基于环境因子的黄瓜病害预测研究

日光温室中病害的预测预报是病害管理的重要组成部分,也是有效防治和控制病害发生发展的依据,更是农业生产管理和决策的前提。植物生长发育进程明显受到温度的影响,而病害发生又与长时间高温度和高湿度相联系。以往的病害预测多是基于对植物本身的病害特征、种植方法和农药的用量来实现的,多是如何防止。因此,病害早期的预测、预报和防治显得非常重要。为此,根据已有的专家知识库建立以环境数据(即温度和湿度)为输入的神经网络病害预测模型,通过此模型用实际的环境数据再预测未来病害,从而减少病害发生的概率,获得速生高产与优质高效的农产品,实现经济效益最优的目标。

Research on Cucumber Disease Prediction Based on the Environmental Factors

The prediction of disease is important part of disease management in the greenhouse,is the basis for disease occurrence and development of effective prevention and control also,and is the premise for the agricultural production management and decision.The plant growth process clearly is influenced by the temperature,while the disease occurrence is associated with high temperature、high humidity and long time.So the previous disease forecast researches are mostly based on the disease characteristics,planting methods and the amounts of pesticide,which is mainly to realize how to prevent.According to the established expert knowledge,the neural network models of disease prediction based on environmental data that temperature and humidity are as input are established in this paper.The goals that is to reduce the probability of occurrence of disease,fast-growing high-yield,high-quality,efficient agricultural products and the optimal economic benefit are to be achieved.