Study On Greenhouse Environment Management System Based On Tomato Growth Model

Development of protected agriculture is a main direction of the modern agriculture in future and while improving greenhouse environment control is the key technology of greenhouse production. At present, the management of greenhouse environment usually depends an the analysis of history data to make decision with poor real time characteristics and at the same time, the referred crop model is just simple in support function. To improve the performance of the greenhouse environment management system and to fetch dynamic information about decision-making in time, a greenhouse environment management system based on crop model was put forward in this paper with tomato as study object and temperature as control target. The main contents and results of this study were as follows:1) A greenhouse tomato growth model was established and relevant parameters were determined based on advanced research achievements from abroad and homeland as well as through experiment results.The model was made suitable for decision-making by reconstruction and was validated with follow-up experiments. This model includes submodels of growth period, photosynthesis, respiration, dry matter production and dry matter partitioning. The results showed that the model could output good simulation for tomato growth and development.2) Parameters were optimized in the greenhouse environment control based on crop model. The proper temperature domain was figured out under constrain of dry matter production within a certain percentage of the maximum value through the crop model simulation. Compared with the real-time data, the nearest value in the range would be the dynamic optimal temperature. According this value, more proper suggestion could be given for the greenhouse control and management.3) Method of greenhouse control based on crop model was discussed and the support function of crop model was extended. Through cumulated analysis and rule reasoning of radiation data, stress forecast based on illumination was made. According to the previous optimization algorithms and experiential knowledge the decision was made dynamically considering the change trend of temperature. In this way, the decision support information would be more scientific and suitable. By analyzing the differences between real and max results of diurnal dry matter production simulation, Diagnosis based on crop model could be provided to facilitate users finding laws and accumulating experiences.4) The management function of the system could be executed more timely and targeted by making use of real-time data acquired dynamically. Greenhouse control was optimized through integrating the technology of model prediction and knowledge reasoning and a decision support system for greenhouse environment management was developed with Visual Basic 6.0 as programming tool and access 6.0 5.0?? as database tool. The system offers several functions such as dynamic simulation of crop growth, decision support of environment management, common technology inquiry, etc. It could help producers with greenhouse environment optimized control and product management all-round on small time scale.To a great extend, the reliability of greenhouse environment control based on crop model depends on the accuracy of crop model itself. Because of the limitation of experimental condition and time period, the crop model in this paper should be refined and validated further. Besides, the system itself should be tested and developed through practice and application.