朴素贝叶斯算法在蝴蝶兰病害分类中的应用研究

摘要

蝴蝶兰属于热带气生兰，对生长环境的温湿度非常敏感，温湿度过高或过低不仅对蝴蝶兰生长不利而且易引发多种病害。为此，利用传感器采集的蝴蝶兰生长环境空气温湿度数据，运用朴素贝叶斯算法对其进行分类，以此判断蝴蝶兰的健康状况，达到病害分类的目的。实验表明，该方法的平均分类准确率达到81％，与其它统计学预报方法相比，具有简单、易行的优点。%Phalaenopsis is a type of tropical flower , which is very sensitive to temperature and humidity .It is bad for the growth of Phalaenopsis and causing a variety of diseases when the temperature and humidity is too high or too low .In this paper , the naive Bayesian algorithm is used to classify the diseases by data of temperature and humidity .Experiments show that the model prediction accuracy was 81%.Compared with other statistical forecasting methods , the forecast model is more simple and convenient .