Visually assessing maize leaves: from spectral sampling to high-fidelity color reproduction
Bibliographic record
Abstract
Maize (Zea mays L., corn) crops are extensively used in food and biofuel production worldwide. A number of protocols have been proposed to use leaf color as an indicator of the health status of maize plants. Color perception is a complex process, however. The correct interpretation of its outcomes depends on several aspects. Accordingly, a variety of spectral vegetation indices have also been proposed to monitor the development of these plants. These indices usually require a number of spectral reflectance and transmittance samples taken from selected specimens using specialized sensors. Since these radiometric quantities do not depend neither on the spectra of the light sources nor on the physiological characteristics of the human visual system, these indices are not subject to color perception issues. The visual feedback provided by the chromatic attributes of plant leaves, on the other hand, can enable a broader assessment of the net effect of several environmental factors affecting an entire maize crop. Also, these attributes can be obtained using spectral reflectance and transmittance samples already employed in the computation of the aforementioned indices. These aspects indicate the potential benefits of the combined use of vegetation indices and leaf chromatic attributes in the monitoring of maize crops. Ideally, one would like to employ a number of spectral samples that would maximize the color fidelity to sensor costs ratio. In this paper, we address this practical trade-off. More specifically, using hyperspectral reflectance and transmittance data for maize specimens, we performed colorimetric experiments to obtain a lower bound for the number of spectral reflectance and transmittance samples sufficient to achieve a high degree of fidelity in the reproduction of maize leaves’ colors under distinct illumination 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".