Improvement of photosynthetic rate evaluation by plant bioelectric potential using illuminating information and a neural network
Ki Ando, Hiroshi Igarashi, Hiroyuki Shinoda, Nobuki Mutsukura

TL;DR
This paper presents a neural network-based method that incorporates illuminating information and plant bioelectric potential to accurately evaluate the photosynthetic rate in real-time, noninvasively.
Contribution
It introduces a neural network model that combines bioelectric potential and illumination parameters to improve photosynthetic rate estimation accuracy.
Findings
Correlation coefficient of 0.95 between actual and estimated photosynthetic rates
Bioelectric potential response enhances estimation accuracy over illumination parameters alone
Illuminating color variations affect bioelectric response and evaluation accuracy
Abstract
The plant bioelectric potential is believed to be a suitable real-time and noninvasive method that can be used to evaluate plant activities, such as the photosynthetic reaction. The amplitude of the bioelectric potential response when plants are illuminated is correlated with the photosynthetic rate. However, practically, the bioelectric potential is affected by various cultivation parameters. This study analyzes the relationship between the bioelectric potential response and the illuminating parameters using a neural network to improve the accuracy of the photosynthetic rate evaluation. The variation of the illuminating colors to the plant affected the relationship between the amplitude of the bioelectric potential response and the photosynthetic rate; therefore, evaluating the photosynthetic rate using the amplitude is difficult. The analysis result shows that the correlation…
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Taxonomy
TopicsPlant and Biological Electrophysiology Studies · Light effects on plants · Greenhouse Technology and Climate Control
