ReviewFrontiers in plant science2024
Harnessing the power of machine learning for crop improvement and sustainable production.
Review in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Research opportunities to advance cardiovascular health through a planetary health lens.American journal of preventive cardiology · 2026Review
- Year-independent prediction of rice grain protein content using machine learning with agronomy-aligned multi-year field data.Frontiers in plant science · 2026Article
- A human in the loop approach to applying large language models for farm management insight.Scientific reports · 2025Article
- Evaluation of attitudes of university students towards artificial intelligence using data mining methods.Scientific reports · 2025Article
- Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.Scientific reports · 2025Article
- Molecular Breeding for Abiotic Stress Tolerance in Crops: Recent Developments and Future Prospectives.International journal of molecular sciences · 2025Review
- Advanced technologies for reducing greenhouse gas emissions from rice fields: Is hybrid rice the game changer?Plant communications · 2025Review
- Recent advances in plant stress analysis using chlorophyllPhotosynthetica · 2025Review
- Decrypting molecular mechanism of heat stress tolerance in rice to tackle climate change challenges through recent approaches.Frontiers in plant science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.
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.