基于深度卷积神经网络的储粮害虫图像识别

doi:10.11924/j.issn.1000-6850.casb16110146

中国农学通报 ›› 2018, Vol. 34 ›› Issue (1): 154-158.doi: 10.11924/j.issn.1000-6850.casb16110146

基于深度卷积神经网络的储粮害虫图像识别

程曦,吴云志,张友华,乐毅

安徽农业大学信息与计算机学院,安徽农业大学信息与计算机学院,安徽农业大学信息与计算机学院,安徽农业大学信息与计算机学院

收稿日期:2016-11-28 修回日期:2017-06-10 接受日期:2017-06-14 出版日期:2018-01-12 发布日期:2018-01-12
通讯作者: 乐毅
基金资助:
安徽省自然科学基金项目“基于基因表达式编程的作物生长建模方法研究”(1508085MF110)；茶树生物学与资源利用国家重点实验室开放基金“基于茶树转录组装优化后的中国变种与阿萨姆变种SSR与SNP的特征比较研究”(SKLTOF20150103)。

Image Recognition of Stored Grain Pests: Based on Deep Convolutional Neural Network

Received:2016-11-28 Revised:2017-06-10 Accepted:2017-06-14 Online:2018-01-12 Published:2018-01-12

摘要/Abstract

摘要： [目的]为了防治储粮害虫带来的危害,借助计算机对储粮害虫进行有效的图像识别是具有重要意义的。[方法]针对基于图像的储粮害虫多分类识别问题,引入了基于深度卷积神经网络的储粮害虫图像识别方法。该方法与传统的储粮害虫识别方法相比,大幅度简化了数据预处理过程,[结果]同时该方法在识别精确度方面达到了97.61%,也明显优于传统方法。[结论]因此基于深度卷积神经网络的储粮害虫识别方法具有较高的实用性,且具有进一步研究和推广的意义。

关键词: 骨架增长, 骨架增长, 生长板, 软骨内成骨, 膜内成骨, 生长轴

Abstract: In order to prevent and control the damage caused by stored grain pests, it is of great significance to find an effective way of recognizing stored grain pests with the help of computer. Focusing on the problems of multi-class recognition of stored grain pests based on images, a new image recognition method of stored grain pests based on deep convolutional neural network was proposed. Compared with the traditional ways of stored grain pests recognition, this new method greatly simplified the data preprocessing process, and the accuracy of this new method achieved 97.61%, which was significantly superior to the traditional methods. Therefore, the method of recognizing stored grain pests based on deep convolutional neural network has high practicability as well as the significance of further research and promotion.

中图分类号:

TP391.41

程曦,吴云志,张友华,乐毅. 基于深度卷积神经网络的储粮害虫图像识别[J]. 中国农学通报, 2018, 34(1): 154-158.

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基于深度卷积神经网络的储粮害虫图像识别

Image Recognition of Stored Grain Pests: Based on Deep Convolutional Neural Network

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