Electrophysiological monitoring of plants: an exploratory study on drought stress
Bibliographic record
Abstract
Abstract Climate change is increasing environmental stress, particularly rising temperatures and water scarcity, in both natural and human-managed systems such as agroecosystems and urban environments. Traditional methods for monitoring plant health in human-managed systems remain limited, underscoring the need for novel approaches. This study explores the potential of plant electrophysiological signals (EPS) and derived statistical features for the early detection of drought stress. The two main objectives of this research are: i) to identify EPS features that are both ecologically relevant and statistically robust for detecting drought stress, and ii) to develop statistical models that integrate these features. EPS data was collected from two drought-stress experiments, one on tomato plants and one on apricot trees. Sixteen features from both time and frequency domains were selected and evaluated. Two models, a logistic and a machine learning classifier, were developed and compared using accuracy, precision, and recall metrics. In apricots, ten time-domain features (Frequency Center, Generalized Hurst Exponent, Hjorth Complexity, Hjorth Mobility, Kurtosis, Root Mean Squared Frequency, Root Variance Frequency, Shape Factor, Skewness and Standard Deviation) showed significant differences between stressed and control groups. In tomatoes, four frequency-domain features (Frequency Centre, Root Variance Frequency, Root Mean Squared Frequency, and Power Law Distribution Exponent) were significantly different. Model accuracy was approximately 50% for apricots and 66% for tomatoes, insufficient for practical deployment but indicative of potential. This study illustrates the potential value of plant EPS data, its derived statistical features, and models for developing early drought stress detection systems in both agricultural and urban plant management contexts.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".