Customized Shape Detection Algorithms for Radiometric Calibration of Multispectral Imagers for Precision Agriculture Applications
Bibliographic record
Abstract
Dual panel relative radiometric calibration is an important tool for multispectral imagers mounted on UAV's for small farm precision agriculture. A customized dual panel detection technique integrated into the multispectral calibration routine is developed in this work. Otsu segmentation was the most precise method to find square reflectance panels with a controlled background. Canny edge detection proved less noisy than Laplacian of Gaussian filters and more robust to environmental changes than Otsu's method. More post processing on the images was required inside of edge detection algorithms, as the zero crossing edge detection methods amplified noise inside the image. Both hole filling algorithms and morphological filters were employed to reduce the noise. Morphological erosion filters caused under segmented images. This resulted in the desired low false positive rates, and negative volume similarity metrics for the regions of interest. A fast and reliable dual reflection panel detection technique was implemented for radiometric calibration for small farm monitoring where time is of the essence.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".