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In the coast regions of China, strong wind activities such as typhoon are frequent. Typhoon is a major threat to greenhouses. During the typhoon periods, energy level of the winds is usually erratic, sometimes weak and sometimes strong. The nave approach of controlling greenhouse wind protection shutters according to the current wind speed will fail due to multivariate factors that affect wind speed. In this paper, we study the dynamic properties that cause the nonlinear periodic change of wind speed. A Multi-Layer Perceptron Neural Network model is designed to automatically acquire a classification model for real-time strong wind detection, using raw sensory reading of wind speed as training data. This work provides an approach for the unification of agricultural production and management control. Results show that this system is sufficient for the strong wind protection requirement of the south china coastal regions. Experiments also demonstrate that the system is robust against erratic strong wind behaviors that are frequent under real typhoon conditions.