ArticlePlant methods2020
Reconstruction method and optimum range of camera-shooting angle for 3D plant modeling using a multi-camera photography system.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Digitalised phenotyping of pepper (Capsicum spp.) using effective RGB imaging and optimised camera positioning.Scientific reports · 2026Article
- Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters.Plant methods · 2026Article
- Monitoring of the growth ofFrontiers in plant science · 2026Article
- Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants.Plant methods · 2025Article
- Adaptive Symmetry Self-Matching for 3D Point Cloud Completion of Occluded Tomato Fruits in Complex Canopy Environments.Plants (Basel, Switzerland) · 2025Article
- A 3D reconstruction platform for complex plants using OB-NeRF.Frontiers in plant science · 2025Article
- Three-Dimensional Leaf Edge Reconstruction Combining Two- and Three-Dimensional Approaches.Plant phenomics (Washington, D.C.) · 2024Article
- A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction.Frontiers in plant science · 2022Article
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9 authors.
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Abstract
backgroundMeasurement of plant structure is useful in monitoring plant conditions and understanding the responses of plants to environmental changes. 3D imaging technologies, especially the passive-SfM (Structure from Motion) algorithm combined with a multi-camera photography (MCP) system has been studied to measure plant structure due to its low-cost, close-range, and rapid image capturing ability. However, reconstruction of 3D plant models with complex structure is a time-consuming process and some systems have failed to reconstruct 3D models properly. Therefore, an MCP based SfM system was developed and an appropriate reconstruction method and optimal range of camera-shooting angles were investigated.
resultsAn MCP system which utilized 10 cameras and a rotary table for plant was developed. The 3D mesh model of a single leaf reconstruction using a set of images taken at each viewing zenith angle (VZA) from 12° (C2 camera) to 60° (C6 camera) by the MCP based SfM system had less undetected or unstable regions in comparison with other VZAs. The 3D mesh model of a whole plant, which merged 3D dense point cloud models built from a set of images taken at each appropriate VZA (Method 1), had high accuracy. The Method 1 error percentages for leaf area, leaf length, leaf width, stem height, and stem width are in the range of 2.6-4.4%, 0.2-2.2%, 1.0-4.9%, 1.9-2.8%, and 2.6-5.7% respectively. Also, the error of the leaf inclination angle was less than 5°. Conversely, the 3D mesh model of a whole plant built directly from a set of images taken at all appropriate VZAs (Method 2) had lower accuracy than that of Method 1. For Method 2, the error percentages of leaf area, leaf length, and leaf width are in the range of 3.1-13.3%, 0.4-3.3%, and 1.6-8.6%, respectively. It was difficult to obtain the error percentages of stem height and stem width because some information was missing in this model. In addition, the calculation time for Method 2 was 1.97 times longer computational time in comparison to Method 1.
conclusionsIn this study, we determined the optimal shooting angles on the MCP based SfM system developed. We found that it is better in terms of computational time and accuracy to merge partial 3D models from images taken at each appropriate VZA, then construct complete 3D model (Method 1), rather than to construct 3D model by using images taken at all appropriate VZAs (Method 2). This is because utilization of incorporation of incomplete images to match feature points could result in reduced accuracy in 3D models and the increase in computational time for 3D model reconstruction.
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