Integrating Genomic Selection and Machine Learning for Predicting Maize Yield Under Drought
Bibliographic record
Abstract
Drought stress severely constrains maize yields, posing a significant challenge to global food security. This study explores the integration of genomic selection (GS) and machine learning (ML) methods to improve the accuracy of maize yield prediction under drought conditions. First, we outline the principles of GS, highlighting its advantages over traditional breeding methods and its growing application in drought-tolerant breeding. Next, we explore the application of various ML algorithms (such as random forests, support vector machines, and deep learning) for crop yield prediction, along with their strengths and limitations in the context of genomics. We then propose strategies for integrating GS with ML, including hybrid modeling frameworks and context-specific optimization, and discuss recent trends and research advances. Particular emphasis is placed on drought-specific modeling approaches that incorporate stress-responsive traits and evaluate their predictive accuracy under water-deficit environments. A case study from sub-Saharan Africa illustrates the practical application of an integrated GS-ML prediction system and its implications for climate-resilient maize breeding. Despite this promising outlook, challenges remain, including data heterogeneity, model interpretability, and implementation barriers. This study summarizes the future prospects of advancing the integration of genomic selection and machine learning (GS-ML) through technological innovation and its potential to support global climate-smart maize breeding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".