基于轻量级卷积神经网络的植物叶片病害识别方法

doi:10.11772/j.issn.1001-9081.2020091471

计算机应用 ›› 2021, Vol. 41 ›› Issue (6): 1812-1819.DOI: 10.11772/j.issn.1001-9081.2020091471

所属专题：前沿与综合应用

基于轻量级卷积神经网络的植物叶片病害识别方法

贾鹤鸣^1,2, 郎春博³, 姜子超⁴

1. 三明学院信息工程学院, 福建三明 365004;
2. 福建省农业物联网应用重点实验室(三明学院), 福建三明 365004;
3. 西北工业大学自动化学院, 西安 710129;
4. 东北林业大学机电工程学院, 哈尔滨 150040

收稿日期:2020-09-21 修回日期:2020-11-28 出版日期:2021-06-10 发布日期:2021-01-21
通讯作者: 郎春博
作者简介:贾鹤鸣(1983-),男,辽宁辽阳人,教授,博士,CCF会员,主要研究方向:数字图像处理、模式识别;郎春博(1998-),男,辽宁沈阳人,博士研究生,主要研究方向:图形图像处理、计算机视觉;姜子超(1995-),男,黑龙江齐齐哈尔人,硕士研究生,主要研究方向:机器学习、图像分割。
基金资助:
中央高校基本科研业务费专项资金资助项目（2572019BF04）。

Plant leaf disease recognition method based on lightweight convolutional neural network

JIA Heming^1,2, LANG Chunbo³, JIANG Zichao⁴

1. School of Information Engineering, Sanming University, Sanming Fujian 365004, China;
2. Fujian Key Lab of Agriculture IOT Application(Sanming University), Sanming Fujian 365004, China;
3. School of Automation, Northwestern Polytechnical University, Xi'an Shaanxi 710129, China;
4. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China

Received:2020-09-21 Revised:2020-11-28 Online:2021-06-10 Published:2021-01-21
Supported by:
This work is partially supported by the Fundamental Research Funds for the Central Universities (2572019BF04).

摘要/Abstract

摘要： 针对目前农业信息领域植物病害识别精度较低、实时性较差的问题，提出了一种基于轻量级卷积神经网络（CNN）的植物叶片病害识别方法。在原有网络中引入深度可分离卷积（DSC）和全局平均池化（GAP）方法，分别用来代替标准卷积运算操作并对网络末端的全连接层部分进行替换。同时，批归一化的技巧也被运用到训练网络的过程中，以改善中间层数据分布并提高收敛速度。为全面而可靠地评估所提方法的性能，在公开的植物叶片病害图像数据集PlantVillage上进行实验，选取损失函数收敛曲线、测试精度、参数内存需求等指标来验证改进策略的有效性。实验结果表明，改进后的网络具有较高的病害识别精度（99.427%）以及较小的内存空间占用（6.47 MB），可见其与其他基于神经网络的叶片识别技术相比具有优势，工程实用性较强。

关键词: 卷积神经网络, 植物叶片病害, 图像分类, 深度可分离卷积, 全局平均池化

Abstract: Aiming at the problems of low accuracy and poor real-time performance of plant leaf disease recognition in the field of agricultural information, a plant leaf disease recognition method based on lightweight Convolutional Neural Network (CNN) was proposed. The Depthwise Separable Convolution (DSC) and Global Average Pooling (GAP) methods were introduced in the original network to replace the standard convolution operation and the fully connected layer part at the end of the network respectively. At the same time, the technique of batch normalization was also applied to the process of training network to improve the intermediate layer data distribution and increase the convergence speed. In order to comprehensively and reliably evaluate the performance of the proposed method, experiments were conducted on the open plant leaf disease image dataset PlantVillage, and loss function convergence curve, test accuracy, parameter memory demand and other indicators were selected to verify the effectiveness of the improved strategy. Experimental results show that the improved network has higher disease recognition accuracy (99.427%) and smaller memory space occupation (6.47 MB), showing that it is superior to other leaf recognition technologies based on neural network, and has strong engineering practicability.

Key words: Convolutional Neural Network (CNN), plant leaf disease, image classification, Depthwise Separable Convolution (DSC), Global Average Pooling (GAP)

中图分类号:

TP391.4

贾鹤鸣, 郎春博, 姜子超. 基于轻量级卷积神经网络的植物叶片病害识别方法[J]. 计算机应用, 2021, 41(6): 1812-1819.

JIA Heming, LANG Chunbo, JIANG Zichao. Plant leaf disease recognition method based on lightweight convolutional neural network[J]. Journal of Computer Applications, 2021, 41(6): 1812-1819.

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基于轻量级卷积神经网络的植物叶片病害识别方法

Plant leaf disease recognition method based on lightweight convolutional neural network

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