Evidence map›Paper›PMID 29509841›Full record

ArticleAnnals of botany2018

Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations.

Fang Hui, Jinyu Zhu, Pengcheng Hu, Lei Meng, Binglin Zhu, Yan Guo, Baoguo Li, Yuntao Ma

Abstract readEvaluation Study
In one paragraph

Article in Annals of botany, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fang HuiKey Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Jinyu ZhuInstitute of Vegetables and Flowers, Chinese Academy of Agricultural Science, Beijing, China.
Pengcheng HuKey Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Lei MengDepartment of Geography and Institute of the Environment and Sustainability, Western Michigan University, Kalamazoo, MI, USA.
Binglin ZhuKey Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Yan GuoKey Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Baoguo LiKey Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Yuntao MaKey Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Global agriculture is facing the challenge of a phenotyping bottleneck due to large-scale screening/breeding experiments with improved breeds. Phenotypic analysis with high-throughput, high-accuracy and low-cost technologies has therefore become urgent. Recent advances in image-based 3D reconstruction offer the opportunity of high-throughput phenotyping. The main aim of this study was to quantify and evaluate the canopy structure of plant populations in two and three dimensions based on the multi-view stereo (MVS) approach, and to monitor plant growth and development from seedling stage to fruiting stage. Methods: Multi-view images of flat-leaf cucumber, small-leaf pepper and curly-leaf eggplant were obtained by moving a camera around the plant canopy. Three-dimensional point clouds were reconstructed from images based on the MVS approach and were then converted into surfaces with triangular facets. Phenotypic parameters, including leaf length, leaf width, leaf area, plant height and maximum canopy width, were calculated from reconstructed surfaces. Accurate evaluation in 2D and 3D for individual leaves was performed by comparing reconstructed phenotypic parameters with referenced values and by calculating the Hausdorff distance, i.e. the mean distance between two surfaces. Key Results: Our analysis demonstrates that there were good agreements in leaf parameters between referenced and estimated values. A high level of overlap was also found between surfaces of image-based reconstructions and laser scanning. Accuracy of 3D reconstruction of curly-leaf plants was relatively lower than that of flat-leaf plants. Plant height of three plants and maximum canopy width of cucumber and pepper showed an increasing trend during the 70 d after transplanting. Maximum canopy width of eggplants reached its peak at the 40th day after transplanting. The larger leaf phenotypic parameters of cucumber were mostly found at the middle-upper leaf position. Conclusions: High-accuracy 3D evaluation of reconstruction quality indicated that dynamic capture of the 3D canopy based on the MVS approach can be potentially used in 3D phenotyping for applications in breeding and field management.

Indexed as

AgricultureCucumis sativusFruitImage Processing, Computer-AssistedPhenotypePiperPlant BreedingPlant LeavesSeedlingsSolanum melongena

Identifiers

PMID29509841
PMCPMC5906925

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Registered trials

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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.