ReviewPlants (Basel, Switzerland)2022
Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management.
Review in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drought-responsive genes in tomato: meta-analysis of gene expression using machine learning.Scientific reports · 2023Pooled it
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- AI and Robotics Advancement in Analytical Mineral Characterization and Mining Processes: A Review and Research Trends Analysis.Topics in current chemistry (Cham) · 2026Review
- Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.Sensors (Basel, Switzerland) · 2026Review
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Unlocking Plant Resilience: Metabolomic Insights into Abiotic Stress Tolerance in Crops.Metabolites · 2025Review
- A comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches.Frontiers in plant science · 2025Review
- Confronting the data deluge: How artificial intelligence can be used in the study of plant stress.Computational and structural biotechnology journal · 2024Review
- Heat Stress and Plant-Biotic Interactions: Advances and Perspectives.Plants (Basel, Switzerland) · 2024Review
- Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (HPlant molecular biology · 2024Article
- Radiation Hormesis in Barley Manifests as Changes in Growth Dynamics Coordinated with the Expression ofInternational journal of molecular sciences · 2024Article
- Early detection of abiotic stress in plants through SNARE proteins using hybrid feature fusion model.PeerJ. Computer science · 2024Article
- Factors influencing fruit cracking: an environmental and agronomic perspective.Frontiers in plant science · 2024Review
- Review
- Antioxidant and drought-acclimation responses in UV-B-exposed transgenic Nicotiana tabacum displaying constitutive overproduction of HPhotochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology · 2023Article
- Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Physiology and molecular biology of plants : an international journal of functional plant biology · 2023Article
- An artificial intelligence-integrated analysis of the effect of drought stress on root traits of "modern" and "ancient" wheat varieties.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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.
Indexed as
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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.