ISHS Acta Horticulturae 1327: IV International Symposium on Horticulture in Europe - SHE2021
Integrating microweather forecasts and crop physiological indicators for greenhouse environmental control |
| Authors: | M.H. Yao, H.C. Wang, Y.W. Chiu, H.C. Hsieh, T.H. Chang, Y.J. Lee |
| Keywords: | greenhouse, grid, weather, tomato, Solanum lycopersicum 'Yu Nu', forecast |
| DOI: | 10.17660/ActaHortic.2021.1327.59 |
| Abstract:
Extreme weather events, especially strong winds and torrential rain, affect crop production. Greenhouses enable protective crop cultivation through control of the production environment and protection from pests and diseases. Improving cultivation efficiency and increasing farmers' income are crucial aims of horticultural production to improve people's quality of life. Tomato greenhouses in Taiwan require a large number of sensors to monitor the weather because their environmental control systems rely on real-time observed meteorological data. In this study, atmospheric gridded meteorological forecast data are used to infer the inner microweather of a greenhouse. We use a long short-term memory model to forecast inside temperature for the upcoming hour by using 5 h of temporal clues from outside data. The results demonstrate that our simulation model achieves 94.2% accuracy within an error bound of 3°C of the predicted temperature. Moreover, the control strategies are based on the physiological requirements of crops, and greenhouse microweather forecast data are applied as inputs for the crop photosynthesis model. This photosynthesis model, which uses a nonrectangular hyperbola and biochemical factors, could predict the hourly photosynthesis rate of tomato (Solanum lycopersicum 'Yu Nu') plants in the greenhouse. The simulated data and actual measurements of single-leaf photosynthesis rate were similar (R2=0.91). We combined a weather forecast technique with a greenhouse cultivation system to enhance the performance of an environmental control system, which integrates crop physiology and weather forecast data, to serve as a new control strategy for tomato greenhouse cultivation. In this way, it can provide farmers with a reference basis for greenhouse cultivation. | |
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