基于卷积神经网络的植物叶片分类

计算机与现代化

基于卷积神经网络的植物叶片分类

厦门大学信息科学与技术学院，福建厦门361005

收稿日期:2014-02-28 出版日期:2014-04-17 发布日期:2014-04-23
作者简介::龚丁禧(1988)，男，湖南郴州人，厦门大学信息科学与技术学院硕士研究生，研究方向：图像处理，机器学习；曹长荣(1987)，男，硕士研究生，研究方向：图像处理，机器学习。

Plant Leaf Classification Based on CNN

School of Information Science and Technology， Xiamen University， Xiamen 361005， China

Received:2014-02-28 Online:2014-04-17 Published:2014-04-23

摘要/Abstract

摘要： 回顾近年来国内外植物叶片分类的研究进展，指出传统方法存在的缺陷。简述卷积神经网络在图像分类的优势，为了简单高效地对植物叶片进行识别，提出一种基于卷积神经网络(Convolutional Neural Network, CNN)的植物叶片识别方法。在Swedish叶片数据集上的实验结果表明，本算法识别正确率高达99.56%，显著优于传统的叶片识别算法。

关键词: 植物叶片分类, 卷积神经网络, 深度学习, 神经网络, 特征图

Abstract:

Plant plays an important role in human life, so it is necessary to build an automatic system for recognizing plant. Plant leaf classification has become a research focus for twenty years. However, conventional methods for recognizing plant leaf have various drawbacks. CNN gained great success in image recognition, in order to utilize CNN to recognize plant leaf, a hierarchical model based on convolutional neural network is proposed. We applied our method to Swedish leaf dataset classification, the experimental results showed that the proposed method is quite effective and feasible.

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Key words:

Key words: plant leaf classification； convolutional neural network； deep learning, neural network； feature map

中图分类号:

TP391

龚丁禧，曹长荣. 基于卷积神经网络的植物叶片分类[J]. 计算机与现代化.

GONG Dingxi, CAO Changrong. Plant Leaf Classification Based on CNN[J]. Computer and Modernization.

参考文献

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