ReviewOpen biology2022
Capturing crop adaptation to abiotic stress using image-based technologies.
Review in Open biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 59 citations in OpenAlex.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.Sensors (Basel, Switzerland) · 2026Review
- An explainable vision transformer with transfer learning based efficient drought stress identification.Plant molecular biology · 2025Article
- Phenotyping drought stress tolerance in citrus rootstocks using high-throughput imaging and physio-biochemical techniques.BMC plant biology · 2025Article
- StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis.Frontiers in plant science · 2025Article
- Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning.Frontiers in plant science · 2025Article
- EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.Frontiers in plant science · 2025Article
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- Quantifying aluminum toxicity effects on corn phenotype using advanced imaging technologies.Plant direct · 2024Article
- Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach.Journal of imaging · 2024Article
- Explainable light-weight deep learning pipeline for improved drought stress identification.Frontiers in plant science · 2024Article
- Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View.Plant phenomics (Washington, D.C.) · 2024Article
- Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss.Plant phenomics (Washington, D.C.) · 2024Article
- Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging.Frontiers in plant science · 2024Article
- Advances in viticultureFrontiers in plant science · 2024Article
- Review
- Identification of new cold tolerant Zoysia grass species using high-resolution RGB and multi-spectral imaging.Scientific reports · 2023Article
- Assessment of Soybean Lodging Using UAV Imagery and Machine Learning.Plants (Basel, Switzerland) · 2023Article
- Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses.Genes · 2023Review
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.
Indexed as
Identifiers
What Socratic 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 Socratic graph.