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基于轻量级残差网络的苹果叶病识别

周罕觅 陈佳庚 代智光 牛晓丽 秦龙 向友珍 赵龙

周罕觅,陈佳庚,代智光,等. 基于轻量级残差网络的苹果叶病识别 [J]. 福建农业学报,2024,39(1):83−92 doi: 10.19303/j.issn.1008-0384.2024.01.011
引用本文: 周罕觅,陈佳庚,代智光,等. 基于轻量级残差网络的苹果叶病识别 [J]. 福建农业学报,2024,39(1):83−92 doi: 10.19303/j.issn.1008-0384.2024.01.011
ZHOU H M, CHEN J G, DAI Z G, et al. Lightweight Residual Networks for Diagnosis of Apple Leaf Diseases [J]. Fujian Journal of Agricultural Sciences,2024,39(1):83−92 doi: 10.19303/j.issn.1008-0384.2024.01.011
Citation: ZHOU H M, CHEN J G, DAI Z G, et al. Lightweight Residual Networks for Diagnosis of Apple Leaf Diseases [J]. Fujian Journal of Agricultural Sciences,2024,39(1):83−92 doi: 10.19303/j.issn.1008-0384.2024.01.011

基于轻量级残差网络的苹果叶病识别

doi: 10.19303/j.issn.1008-0384.2024.01.011
基金项目: 国家自然科学基金项目(51909079、52069016);河南省科技攻关计划项目(232102110264);河南科技大学青年骨干教师项目(13450001)
详细信息
    作者简介:

    周罕觅(1986 —),男,博士,副教授,主要从事农业工程与信息技术研究,E-mail:zhouhm@163.com

    通讯作者:

    赵龙(1988 —),男,博士,讲师,主要从事农业工程与信息技术研究,E-mail:hkdzhaolong@163.com

  • 中图分类号: S661.1;S24

Lightweight Residual Networks for Diagnosis of Apple Leaf Diseases

  • 摘要:   目的  解决卷积神经网络在复杂环境下识别率低、模型参数多等问题,为苹果叶病智能识别提供参考。  方法  本研究提出一种基于改进ResNet18的苹果叶病识别模型。首先,通过离线增强和在线增强两种方式解决数据不平衡和数据过拟合现象,增强模型的泛化能力;其次,引入缩放因子调整通道参数以减少网络参数量,并在下采样残差结构的恒等映射中用最大池化层代替1×1卷积完成下采样,去除图片中的冗余特征,增大模型的感受野;将ResNet18模型的第一层7×7卷积层替换为多尺度特征提取模块,提高模型对细小病斑的提取能力;最后,在特征提取网络中插入DenseBlock模块,加强模型对浅层有效特征的重用。  结果  改进后的ResNet18模型准确率为97.94%,比原模型高出0.88个百分点;模型大小为3.97 MB,比原模型减小90.77%。与ShuffleNetv2、MobileNetv3、EfficientNet等轻量化模型和Inceptionv2、DenseNet、ResNet等经典模型相比,该模型拥有更好的性能。  结论  改进后的模型在复杂环境下能够准确识别苹果叶病,并且具有较少的模型参数,方便移植到移动设备上,为苹果叶病的智能诊断提供参考。
  • 图  1  数据集样本示例

    A:复杂背景下样本图像示例;B:简单背景下样本图像示例。

    Figure  1.  Sample data set

    A: Sample image on complex background; B: Sample image on simple background.

    图  2  下采样残差模块及改进

    a:原始的下采样残差模块;b:改进后的下采样残差模块。

    Figure  2.  Original and improved down-sampling residual modules

    a: Original down-sampling residual module; b: improved down-sampling residual module.

    图  3  1×1卷积下采样

    代表在卷积过程中参与计算; 代表在卷积过程中没有参与计算。

    Figure  3.  1×1 convolution down-sampling

    : Participated in calculation during convolution process; : not involved in calculation during convolution process.

    图  4  多尺度特征提取模块 (MFEM)

    CBR为Conv+BN+Relu。

    Figure  4.  Multi-scale feature extraction module (MFEM)

    CBR: Conv+BN+Relu.

