Development of a Semi-Automatic 3 D Modeling System for Phenotyping Morphological Traits in Plants

Tanabata, Takanari; Hayashi, Atsushi; Kochi, Nobuo; Isobe, Sachiko

doi:10.1109/iecon.2018.8592904

Cited by 6 publications

(9 citation statements)

References 13 publications

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“…The iPOTs is designed to be used in combination with 3D imaging‐based phenotyping facilities to analyze the shoot architecture (Tanabata et al., 2018) and RSA (RSAvis3D; Teramoto et al., 2020), thus creating a high‐throughput phenotyping platform. The underground sensor devices, for instance, were designed to minimize the effect of X‐rays and avoid their interruption while capturing CT images.…”

Section: Discussionmentioning

confidence: 99%

“…Non‐destructive 3D imaging was conducted at 28 days after sowing. To examine shoot architecture, plant multi‐view images (4096 pixel × 2160 pixel resolution) obtained by four cameras (TRI089S‐CC, LUCID vision labs, http://thinklucid.com) were synthesized to reconstruct a 3D model, according to the method reported previously (Tanabata et al., 2018). Each plant pot was placed on a motorized stage, with three poles attached to the periphery of the stage, for capturing 360° images.…”

Section: Methodsmentioning

confidence: 99%

“…The poles have random dot patterns and target markers to detect the capture position with Agisoft Metashape API (Agisoft, http://www.agisoft.com). The 3D image reconstruction was conducted using the structure from motion and multi‐view stereo technologies (Tanabata et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

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iPOTs: Internet of Things‐based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

et al. 2021

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SUMMARY A cultivation facility that can assist users in controlling the soil water condition is needed for accurately phenotyping plants under drought stress in an artificial environment. Here we report the Internet of Things‐based pot system controlling optional treatment of soil water condition (iPOTs), an automatic irrigation system that mimics the drought condition in a growth chamber. The Wi‐Fi‐enabled iPOTs system allows water supply from the bottom of the pot, based on the soil water level set by the user, and automatically controls the soil water level at a desired depth. The iPOTs also allows users to monitor environmental parameters, such as soil temperature, air temperature, humidity, and light intensity, in each pot. To verify whether the iPOTs mimics the drought condition, we conducted a drought stress test on rice (Oryza sativa L.) varieties and near‐isogenic lines, with diverse root system architecture, using the iPOTs system installed in a growth chamber. Similar to the results of a previous drought stress field trial, the growth of shallow‐rooted rice accessions was severely affected by drought stress compared with that of deep‐rooted accessions. The microclimate data obtained using the iPOTs system increased the accuracy of plant growth evaluation. Transcriptome analysis revealed that pot positions in the growth chamber had little impact on plant growth. Together, these results suggest that the iPOTs system is a reliable platform for phenotyping plants under drought stress.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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iPOTs: Internet of Things‐based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

et al. 2021

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“…Meanwhile, photogrammetry of plants and trees is sometimes challenging because of the repetition of similar textures, resulting in a low-quality outcome or failure of 3D reconstruction. Therefore, to achieve a better and faster reconstruction of 3D models for high-throughput phenotyping, some studies have discussed ways to develop multiview imaging systems (Gao et al 2021, Tanabata et al 2018, Wu et al 2020, or the ways to select suitable images from multiple images…”

Section: Multiview 3d Reconstruction With Photogrammetrymentioning

confidence: 99%

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Okura

2022

Breed. Sci.

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This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants ( modeling ) and 2) creating models from real-world plants ( reconstruction ). This paper also discusses the limitations of current methods and possible future directions.

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“…Real plant (soybeans) scene is a set of multi-view images of a soybean plant captured by a multi-view capturing system [57], which was originally created for plant science studies. We used 28 views for instance matching.…”

Section: Datasetmentioning

confidence: 99%

Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction

Doi

Okura

Nagahara

et al. 2020

Preprint

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This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching. Multiview instance segmentation becomes challenging for scenes with repetitive textures and shapes, e.g., plant leaves, due to the difficulty of multiview matching using texture or shape descriptors. To this end, we propose a multi-view region matching method based on epipolar geometry, which does not rely on any feature descriptors. We further show that the epipolar region matching can be easily integrated into instance segmentation and effective for instance-wise 3D reconstruction. Experiments demonstrate the improved accuracy of multi-view instance matching and the 3D reconstruction compared to the baseline methods.

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Development of a Semi-Automatic 3 D Modeling System for Phenotyping Morphological Traits in Plants

Cited by 6 publications

References 13 publications

iPOTs: Internet of Things‐based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

iPOTs: Internet of Things‐based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

Descriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction

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