Stress Detection in Crops with Hyperspectral Remote Sensing and Physical Simulation Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Progress made on the detection of stress in heterog eneous crop canopies with hyperspectral remote sensing imagery is presented. High-spatial resolution multispectral remote sensing imagery was collected in 2002, 2003 and 2004 over vineyard and olive orchards in Spain. Imagery acquired with the Compact Airborne Spectrographic Imager (CASI) and the Reflective Optics System Imaging Spectrometer (ROSIS) in the visible and near infrared wavelengt h regions 400-950 nm at 1 m resolution, and with the Airborne Hyperspectral Scanner (AHS) in the reflective and thermal regions at 2 m resolution enabled the study of narrow-band vegetation indices and model simula tion for estimation of chlorophyll content for chlorosis det ection at the tree and vine level, as well as deriv ing thermal information function of the stress status. Ground d ata collection consisted of measurements of crown t ransmittance with a PCA LAI-2000 and geometrical measurements of crown projected area, height, crown cross-section, and biochemical constituents such as chlorophyll a+b and carotenoids, enabling the estimation of crown leaf area index, crown leaf density, biophysical variables related t o the crown intercepted radiation, such as crop yie ld and canopy fractional cover, as well as crop functioning throu gh chlorophyll content estimation. Leaf and canopy simulation models, such as PROSPECT, SAILH, FLIM, and rowMCRM were used and the scaling up methodology presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it