ArticleFrontiers in plant science2022
A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Fine-grained 3D rice phenotyping via multi-scale NeRF and multimodal segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- 3D point cloud driven organ semantic segmentation to assess maize structural responses along the planting-density gradient.Plant phenomics (Washington, D.C.) · 2026Article
- Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- 2D-to-3D Image Reconstruction in Agriculture: A Review of Methods, Challenges, and AI-Driven Opportunities.Sensors (Basel, Switzerland) · 2026Review
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow.Plant methods · 2026Article
- High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.Plant methods · 2026Article
- Balancing accuracy, completeness, and efficiency for rice 3D reconstruction through CBAM-UNet-based multi-view segmentation and camera configuration optimization.Frontiers in plant science · 2026Article
- PMSPA-Net framework with geometric region decomposition for high-precision phenotyping of field-grown maize seedlings.Frontiers in plant science · 2026Article
- Three-dimensional reconstruction of densely planted rice seedlings based on MultiView images.Plant phenomics (Washington, D.C.) · 2025Article
- Design of a binocular multispectral stereo imaging system and its application in plant phenotyping.Plant phenomics (Washington, D.C.) · 2025Article
- A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond.Plant phenomics (Washington, D.C.) · 2025Review
- Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.Plant molecular biology · 2025Review
- Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants.Plant methods · 2025Article
- Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras.Plants (Basel, Switzerland) · 2025Article
- High-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields.GigaScience · 2025Article
- CropPhenoX: high-throughput automatic extraction system for wheat seedling phenotypic traits based on software and hardware collaboration.Frontiers in plant science · 2025Article
- Accurate plant 3D reconstruction and phenotypic traits extraction via stereo imaging and multi-view point cloud alignment.Frontiers in plant science · 2025Article
- A 3D reconstruction platform for complex plants using OB-NeRF.Frontiers in plant science · 2025Article
- Plant stem and leaf segmentation and phenotypic parameter extraction using neural radiance fields and lightweight point cloud segmentation networks.Frontiers in plant science · 2025Article
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9 authors.
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Abstract
Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition and automatic phenotypes extraction are common concerns in plant phenotyping. Despite the development of phenotyping platforms and the realization of high-throughput three-dimensional (3D) data acquisition in tall plants, such as maize, handling small-size plants with complex structural features remains a challenge. This study developed a miniaturized shoot phenotyping platform MVS-Pheno V2 focusing on low plant shoots. The platform is an improvement of MVS-Pheno V1 and was developed based on multi-view stereo 3D reconstruction. It has the following four components: Hardware, wireless communication and control, data acquisition system, and data processing system. The hardware sets the rotation on top of the platform, separating plants to be static while rotating. A novel local network was established to realize wireless communication and control; thus, preventing cable twining. The data processing system was developed to calibrate point clouds and extract phenotypes, including plant height, leaf area, projected area, shoot volume, and compactness. This study used three cultivars of wheat shoots at four growth stages to test the performance of the platform. The mean absolute percentage error of point cloud calibration was 0.585%. The squared correlation coefficient
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