ReviewFrontiers in plant science2019
Leveraging Image Analysis for High-Throughput Plant Phenotyping.
Review in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled 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.
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
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology.Frontiers in plant science · 2022Pooled it
- Multiomics Insights in Research of Nitric Oxide (NO) Donors in Plants upon Stress.International journal of molecular sciences · 2026Review
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Rapid Classification and Deep Learning-Based Development Estimation of the Seeds ofPlants (Basel, Switzerland) · 2026Article
- Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow.Plant methods · 2026Article
- Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses.Plants (Basel, Switzerland) · 2026Article
- Soy-RepViT: a lightweight deep learning framework for fine-grained recognition of soybean growth habits.Frontiers in plant science · 2026Article
- Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using YOLOv8.Frontiers in plant science · 2026Article
- A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond.Plant phenomics (Washington, D.C.) · 2025Review
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- Deep-learning-ready RGB-depth images of seedling development.Plant methods · 2025Article
- DIRT/µ: automated extraction of root hair traits using combinatorial optimization.Journal of experimental botany · 2025Article
- Automated dynamic phenotyping of whole oilseed rape (Frontiers in plant science · 2025Article
- Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet.Plant methods · 2024Article
- Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.Sensors (Basel, Switzerland) · 2024Article
- GRABSEEDS: extraction of plant organ traits through image analysis.Plant methods · 2024Article
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 2024Article
- PlantSR: Super-Resolution Improves Object Detection in Plant Images.Journal of imaging · 2024Article
- Phenotyping of Potato Plants Using Morphological and Physiological Tools.Plants (Basel, Switzerland) · 2024Article
- An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages.Frontiers in plant science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.
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.