{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T05:47:18Z","timestamp":1785822438955,"version":"3.56.0"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"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                    Leaf parameters are crucial indicators reflecting the growing status of plants. Monitoring and analysis of leaf parameters significantly contributes to the improvement of crop yield and food quality. This study focused on three tomato plant varieties commonly grown in the Netherlands and proposed a fully automatic pipeline for leaf phenotyping. Three-dimensional (3D) point clouds of target plants were acquired with a specially designed imaging unit naming Maxi-Marvin. A semantic segmentation of plant organs was performed with PointNet++ model. To mitigate point cloud resolution decrement, the down-sampling operation in the baseline model was replaced with a distributed segmentation strategy. Leaf instances were further identified with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), followed by a morphological phenotypic trait quantification based on 3D geometrical analysis. Target phenotypic traits including leaf length, leaf width, and leaf area. The evaluation results indicated that the distributed segmentation strategy achieved the best\n                    <jats:italic>F<\/jats:italic>\n                    1 scores of 0.98 with block size set to 30,000. The Mean Average Errors (MAE) of leaf length, leaf width, and leaf area estimation were 2.09\u00a0cm, 1.78\u00a0cm and 8.98 cm\n                    <jats:sup>2<\/jats:sup>\n                    respectively. The estimation accuracies for leaf length, leaf width, and leaf area were 91.98%, 92.66%, and 89.67%, respectively.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1839309","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T05:36:42Z","timestamp":1785821802000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A distributed segmentation strategy developed for three-dimensional leaf trait quantification of tomato plants"],"prefix":"10.3389","volume":"17","author":[{"given":"Bolai","family":"Xin","sequence":"first","affiliation":[{"name":"College of Engineering, South China Agricultural University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Lie","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiyuan","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjie","family":"Zhan","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunming","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, South China Agricultural University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s13007-022-00889-9","article-title":"Plant trait measurement in 3d for growth monitoring","volume":"18","author":"Abhipray","year":"2022","journal-title":"Plant Methods"},{"key":"B2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.biosystemseng.2020.01.023","article-title":"Robust node detection and tracking in fruit-vegetable crops using deep learning and multi-view imaging","volume":"192","author":"Boogaard","year":"2020","journal-title":"Biosyst. 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Electron. Agric."},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1045545","article-title":"3d data-augmentation methods for semantic segmentation of tomato plant parts","volume":"14","author":"Xin","year":"2023","journal-title":"Front. Plant Sci."},{"key":"B22","first-page":"2464","article-title":"A technical survey and evaluation of traditional point cloud clustering methods for lidar panoptic segmentation","author":"Zhao","year":"2021"}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1839309\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T05:36:43Z","timestamp":1785821803000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2026.1839309\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,4]]},"references-count":21,"alternative-id":["10.3389\/fpls.2026.1839309"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2026.1839309","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,4]]},"article-number":"1839309"}}