Soft computing technology for modeling of greenhouse climate control

The objective of this paper is present a reasonable system model to set greenhouse daytime optimal temperature thereby to achieve the most net profit. In order to set an optimal temperature point in a greenhouse, it is essential to construct plants growth model and calculate the cost of modifying environment. In this paper a soft computing system for greenhouse temperature setting has been developed and integrated. It includes three parts. One is an algorithm depending on the energy consumption of each component of heating and ventilation equipment according to two reasonable formulae. The other is a neural network for forecast the photosynthesis rate of tomato according to light intensity, temperature, CO 2 concentration, and LAI. The sample data rooted in TOMGRO. The last part is a GA for searching an optimal temperature setting point in daytime.