An operational fluorescence system for crop assessment
Bibliographic record
Abstract
The development of precision farming requires new tools for plant nutritional stress monitoring. An operational fluorescence system has been designed for vegetation status mapping and stress detection at plant and field scale. The instrument gives relative values of fluorescence at different wavelengths induced by the two-excitation sources. Lightinduced fluorescence has demonstrated successful crop health monitoring and plant nutritional stress detection capabilities. The spectral response of the plants has first been measured with an hyperspectral imager using laser-induced fluorescence. A tabletop imaging fluorometer based on flash lamp technology has also been designed to study the spatial distribution of fluorescence on plant leaves. For field based non-imaging system, LED technology is used as light source to induce fluorescence of the plant. The operational fluorescence system is based on ultraviolet and blue LED to induce fluorescence. Four narrow fluorescence bands centered on 440, 520, 690 and 740nm are detected. The instrument design includes a modular approach for light source and detector. It can accommodate as many as four different light sources and six bands of fluorescence detection. As part of the design for field application, the instrument is compatible with a mobile platform equipped with a GPS and data acquisition system. The current system developed by Telops/GAAP is configured for potato crops fluorescence measurement but can easily be adapted for other crops. This new instrument offers an effective and affordable solution for precision farming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.021 | 0.008 |
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".