Acta Horticulturae
Dayan, E., A.R.O., Besor Experimental Station, Mobile Post Negev 4, 85400, Israel
Dayan, J., Mechanical Engineering Department, Technion - Israel Institute of Technology, Haifa, 32000, Israel
Strassberg, Y., Mechanical Engineering Department, Technion - Israel Institute of Technology, Haifa, 32000, Israel
A simple model, based on energy and mass balances at several zones of the greenhouse and, which enables the prediction of the ventilation within a commercial greenhouse containing rose plants, is presented. The model takes into consideration the microclimate conditions within the greenhouse and the conditions of the environment. It can be updated and calibrated for any possible combinations of conditions generated in and around the specific structure by online measurements of transpiration, leaves temperature (which, alternatively, can be predicted by the model) and several air temperatures and humidities. The equations express the exposure of the different zones to the environments and the effects of the plants. Additional neural network model allows the prediction of the transpiration rates for a few days ahead if the weather remains unchanged. Employing intelligent control methods, the model can be utilized efficiently to modify irrigation, CO2 enrichment and manage energy within the greenhouse.
פותח על ידי קלירמאש פתרונות בע"מ -
הספר "אוצר וולקני"
אודות
תנאי שימוש
The prediction of ventilation rates in greenhouses containing rose crops
593
Dayan, E., A.R.O., Besor Experimental Station, Mobile Post Negev 4, 85400, Israel
Dayan, J., Mechanical Engineering Department, Technion - Israel Institute of Technology, Haifa, 32000, Israel
Strassberg, Y., Mechanical Engineering Department, Technion - Israel Institute of Technology, Haifa, 32000, Israel
The prediction of ventilation rates in greenhouses containing rose crops
A simple model, based on energy and mass balances at several zones of the greenhouse and, which enables the prediction of the ventilation within a commercial greenhouse containing rose plants, is presented. The model takes into consideration the microclimate conditions within the greenhouse and the conditions of the environment. It can be updated and calibrated for any possible combinations of conditions generated in and around the specific structure by online measurements of transpiration, leaves temperature (which, alternatively, can be predicted by the model) and several air temperatures and humidities. The equations express the exposure of the different zones to the environments and the effects of the plants. Additional neural network model allows the prediction of the transpiration rates for a few days ahead if the weather remains unchanged. Employing intelligent control methods, the model can be utilized efficiently to modify irrigation, CO2 enrichment and manage energy within the greenhouse.
Scientific Publication