ReviewPlants (Basel, Switzerland)2026
Early Plant Development as a Systems-Level Trait: Integrating Omics, Artificial Intelligence, and Emerging Biotechnologies.
Review in Plants (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Hormone-metabolism crosstalk in plants: an integrated signaling network coordinating growth and stress adaptation.Plant signaling & behavior · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seed germination and early seedling development are critical determinants of crop establishment, stress tolerance, and yield stability, yet these stages remain insufficiently integrated into contemporary crop improvement strategies. Recent advances across genome editing, microbiome-assisted seed treatments, nanotechnology-enabled priming, and artificial intelligence-guided phenotyping have generated substantial but fragmented insights into early developmental regulation. This review synthesizes recent advances across early plant development research. It demonstrates that seemingly diverse technologies converge on a limited set of regulatory control nodes, including abscisic acid-gibberellin balance, redox homeostasis, and root system architectural plasticity. By integrating evidence from molecular, microbial, physicochemical, and computational studies, early plant ontogeny is presented as a tunable regulatory state governed by quantitative thresholds rather than as a strictly predetermined genetic process. Advances in deep learning, reinforcement learning, and high-throughput phenotyping further enable the modeling and optimization of early developmental trajectories across genotype by environment contexts. Together, these insights establish early development as a programmable target for crop improvement and provide a mechanistic foundation for designing integrated interventions that enhance developmental uniformity, stress resilience, and yield stability across diverse agroecological systems.
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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.