ArticlePlant phenomics (Washington, D.C.)2023
Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Integrating 3D phenotyping and functional-structural plant models for crop ideotype breeding.Nature communications · 2026Review
- 3D point cloud driven organ semantic segmentation to assess maize structural responses along the planting-density gradient.Plant phenomics (Washington, D.C.) · 2026Article
- OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.Plant methods · 2026Article
- Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions.Frontiers in plant science · 2026Review
- Measurement of Maize Leaf Phenotypic Parameters Based on 3D Point Cloud.Sensors (Basel, Switzerland) · 2025Article
- Targeted integrating hyperspectral and metabolomic data with spectral indices and metabolite content models for efficient salt-tolerant phenotype discrimination inPlant phenomics (Washington, D.C.) · 2025Article
- Genetic resolution of multi-level plant height in common wheat using the 3D canopy model from ultra-low altitude unmanned aerial vehicle imagery.Plant phenomics (Washington, D.C.) · 2025Article
- StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis.Frontiers in plant science · 2025Article
- DCPlant phenomics (Washington, D.C.) · 2024Article
- Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.Plant phenomics (Washington, D.C.) · 2024Article
- Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling.Plant phenomics (Washington, D.C.) · 2024Article
- Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.Frontiers in plant science · 2024Article
- Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (
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Registered trials
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