ReviewGigaScience2017
Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.
Review in GigaScience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed.
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Morphometric trait analysis and machine learning-based yield modeling in wood apple (Feronia limonia L.).BMC plant biology · 2025Article
- Cutting-edge computational approaches to plant phenotyping.Plant molecular biology · 2025Review
- Deep learning-based approach for phenotypic trait extraction and computation of tomato under varying water stress.Frontiers in plant science · 2025Article
- Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field.Frontiers in plant science · 2025Article
- EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.Frontiers in plant science · 2025Article
- Needle angle dynamics as a rapid indicator of drought stress inFrontiers in plant science · 2025Article
- Search of Reflectance Indices for Estimating Photosynthetic Activity of Wheat Plants Under Drought Stress.Plants (Basel, Switzerland) · 2024Article
- Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling.Scientific reports · 2024Article
- Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View.Plant phenomics (Washington, D.C.) · 2024Article
- Article
- Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging.Frontiers in plant science · 2024Article
- The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.Plant phenomics (Washington, D.C.) · 2024Review
- Open-source workflow design and management software to interrogate duckweed growth conditions and stress responses.Plant methods · 2023Article
- Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses.Genes · 2023Review
- A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping.Plants (Basel, Switzerland) · 2023Review
- Feasibility Study on the Classification of Persimmon Trees' Components Based on Hyperspectral LiDAR.Sensors (Basel, Switzerland) · 2023Article
- Metabolic pathways engineering for drought or/and heat tolerance in cereals.Frontiers in plant science · 2023Review
- Plant responses to climate change, how global warming may impact on food security: a critical review.Frontiers in plant science · 2023Review
- Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.Frontiers in plant science · 2023Article
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 study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.
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.