ReviewPhenomics (Cham, Switzerland)2022
A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping.
Review in Phenomics (Cham, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.
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
98 citing papers in PubMed, 1 synthesis or guideline pooled it, 248 citations in OpenAlex.
- Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding.Scientific reports · 2022Pooled it
- Unlocking the potential of spring wheat genetic resources: uncovering resistance sources against leaf rust and yellow rust.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Estimation of the Comprehensive High Photosynthetic-Efficiency Phenotypic Index in Winter Wheat Based on UAV Multimodal Remote Sensing Data.Plants (Basel, Switzerland) · 2026Article
- Proxima Green: RGB Color Metrics for Turfgrass Phenotyping in Controlled Conditions.Sensors (Basel, Switzerland) · 2026Article
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Image-based high-throughput phenotyping enables genetic analyses of pod morphological traits in mungbean (Vigna radiata (L.) R. Wilczek).G3 (Bethesda, Md.) · 2026Article
- Early screening of salt-tolerant and high-yielding rice varieties via cost-effective UAV data.Plant phenomics (Washington, D.C.) · 2026Article
- Projecting 2D top-view of PSII efficiency onto 3D plant models to quantify PSII efficiency across canopy layers.Plant methods · 2026Article
- Regional Cultivation Forms ofJournal of fungi (Basel, Switzerland) · 2026Review
- Integrating image-based phenotyping and QTL mapping to enhance genetic resistance and accelerate breeding for bacterial grain rot resistance in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping.Scientific data · 2026Article
- The future is FAIR: a community framework for enhanced data management and data sharing.Database : the journal of biological databases and curation · 2026Article
- Three-dimensional organ segmentation and structural phenotyping of salt-stressed coriander seedlings using the optimized point transformer-based model PTV-SegCo.Frontiers in plant science · 2026Article
- Drought adaptation in spring wheat seedlings relies on coordinated deep root architecture and cortical tissue allocation.Frontiers in plant science · 2026Article
- Drought tolerance classification using unmanned aerial systems based on RGB and multispectral data.Frontiers in plant science · 2026Article
- Post-transcriptional regulation of light-stress responses and predictive modeling in vegetableFrontiers in plant science · 2026Review
- Advancing anthracnose resistance in dry beans through the transition from traditional to computational breeding efforts.Frontiers in plant science · 2026Review
- Fungal foe: exploring cotton's physiological responses to Verticillium wilt.Frontiers in plant science · 2026Review
38 more citing papers are in PubMed but not listed here.
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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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.