Name
Abstract | ||
|---|---|---|
The electrical activity signals in plants can provide useful information to monitor environmental conditions, such as atmospheric pollution. Nonetheless the study of the relationship between environmental stimuli and electrical responses of plants is still a critical step in developing technologies that use plants as organic sensing devices. In this paper an automatic method of analysis of plant electrical signals for ozone critical levels detection is proposed, based on the fundamentals of correlation theory. In order to classify the morphology characteristics of plant response to ozone exposure we used a segmentation of time series measurements of the electrical activity of plants before, during and after the stimulation. Then, we extracted the significant deviations from the baseline trend to detect and identify the response to a known stimulus, in terms of correlation coefficient. As a result, the proposed detection algorithm represents a novel monitoring method for detecting critical levels of ozone concentrations. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | Plant electrical signal,ozone pollution,spike detection,waveform correlation,data classification |
Field | DocType | ISSN |
Correlation coefficient,Signal,Ozone,Remote sensing,Ozone exposure,Pollution,Environmental science,Atmospheric pollution,Air pollution,Correlation theory | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Dolfi | 1 | 8 | 1.17 |
| I. Colzi | 2 | 1 | 0.70 |
| Simone Morosi | 3 | 105 | 25.84 |
| E. Masi | 4 | 5 | 1.87 |
| S. Mancuso | 5 | 9 | 3.59 |
| Enrico Del Re | 6 | 212 | 33.24 |
| F. Francini | 7 | 0 | 0.34 |
| R. Magliacani | 8 | 0 | 0.34 |