Phenotyping plants by vibrations
Bibliographic record
Abstract
Plant vibrations have been studied for many purposes, both at the whole plant scale or at the local organ scale, see for instance in wind induced physical response of trees or crops [1], insect communication through vibration [2] or thigmomorphogenesis [3] . The experimental measurement of vibrational characteristics of plants, such as the frequencies of free motion, has considerably improved in the past ten years thanks to video motion capture techniques and signal processing . Recent tests on Poplar and Arabidopsis showed that these frequencies revealed important information combining the architecture, the geometry and the local mechanical properties of the plant [4] . This led to the idea of using vibrations for phenotyping plants, as a non-destructive, non-contact and fast method. We have developed a way to obtain quickly (less than a minute) a large quantity of information on the dynamics of a plant, more precisely on its dominant vibration modes. The method is based on an excitation by air pulses, a high speed video recording of the motion, and quasi real time signal processing of images to derive the dominant frequency of the captured motion. Tests on water stressed tobacco, on several mutants of Arabidopis thaliana and on mechanically stressed poplar, showed the robustness and performance of the method. More recently an advanced signal processing has been developed that allows extracting several frequencies present simultaneously in the motion, for instance those of leaves. This was tested on poplar and oak foliage. The method is currently under implementation in automatic phenotyping facilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".