ArticlePlant methods2017
A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage.
Article in Plant methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Plant phenomics (Washington, D.C.) · 2025Article
- Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet.Plant methods · 2024Article
- An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.Methods in molecular biology (Clifton, N.J.) · 2024Review
- SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.Plant phenomics (Washington, D.C.) · 2024Article
- A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.Journal of imaging · 2023Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2023Article
- High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.Plant phenomics (Washington, D.C.) · 2023Article
- Elimination of Leaf Angle Impacts on Plant Reflectance Spectra Using Fusion of Hyperspectral Images and 3D Point Clouds.Sensors (Basel, Switzerland) · 2022Article
- PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants.Plant phenomics (Washington, D.C.) · 2022Article
- TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce.Frontiers in plant science · 2022Article
- Open source 3D phenotyping of chickpea plant architecture across plant development.Plant methods · 2021Article
- Three-Dimensional Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera.Sensors (Basel, Switzerland) · 2021Article
- Next-Generation Breeding Strategies for Climate-Ready Crops.Frontiers in plant science · 2021Review
- KAT4IA:Plant phenomics (Washington, D.C.) · 2021Article
- Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season.Annals of botany · 2020Article
- Growth monitoring of greenhouse lettuce based on a convolutional neural network.Horticulture research · 2020Article
- Image-Based Assessment of Drought Response in Grapevines.Frontiers in plant science · 2020Article
- Low-Cost Three-Dimensional Modeling of Crop Plants.Sensors (Basel, Switzerland) · 2019Article
- Quantifying Variation in Soybean Due to Flood Using a Low-Cost 3D Imaging System.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe fitness of the rape leaf is closely related to its biomass and photosynthesis. The study of leaf traits is significant for improving rape leaf production and optimizing crop management. Canopy structure and individual leaf traits are the major indicators of quality during the rape seedling stage. Differences in canopy structure reflect the influence of environmental factors such as water, sunlight and nutrient supply. The traits of individual rape leaves traits indicate the growth period of the rape as well as its canopy shape.
resultsWe established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted. To evaluate the measurement accuracy of leaf area and plant height, 66 rape seedlings were randomly selected for automatic and destructive measurements. Compared with the manual measurements, the mean absolute percentage error of automatic leaf area and plant height measurements was 3.68 and 6.18%, respectively, and the squares of the correlation coefficients (R
conclusionsIn this study, a nondestructive and high-throughput stereo-imaging system was developed to quantify canopy three-dimensional structure and individual leaf shape traits with improved accuracy, with implications for rape phenotyping, functional genomics, and breeding.
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