ArticlePlanta2012
Extraction of quantitative characteristics describing wheat leaf pubescence with a novel image-processing technique.
Article in Planta, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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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.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves.Plants (Basel, Switzerland) · 2024Article
- HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait.Plant methods · 2024Article
- Decrypting the multi-functional biological activators and inducers of defense responses against biotic stresses in plants.Heliyon · 2023Review
- Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.Frontiers in plant science · 2023Article
- HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.Plant methods · 2022Article
- Article
- LSM-WBMC systems biology · 2019Article
- Article
- Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leaf pubescence (hairiness) in wheat plays an important biological role in adaptation to the environment. However, this trait has always been methodologically difficult to phenotype. An important step forward has been taken with the use of computer technologies. Computer analysis of a photomicrograph of a transverse fold line of a leaf is proposed for quantitative evaluation of wheat leaf pubescence. The image-processing algorithm is implemented in the LHDetect2 software program accessible as a Web service at http://wheatdb.org/lhdetect2 . The results demonstrate that the proposed method is rapid, adequately assesses leaf pubescence density and the length distribution of trichomes and the data obtained using this method are significantly correlated with the density of trichomes on the leaf surface. Thus, the proposed method is efficient for high-throughput analysis of leaf pubescence morphology in cereal genetic collections and mapping populations.
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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.