Open AccessA Review on Plant Stress Detection and Analysis Through Electrophysiological Signals
Author(s) -
Kavya Sai,
Neetu Sood,
Indu Saini
Publication year - 2021
Publication title -
aijr proceedings
Language(s) - English
Resource type - Conference proceedings
ISSN - 2582-3922
DOI - 10.21467/proceedings.114.22
Subject(s) - electrophysiology , signal (programming language) , signal processing , electroencephalography , computer science , stress (linguistics) , artificial intelligence , field (mathematics) , neurophysiology , pattern recognition (psychology) , machine learning , neuroscience , psychology , digital signal processing , mathematics , linguistics , philosophy , pure mathematics , computer hardware , programming language
The bioelectrical activity like ECG, EMG and EEG provides the health condition of heart, muscles, and brain in human beings. In plants, the sensible measurements of physical activity are in their infant phase. Substitution of technology used in biomedical field (human medicine) might consequently provide an understanding about electrophysiological signal activity in plants. These signals in plants when monitored show various dynamics in different stress conditions like osmotic, cold, low light, chemical, over watering etc. Several studies analysing and classifying features of ideal and stressed signal subtleties have shown promising results. In this paper we present a comprehensive review of research contributed to EPG signal analysis in different domains, applications of machine learning in plant stress detection and classification.