ArticleSensors (Basel, Switzerland)2016
Estimating 3D Leaf and Stem Shape of Nursery Paprika Plants by a Novel Multi-Camera Photography System.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- CLCFM3: A 3D Reconstruction Algorithm Based on Photogrammetry for High-Precision Whole Plant Sensing Using All-Around Images.Sensors (Basel, Switzerland) · 2025Article
- Combined Structural and Functional 3D Plant Imaging Using Structure from Motion.Sensors (Basel, Switzerland) · 2025Article
- A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.Journal of imaging · 2023Article
- Plant trait measurement in 3D for growth monitoring.Plant methods · 2022Article
- A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction.Frontiers in plant science · 2022Article
- Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping.Sensors (Basel, Switzerland) · 2020Article
- Analysing the phenotype development of soybean plants using low-cost 3D reconstruction.Scientific reports · 2020Article
- MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction.Plant phenomics (Washington, D.C.) · 2020Article
- Reconstruction method and optimum range of camera-shooting angle for 3D plant modeling using a multi-camera photography system.Plant methods · 2020Article
- Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features.Sensors (Basel, Switzerland) · 2019Article
- Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging.Sensors (Basel, Switzerland) · 2019Article
- Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants.Sensors (Basel, Switzerland) · 2019Article
- A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model.Sensors (Basel, Switzerland) · 2019Article
- Three-Dimensional Monitoring of Plant Structural Parameters and Chlorophyll Distribution.Sensors (Basel, Switzerland) · 2019Article
- Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images.Sensors (Basel, Switzerland) · 2018Article
- A novel 3D imaging system for strawberry phenotyping.Plant methods · 2017Article
- Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models.Sensors (Basel, Switzerland) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For plant breeding and growth monitoring, accurate measurements of plant structure parameters are very crucial. We have, therefore, developed a high efficiency Multi-Camera Photography (MCP) system combining Multi-View Stereovision (MVS) with the Structure from Motion (SfM) algorithm. In this paper, we measured six variables of nursery paprika plants and investigated the accuracy of 3D models reconstructed from photos taken by four lens types at four different positions. The results demonstrated that error between the estimated and measured values was small, and the root-mean-square errors (RMSE) for leaf width/length and stem height/diameter were 1.65 mm (R² = 0.98) and 0.57 mm (R² = 0.99), respectively. The accuracies of the 3D model reconstruction of leaf and stem by a 28-mm lens at the first and third camera positions were the highest, and the number of reconstructed fine-scale 3D model shape surfaces of leaf and stem is the most. The results confirmed the practicability of our new method for the reconstruction of fine-scale plant model and accurate estimation of the plant parameters. They also displayed that our system is a good system for capturing high-resolution 3D images of nursery plants with high efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.