基于BP神经网络模型的果实蝇自动分类系统

果实蝇属昆虫危害水果和蔬菜,造成产量下降,影响对外贸易,对其识别是检疫工作中的重要部分,现有的人工辨识方法受时间、知识等因素影响不能准确、有效辨识。提出了一种基于BP神经网络模型的果实蝇分类方法,采用几何形态测量学中的标记点法对果蝇翅进行特征提取,通过方差分析确定了用于果蝇鉴定的11个主特征,建立3层BP神经网络模型,结合Levenberg-Marquardt BP训练函数对数据集进行训练,得到完整的可用于果实蝇分类的BP神经网络。实验表明,该方法能够对实蝇进行有效的辨识,对桔小实蝇、瓜实蝇、具条实蝇和南亚果实蝇等高风险果实蝇辨识的准确率分别是90.0%、93.3%、90.0%和96.7%,总体准确率为92.5%,具有良好的应用前景。

A Study on the Automatic Classification System for Fruit Flies Based on BP Neural Network Model

Fruit fly genus insects hazard fruits and vegetables,resulting in production decline and under-mine foreign trade.Its recognition is an important part of the quarantine work.Artificial identification methods restricted by factors of time,knowledge,etc,cannot accurately and effectively identify the insects.This paper in-troduces a system for fruit fly classification based on a BP neural network model,using geometric morphometric method in marker on Drosophila extracts the wing features,identifies 11 main features by analysis of variance, and establishes the three layer BP neural network model. Combined with Levenberg-Marquardt BP training function the data sets were trained to give a complete BP neural network,which can be used to classify the fruit fly. Experiments show that the method can effectively identify fruit flies,melon fly,stripped fly and the accuracy of identification are 90.0%,93.3%,90.0% and 96.7%,respectively the overall accuracy is 92.5%.This method hsa a good prospect.