    图  5  DenseBlock模块

    Figure  5.  DenseBlock module

    图  6  改进模型的总体结构

    Figure  6.  Overall structure of improved model

    图  7  加入多尺度特征提取模块准确率

    Figure  7.  Accuracy by adding multi-scale feature extraction module

    图  8  不同DenseBlock模块插入位置的模型准确率

    D1、D5、D9、D13分别代表在模型第1、5、9、13卷积层后加入DenseBlock模块。

    Figure  8.  Model accuracy with additions of DenseBlock module after convolutional layers

    D1, D5, D9, and D13 are models with additions of DenseBlock module after 1st, 5th, 9th, and 13th convolutional layers, respectively.

    图  9  苹果叶病类激活图

    Figure  9.  Activation map of apple leaf disease

    图  10  不同网络模型的混淆矩阵

    a、b、c、d和e分别代表为ResNet18、ShuffleNetv2、MobileNetv3、DenseNet121和本文模型的混淆矩阵图。

    Figure  10.  Confusion matrix for network models

    a, b, c, d, and e represent confusion matrix plots of ResNet18, ShuffleNetv2, MobileNetv3, DenseNet121, and improved model in this study, respectively.

    表  1  数据集样本数量

    Table  1.   Information on sample data set

    类别名称
    Class name
    复杂背景
    Complex background
    简单背景
    Simple background
    增强后 Augmented 总数
    Total
    训练集 Training set 验证集 Test set
    健康 Healthy 845 676 169 845
    斑点落叶病 Alternaria boltch 271 145 667 165 832
    褐斑病 Brown spot 33 379 659 165 824
    灰斑病 Grey spot 163 176 543 135 678
    花叶病 Mosaic 171 200 594 148 742
    白粉病 Powdery mildew 872 698 174 872
    黑星病 Scab 809 648 161 809
    锈病 Rush 715 572 143 715
    总数 Total 3879 900 5057 1260 6317
    下载: 导出CSV

    表  2  参数调整和残差改进的对比分析

    Table  2.   Comparative analysis on parameter adjustment and residual improvement

    模型
    Model
    模型大小
    Model size/MB
    参数量
    Parameters/M
    准确率
    Accuracy/%
    ResNet42.711.1897.06
    ResNet-α0.7524.06.2997.22
    ResNet-α0.510.72.8097.14
    ResNet-α0.252.730.7096.27
    ResNet-α0.25+Max2.730.7096.98
    “α0.75”的数字代表通道缩放因子取值;Max代表最大池化层改进的下采样残差模块;M是百万的缩写,表示为1×106
    Data on "α0.75" is channel scaling factor; Max represents down-sampling residual module of maximum pooling layer improvement; M represents 1×106.
    下载: 导出CSV

    表  3  消融试验结果

    Table  3.   Results on ablation experiments

    模型
    Model
    精确度
    P/%
    召回率
    R/%
    F1分数
    F1 score/%
    准确率
    Accuracy/%
    参数量
    Parameters/M
    模型大小
    Model size/MB
    Resnet1897.1297.0197.0697.0611.1842.7
    Resnet18-α0.2596.3496.2496.2996.270.702.73
    Resnet18-α0.25+Max96.9696.9996.9896.980.702.73
    Resnet18-α0.25+Max+MFEM97.5797.5597.5697.540.712.75
    Resnet18-α0.25+Max+MFEM+DenseBlock98.0097.9197.9597.941.013.94
    下载: 导出CSV

    表  4  不同模型对比

    Table  4.   Comparisons on models

    模型
    Model
    精确度
    P/%
    召回率
    R/%
    F1分数
    F1 score/%
    准确率
    Accuracy/%
    参数量
    Parameters/M
    模型大小
    Model size/MB
    ShuffleNetv294.6094.6494.6294.601.264.98
    GhostNet91.7191.7791.7491.803.8915.1
    MobileNetv393.1793.2093.1993.171.525.95
    EfficientNet95.5595.4295.4895.304.0215.6
    ResNet1897.1297.0197.0697.0611.1842.7
    AlexNet88.6988.7488.7188.6514.6055.6
    DenseNet12197.3197.2897.3097.226.9027.1
    Inceptionv294.5494.5994.5694.527.328.2
    Our model 98.0097.9197.9597.941.013.94
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-03
  • 修回日期:  2023-10-13
  • 刊出日期:  2024-01-28

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