[1]孟亮,郭小燕,杜佳举,等.一种轻量级CNN农作物病害图像识别模型[J].江苏农业学报,2021,(05):1143-1150.[doi:doi:10.3969/j.issn.1000-4440.2021.05.008]
 MENG Liang,GUO Xiao-yan,DU Jia-ju,et al.A lightweight CNN model for image recognition of crop disease[J].,2021,(05):1143-1150.[doi:doi:10.3969/j.issn.1000-4440.2021.05.008]
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一种轻量级CNN农作物病害图像识别模型()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年05期
页码:
1143-1150
栏目:
植物保护
出版日期:
2021-10-30

文章信息/Info

Title:
A lightweight CNN model for image recognition of crop disease
作者:
孟亮郭小燕杜佳举沈航驰胡彬
(甘肃农业大学信息科学技术学院,甘肃兰州730070)
Author(s):
MENG LiangGUO Xiao-yanDU Jia-juSHEN Hang-chiHU Bin
(School of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China)
关键词:
CNN轻量级农作物病害识别准确率
Keywords:
convolutional neural network (CNN)lightweightcropsdiseasesrecognition accuracy
分类号:
TP391.41
DOI:
doi:10.3969/j.issn.1000-4440.2021.05.008
文献标志码:
A
摘要:
针对传统CNN (Convolutional neural network)模型存在训练参数量大而无法应用于硬件条件受限的场合这一问题,本研究提出一种轻量级CNN农作物病害识别模型,能够在保证模型识别准确率情况下简化模型结构,扩大模型的适用场景。设计1个深度卷积模块作为基本卷积单元,2个深度卷积模块和1个批归一化层组成1个残差块作为残差单元,以残差单元作为基本元素设计一个轻量级CNN农作物病害识别模型。对辣椒、番茄和马铃薯的病害图像进行分类识别,最终模型在训练集上的总识别准确率为99.33%,测试集上的总识别准确率为98.32%。相对VGG16等传统模型,在进行农作物病害识别时本模型有更高的识别准确率、更快的识别速度和更小的内存占用。
Abstract:
The traditional convolutional neural network (CNN) model cannot be applied to occasions with limited hardware conditions due to the large amount of training parameters. This research proposed a lightweight CNN model for crop disease recognition, which could simplify the model structure and expand the applicable scenarios under the condition of ensuring the accuracy of model recognition. A deep convolution module was designed as the basic convolution unit, and two deep convolution modules and a batch normalization layer were used as the residual units. A lightweight CNN model for crop disease recognition was designed with the residual unit as the basic element. The total recognition accuracy of the model on the training set was 99.33%, and the total recognition accuracy on the test set was 98.32%. Compared with the traditional models such as VGG16, the lightweight CNN model has higher recognition accuracy, faster recognition speed and less memory occupation.

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备注/Memo

备注/Memo:
收稿日期:2021-01-10基金项目:甘肃省自然科学基金项目(20JR5RA023);甘肃农业大学青年导师基金项目(QAU-QDFC-2019-04)作者简介:孟亮(1995-),男,江苏淮安人,硕士研究生,研究方向为图像识别、目标检测。(E-mail)mengl@st.gsau.edu.cn通讯作者:郭小燕,(E-mail)guoxy@gsau.edu.cn
更新日期/Last Update: 2021-11-09