ArticlePlant phenomics (Washington, D.C.)2020
Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
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
23 citing papers in PubMed, 46 citations in OpenAlex.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Bridging domain gaps in agricultural 3D point cloud classification using adversarial domain adaptation.Scientific reports · 2025Article
- Seeing the unseen: A novel approach to extract latent plant root traits from digital images.Plant phenomics (Washington, D.C.) · 2025Article
- Phenome-to-genome insights for evaluating root system architecture in field studies of maize.The plant genome · 2025Article
- Breaking the field phenotyping bottleneck in maize with autonomous robots.Communications biology · 2025Article
- Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement.Frontiers in plant science · 2025Article
- High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.Plant phenomics (Washington, D.C.) · 2024Article
- Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.Frontiers in plant science · 2024Article
- Disentangling genotype and environment specific latent features for improved trait prediction using a compositional autoencoder.Frontiers in plant science · 2024Article
- Focusing the GWAS Lens on days to flower using latent variable phenotypes derived from global multienvironment trials.The plant genome · 2023Article
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Classification of high-throughput phenotyping data for differentiation among nutrient deficiency in common bean.Frontiers in plant science · 2022Article
- Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum.Frontiers in artificial intelligence · 2022Article
- Multi-dimensional leaf phenotypes reflect root system genotype in grafted grapevine over the growing season.GigaScience · 2021Article
- Quinoa Phenotyping Methodologies: An International Consensus.Plants (Basel, Switzerland) · 2021Review
- Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.Plant communications · 2021Review
- Machine learning in plant science and plant breeding.iScience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C
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.