ArticleScientific reports2020
Analysing the phenotype development of soybean plants using low-cost 3D reconstruction.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- 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
- Leaf bidirectional reflectance distribution function (BRDF) prediction with phenotypic traits in four species: Development of a novel measuring and analyzing framework.Plant phenomics (Washington, D.C.) · 2025Article
- CLCFM3: A 3D Reconstruction Algorithm Based on Photogrammetry for High-Precision Whole Plant Sensing Using All-Around Images.Sensors (Basel, Switzerland) · 2025Article
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- A Review of Optical-Based Three-Dimensional Reconstruction and Multi-Source Fusion for Plant Phenotyping.Sensors (Basel, Switzerland) · 2025Review
- Plant height measurement using UAV-based aerial RGB and LiDAR images in soybean.Frontiers in plant science · 2025Article
- Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications.Scientific data · 2024Article
- Soybean genomics research community strategic plan: A vision for 2024-2028.The plant genome · 2024Article
- Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 2024Article
- PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone.Plant phenomics (Washington, D.C.) · 2024Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN.Frontiers in plant science · 2023Article
- Multi-Omics Techniques for Soybean Molecular Breeding.International journal of molecular sciences · 2022Review
- Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping.Sensors (Basel, Switzerland) · 2022Article
- A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction.Frontiers in plant science · 2022Article
- Open source 3D phenotyping of chickpea plant architecture across plant development.Plant methods · 2021Article
- Soybean Yield Formation Physiology - A Foundation for Precision Breeding Based Improvement.Frontiers in plant science · 2021Review
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15 authors.
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Abstract
With the development of digital agriculture, 3D reconstruction technology has been widely used to analyse crop phenotypes. To date, most research on 3D reconstruction of field crops has been limited to analysis of population characteristics. Therefore, in this study, we propose a method based on low-cost 3D reconstruction technology to analyse the phenotype development during the whole growth period. Based on the phenotypic parameters extracted from the 3D reconstruction model, we identified the "phenotypic fingerprint" of the relevant phenotypes throughout the whole growth period of soybean plants and completed analysis of the plant growth patterns using a logistic growth model. The phenotypic fingerprint showed that, before the R3 period, the growth of the five varieties was similar. After the R5 period, the differences among the five cultivars gradually increased. This result indicates that the phenotypic fingerprint can accurately reveal the patterns of phenotypic changes. The logistic growth model of soybean plants revealed the time points of maximum growth rate of the five soybean varieties, and this information can provide a basis for developing guidelines for water and fertiliser application to crops. These findings will provide effective guidance for breeding and field management of soybean and other crops.
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