Affiliation:
1. Organization for Research Promotion, Osaka Metropolitan University, Osaka 599–8531, Japan
2. Graduate School of Agriculture, Osaka Metropolitan University, Osaka 599–8531, Japan
Abstract
Environmental control in greenhouse horticulture is essential for providing optimal conditions for plant growth and achieving greater productivity and quality. To develop appropriate environmental management practices for greenhouse horticulture through sensing technologies for monitoring the environmental stress responses of plants in real time, we evaluated the relative value of the stomatal opening to develop a technology that continuously monitors stomatal aperture to determine the moisture status of plants. When plants suffer from water stress, the stomatal conductance of leaves decreases, and transpiration and photosynthesis are suppressed. Therefore, monitoring stomatal behavior is important for controlling plant growth. In this study, a method for simply monitoring stomatal conductance was developed based on the heat balance method. The stomatal opening index (SOI) was derived from heat balance equations on intact tomato leaves, wet reference leaves, and dry reference leaves by measuring their temperatures in a growth chamber and a greenhouse. The SOI can be approximated as the ratio of the conductance of the intact leaf to the conductance of the wet reference leaf, which varies from 0 to 1. Leaf temperatures were measured with infrared thermometry. The theoretically and experimentally established SOI was verified with tomato plants grown hydroponically in a greenhouse. The SOI derived by this method was consistent with the leaf conductance measured via the porometer method, which is a standard method for evaluating actual leaf conductance that mainly consists of stomatal conductance. In conclusion, the SOI for the continuous monitoring of stomatal behavior will be useful not only for studies on interactions between plants and the environment but also for environmental management, such as watering at plant production sites.
Reference33 articles.
1. Kozai, T. (2018). Smart Plant Factory: The Next Generation Indoor Vertical Farms, Springer.
2. Gao, Z., Luo, Z., Zhang, W., Lv, Z., and Xu, Y. (2020). Deep learning application in plant stress imaging: A review. AgriEngineering, 2.
3. Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications;Zhou;Comput. Electron. Agric.,2020
4. Greenhouse environment control technologies for improving the sustainability of food production;Kozai;Acta Hortic.,2014
5. A new low-cost portable multispectral optical device for precise plant status assessment;Tagarakis;Comput. Electron. Agric.,2019