ng Agricultural University and 478 Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We 479 thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and 480 experimental resources. 481 482 Data Availability 483 All the phenotypic data and images can be viewed and downloaded via the link 484 (http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485 486 References 487 Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d 488 Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives 489 of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144. 490 An N, Welch SM, Markelz RJC, Baker RL, Palmer CM,
Open resource ↗plantphenomics.hzau.edu.cn · pdf-raw-page:24 lines:1-78Unverified paper record
4DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using a 3D Reconstruction Point Cloud Based on Multiview Images
bioRxiv · 12 Nov 2021 · 10.1101/2021.11.09.467984
Abstract
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Plant phenotyping relevance
低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
abstractThe results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8.
abstractwe developed a low-cost and open-access 3D phenotyping pipeline for tomato plants
Code and data availability
The paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.
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