ReviewPlanta2025
Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.
Review in Planta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 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
- Plant resistance: scientific basis and latest research progress.Frontiers in plant science · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MAIN
conclusionAI-driven key gene prediction is revolutionizing crop breeding, enhancing precision, efficiency, and sustainability while paving the way for intelligent, data-driven agricultural innovation. The integration of artificial intelligence (AI) into crop breeding is ushering agriculture into a data-driven era of precision practices, fundamentally reshaping the efficiency and accuracy of crop improvement. This review provides an in-depth analysis of recent advances in AI-based key gene prediction within the field of crop breeding. It comprehensively evaluates the application outcomes and potential impacts, encompassing multi-omics data integration, deep learning model construction, key gene prediction, and variety design. Representative models such as SoyDNGP have significantly improved the coefficient of determination (R
Indexed as
Identifiers
41065853What 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.