ArticleApplications in plant sciences2020
Automated trichome counting in soybean using advanced image-processing techniques.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 32 citations in OpenAlex.
- Effect of developmental dynamics on WRKY expression in barley with varying phenologies and trichome micromorphologies.BMC plant biology · 2025Article
- HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait.Plant methods · 2024Article
- SDG26 Is Involved in Trichome Control inPlants (Basel, Switzerland) · 2023Article
- Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.Frontiers in plant science · 2023Article
- TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting.Plant phenomics (Washington, D.C.) · 2023Article
- HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.Plant methods · 2022Article
- Wheat Spike Detection and Counting in the Field Based on SpikeRetinaNet.Frontiers in plant science · 2022Article
- Molecular Mechanisms of Plant Trichome Development.Frontiers in plant science · 2022Review
- Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning.Frontiers in plant science · 2021Article
- Automated trichome counting in soybean using advanced image-processing techniques.Applications in plant sciences · 2020Article
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseTrichomes are hair-like appendages extending from the plant epidermis. They serve many important biotic roles, including interference with herbivore movement. Characterizing the number, density, and distribution of trichomes can provide valuable insights on plant response to insect infestation and define the extent of plant defense capability. Automated trichome counting would speed up this research but poses several challenges, primarily because of the variability in coloration and the high occlusion of the trichomes. METHODS AND
resultsWe developed a simplified method for image processing for automated and semi-automated trichome counting. We illustrate this process using 30 leaves from 10 genotypes of soybean (
conclusionsWe address trichome counting challenges including occlusion by combining image processing with human intervention to propose a semi-automated method for trichome quantification. This provides new opportunities for the rapid and automated identification and quantification of trichomes, which has applications in a wide variety of disciplines.
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.