Optimizing Seeding Ratio for Semi-Leafless and Leafed Pea Mixture with Precise UAV Quantification of Crop Lodging
Bibliographic record
Abstract
The field pea has both semi-leafless (SL) and leafed (L) types. Mixing these two types together might improve yield by optimizing pea solar radiation interception, reducing lodging, and decreasing disease. However, an optimum mixing ratio has not yet been established, since previous studies mixed two leaf types from two separate varieties. This study used four near-isogenic pairs of pea genotypes differing only in leaf type to determine the optimal mixing ratio for yield and agronomic traits. Two leaf types were mixed at seeding in five mixing ratios: 0:100, 50:50, 67:33, 83:17, and 100:0 SL/L. With precise UAV quantification of canopy height (r2 = 0.88, RMSE = 2.6 cm), the results showed that a ratio of over 67% semi-leafless pea had a 10% greater lodging resistance when compared to the leafed monoculture. For mycosphaerella blight and Uromyce viciae-fabae rust diseases, the 83:17 mixture decreased disease severity by 4% when compared with the leafed monoculture. Regression analysis of yield estimated that the 86:14 ratio provided an 11% increase to the leafed monoculture, but there was no increase compared with the semi-leafless monoculture. Mixing the two types in a high semi-leafless ratio can reduce leafed lodging and prevent yield loss but does not increase the overall yield over the semi-leafless monoculture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".