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Article

3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images

National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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Academic Editor: Mario Cunha
Agronomy 2022, 12(8), 1865; https://doi.org/10.3390/agronomy12081865
Received: 30 June 2022 / Revised: 1 August 2022 / Accepted: 1 August 2022 / Published: 8 August 2022
(This article belongs to the Special Issue Rapid Detection of Agricultural Products)
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 3D phenotyping pipeline, 3DPhenoMVS, to calculate 17 phenotypic traits of tomato plants covering the whole life cycle. Among all the phenotypic traits, six of them were used for accuracy evaluation because the true values can be generated by manual measurements, and the results showed that the R2 values between the phenotypic traits and the manual ones ranged from 0.72 to 0.97. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during seven 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 biomass and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato and other plants, and the generalization test was also complemented on other six species, which demonstrated that the proposed pipeline will benefit plant breeding, cultivation research, and functional genomics in the future. View Full-Text
Keywords: 3D phenotyping; 3D reconstructed point cloud; structure from motion; growth analysis; whole growth stages; tomato 3D phenotyping; 3D reconstructed point cloud; structure from motion; growth analysis; whole growth stages; tomato
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MDPI and ACS Style

Wang, Y.; Hu, S.; Ren, H.; Yang, W.; Zhai, R. 3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images. Agronomy 2022, 12, 1865. https://doi.org/10.3390/agronomy12081865

AMA Style

Wang Y, Hu S, Ren H, Yang W, Zhai R. 3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images. Agronomy. 2022; 12(8):1865. https://doi.org/10.3390/agronomy12081865

Chicago/Turabian Style

Wang, Yinghua, Songtao Hu, He Ren, Wanneng Yang, and Ruifang Zhai. 2022. "3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images" Agronomy 12, no. 8: 1865. https://doi.org/10.3390/agronomy12081865

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