On the detection and monitoring of reduced water content in plants using spectral responses in the visible domain
Bibliographic record
Abstract
The water status of cultivated plants can have a significant impact not only on food production, but also on the appropriate usage of increasingly scarce freshwater supplies. Accordingly, the cost-effective detection and monitoring of changes in their water content are longstanding remote sensing goals. Existing procedures employed to achieve these goals are largely based on the spectral responses of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. Recently, it has been suggested that such procedures could be implemented using spectral responses, more specifically spectral subsurface reflectance to transmittance ratios, obtained in the visible domain. The basis for this proposition resides on the premise that a reduced water content (RWC) can result in histological changes whose effects on the foliar optical properties may not be limited to the infrared domain. However, the experiments leading to this proposition were performed on detached leaves, which were not influenced by the whole plant’s adaptation mechanisms to water stress. In this work, we investigate whether the spectral responses of living plant leaves in the visible domain can lead to reliable RWC estimations. We employ measured biophysical data and predictive light transport simulations in order to extend qualitatively and quantitatively the scope of previous studies in this area. Our findings indicate that the living specimens’ physiological responses to water stress should be taken into account in the design of new procedures for the cost-effective RWC estimation using visible subsurface reflectance to transmittance ratios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".