High-Throughput Screening of Wheat Genotypes for Drought Tolerance Using Aerial Thermal Imagery
Bibliographic record
Abstract
Drought tolerance is vital for wheat breeding to maintain yield under water stress. Field trials spanned rainfed and irrigated regimes to capture genotype × environment interactions. We used UAV-based thermal imaging for high-throughput phenotyping, supplemented with ground infrared thermometer (IRT) readings to validate UAV observations and yield data. Canopy Temperature Depression (CTD) indicated physiological traits: lower canopy temperatures reflected efficient water use, deeper roots, and controlled transpiration. Correlations between thermal indices and drought susceptibility index (DSI) demonstrated canopy temperature’s role in pinpointing stress-resilient genotypes, particularly in rainfed trials where CTD–yield relationships were stronger. We compared several modeling approaches for yield-based indices (drought resistance index, yield stability index) and spectral indices (green NDVI, NDWI). Linear Regression delivered the highest coefficients of determination (R20.65) across most combinations, underscoring its robustness and simplicity for breeding pipelines. This approach reduces manual phenotyping labor and accelerates selection cycles. It integrates easily into breeding workflows. Overall, UAV-based thermal phenotyping, when paired with vegetation indices and straightforward predictive models, offers a scalable, efficient method for identifying drought-resilient wheat genotypes and accelerating selection under field conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".