"B11","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.hpj.2022.09.013","article-title":"Nondestructive detection of key phenotypes for the canopy of the watermelon plug seedlings based on deep learning","volume":"12","author":"Li","year":"2026","journal-title":"Hortic. Plant J."},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110049","article-title":"FACNet: A high-precision pumpkin seedling point cloud organ segmentation method","volume":"231","author":"Liu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"B13","first-page":"565","article-title":"V-Net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"B14","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.inpa.2018.09.005","article-title":"Image based leaf segmentation and counting in rosette plants","volume":"6","author":"Praveen Kumar","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"B15","first-page":"5099","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","author":"Qi","year":"2017"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2025.1491170","article-title":"Plant stem and leaf segmentation and phenotypic parameter extraction using neural radiance fields and lightweight point cloud segmentation networks","volume":"16","author":"Qiao","year":"2025","journal-title":"Front. Plant Sci."},{"key":"B17","first-page":"4104","article-title":"Structure-from-motion revisited","author":"Sch\u00f6nberger","year":"2016"},{"key":"B18","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/978-3-319-46487-9_31","article-title":"Pixelwise view selection for unstructured multi-view stereo","volume-title":"Computer Vision \u2013 Eccv 2016","author":"Sch\u00f6nberger","year":"2016"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1029\/2023RG000804","article-title":"Multi\u2010scale soil salinization dynamics from global to pore scale: A review","volume":"62","author":"Shokri","year":"2024","journal-title":"Rev. Geophys."},{"key":"B20","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.aiia.2025.01.006","article-title":"Comprehensive review on 3D point cloud segmentation in plants","volume":"15","author":"Song","year":"2025","journal-title":"Artif. Intell. Agric."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106797","article-title":"Pixelwise instance segmentation of leaves in dense foliage","volume":"195","author":"Vayssade","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.stress.2024.100652","article-title":"Integrative mechanisms of plant salt tolerance: Biological pathways, phytohormonal regulation, and technological innovations","volume":"14","author":"Waheed","year":"2024","journal-title":"Plant Stress"},{"key":"B23","first-page":"11531","article-title":"ECA-Net: Efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2020"},{"key":"B24","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.foodchem.2019.01.171","article-title":"Phytochemical and bioactive profile of Coriandrum sativum L","volume":"286","author":"Wei","year":"2019","journal-title":"Food Chem."},{"key":"B25","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.jare.2021.05.002","article-title":"Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review","volume":"35","author":"Xiao","year":"2022","journal-title":"J. Adv. Res."},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.3390\/rs16173290","article-title":"Delving into the potential of deep learning algorithms for point cloud segmentation at organ level in plant phenotyping","volume":"16","author":"Xie","year":"2024","journal-title":"Remote Sens."},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.34133\/plantphenomics.0254","article-title":"Cucumber seedling segmentation network based on a multiview geometric graph encoder from 3D point clouds","volume":"6","author":"Zhang","year":"2024","journal-title":"Plant Phenomics"},{"key":"B28","first-page":"16239","article-title":"Point transformer","author":"Zhao","year":"2021"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3390\/ijms252010940","article-title":"Understanding of plant salt tolerance mechanisms and application to molecular breeding","volume":"25","author":"Zhou","year":"2024","journal-title":"IJMS"}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1883203\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:40:24Z","timestamp":1788154824000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1883203\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":29,"alternative-id":["10.3389\/fpls.2026.1883203"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1883203","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"article-number":"1883203"}}