Identifying Useful Features in Multispectral Images with Deep Learning for Optimizing Wheat Yield Prediction
Bibliographic record
Abstract
Since unmanned aerial vehicles have been utilized in plant phenotyping, they have revolutionarily improved its accuracy. In this paper, we introduce a deep learning based approach for optimizing the yield prediction process of spring wheat (triticum aestivum), using multispectral images. We assessed both the temporal features to find the most valuable time to take images, as well as the contribution of spectral bands. We processed full stage multispectral images from four site-years (two sites during two years) of a wheat breeding project, and determined the prediction accuracy of the image-based predicted yields and compared them to the harvested yields taken in the field. The results compared the wheat images throughout the season and validated the most crucial flying times for acquiring images were at late-heading, late-flowering, dough-development, and harvesting stages. The two most useful colour-bands for yield prediction were red and red-edge. We found that removing these bands significantly decreased the prediction correctness. The results of this research could be a tool for the development of more efficient sensors and strategies for data collection in plant phenotyping.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".