Figure Review of Genetic Approaches to Improve Yield and Starch Content in Sweet Potato
Bibliographic record
Abstract
Sweet potato ( Ipomoea batatas ) is a globally significant crop for both food and industrial use, with high yield and starch content playing crucial roles in meeting demands for food, feed, and bioenergy. However, improving sweet potato yield and starch content poses challenges due to its genetic complexity and environmental sensitivity. This study summarizes genetic improvement methods for enhancing sweet potato yield and starch content, focusing on traditional breeding, marker-assisted selection (MAS), genomic selection (GS), gene editing, and multi-omics integration strategies. In recent years, MAS and GS have shown distinct advantages in accelerating the selection of high-yield and high-starch traits in sweet potato. Gene editing technologies, such as CRISPR/Cas9, provide precise approaches for the targeted regulation of key genes. Additionally, multi-omics techniques, including transcriptomics, metabolomics, and proteomics, help elucidate the biological pathways and regulatory mechanisms that influence yield and starch synthesis, offering strong support for optimizing breeding strategies. This study provides a clear direction for sweet potato breeding research, advancing progress toward high-yield and high-starch content varieties and carrying profound implications for global agricultural production and sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".