Wearable Volatile Organic Compound Sensors for Plant Health Monitoring
Bibliographic record
Abstract
Abstract Volatile organic compounds (VOCs) are utilized as essential biomarkers for plant health and the surrounding environmental conditions in light of global imperatives surrounding food security and sustainable agriculture. However, conventional VOC detection methods have inherent limitations related to operational costs, portability, in situ monitoring, and accessibility. Wearable electronic systems have garnered significant attention as an alternative method because of their capability to detect, identify, and quantify VOCs quickly and cost‐effectively. This article presents a comprehensive perspective of recently developed wearable VOC monitoring sensors. It highlights various detection methods for VOCs related to plant metabolism, hormones, and environmental conditions and then multi‐VOC sensing based on data‐driven analysis. Emerging wearable sensor devices are comprehensively examined from the perspectives of material, structural, sensing mechanisms, and plant monitoring demonstration. The principal issues inherent in recently developed VOC monitoring techniques are discussed, and potential avenues for future research and development are identified.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".