Research And Application Of Vegetable Growth Cycle Prediction Model Base On Environment On The Agricultural-Cloud Service Platform

The new idea of "Internet+" has brought unprecedented growth to China's economic development.The emerging topic of "Internet + Agriculture" has also found a new development path for the rapid development of China's agriculture.Therefore,agricultural service platform has become one of the hot researches on agricultural informationization in recent years.This paper mainly studies the construction of vegetable growth cycle prediction model under the Agricultural-Cloud server platform to provide guarantee for the integration of vegetable production,supply and marketing.This paper fully considers the production demand,combined with the knowledge of vegetable growth theory and previous research,proposes that the temperature and illumination affecting the growth of vegetables in the environment are the main influencing factors.Considering the generalization of the later stage,the photosynthetic effective radiation and light intensity data were actually fitted,and the light intensity was used as the model input factor selection.The effective cumulative temperature method AE-TLU for vegetable growth based on ambient temperature and illumination intensity was studied and established.This paper constructs an environment-based vegetable growth cycle prediction model and applies the vegetable planting data to test the prediction accuracy and practicability of the model.After prediction accuracy and practicability of the model verified by real data of vegetable planting,it turns out that model's prediction error on vegetable growth cycle can be guaranteed to be less than two natural days.It is proved that the theoretical method of the model can be applied to a variety of common vegetables,and has strong economic practicability and promotion value.Using Python language,Django framework to develop a vegetable growth cycle prediction model application platform with vegetable growth cycle prediction,vegetable information management,environmental information management and data visualization functions.Finally,the prediction model is applied to the platform.and meet the needs of users in practice,which has practicality,innovation and application prospects.