Yunjeong Yang1, Ji Eun Kim1, Hak Jin Song1, Eun Bin Lee1, Yong-Keun Choi1, Jeong Wook Jo1, Hyeon Jin Jeon1, Ho Hyun Kim2, Kwang Jin Kim3, Hyung Joo Kim4. 1. Department of Biological Engineering, Konkuk University, Seoul, 05029, Republic of Korea. 2. Department of Integrated Environmental Systems, Pyeongtaek University, Pyeongtaek, 17869, Republic of Korea. 3. Urban Agriculture Research Division, National Institute of Horticultural and Herbal Science, Chungjoo, 54875, Republic of Korea. 4. Department of Biological Engineering, Konkuk University, Seoul, 05029, Republic of Korea. hyungkim@konkuk.ac.kr.
Abstract
BACKGROUND: Water content variation during plant growth is one of the most important monitoring parameters in plant studies. Conventional parameters (such as dry weight) are unreliable; thus, the development of rapid, accurate methods that will allow the monitoring of water content variation in live plants is necessary. In this study, we aimed to develop a non-invasive, radiofrequency-based monitoring system to rapidly and accurately detect water content variation in live plants. The changes in standing wave ratio (SWR) caused by the presence of stem water and magnetic particles in the stem water flow were used as the basis of plant monitoring systems. RESULTS: The SWR of a coil probe was used to develop a non-invasive monitoring system to detect water content variation in live plants. When water was added to the live experimental plants with or without illumination under drought conditions, noticeable SWR changes at various frequencies were observed. When a fixed frequency (1.611 GHz) was applied to a single experimental plant (Radermachera sinica), a more comprehensive monitoring, such as water content variation within the plant and the effect of illumination on water content, was achieved. CONCLUSIONS: Our study demonstrated that the SWR of a coil probe could be used as a real-time, non-invasive, non-destructive parameter for detecting water content variation and practical vital activity in live plants. Our non-invasive monitoring method based on SWR may also be applied to various plant studies.
BACKGROUND:Water content variation during plant growth is one of the most important monitoring parameters in plant studies. Conventional parameters (such as dry weight) are unreliable; thus, the development of rapid, accurate methods that will allow the monitoring of water content variation in live plants is necessary. In this study, we aimed to develop a non-invasive, radiofrequency-based monitoring system to rapidly and accurately detect water content variation in live plants. The changes in standing wave ratio (SWR) caused by the presence of stem water and magnetic particles in the stem water flow were used as the basis of plant monitoring systems. RESULTS: The SWR of a coil probe was used to develop a non-invasive monitoring system to detect water content variation in live plants. When water was added to the live experimental plants with or without illumination under drought conditions, noticeable SWR changes at various frequencies were observed. When a fixed frequency (1.611 GHz) was applied to a single experimental plant (Radermachera sinica), a more comprehensive monitoring, such as water content variation within the plant and the effect of illumination on water content, was achieved. CONCLUSIONS: Our study demonstrated that the SWR of a coil probe could be used as a real-time, non-invasive, non-destructive parameter for detecting water content variation and practical vital activity in live plants. Our non-invasive monitoring method based on SWR may also be applied to various plant studies.
Entities:
Keywords:
Coil probe; Plant activity monitoring; Plant water content; Radermachera sinica; Standing wave ratio (SWR)
Authors: Rajkumar V Patil; Shouxi Xu; Alfred N van Hoek; Andrew Rusinko; Zixia Feng; Jesse May; Mark Hellberg; Najam A Sharif; Martin B Wax; Macarena Irigoyen; Grant Carr; Tom Brittain; Peter Brown; Damon Colbert; Sindhu Kumari; Kulandaiappan Varadaraj; Alok K Mitra Journal: Chem Biol Drug Des Date: 2016-01-17 Impact factor: 2.817