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融合SKNet与MobilenetV3的芒果叶片病虫害分类方法

沈熠辉 何惠彬 陈小宇 颜胜男

沈熠辉,何惠彬,陈小宇,等. 融合SKNet与MobilenetV3的芒果叶片病虫害分类方法 [J]. 福建农业学报,2024,39(5):1−9
引用本文: 沈熠辉,何惠彬,陈小宇,等. 融合SKNet与MobilenetV3的芒果叶片病虫害分类方法 [J]. 福建农业学报,2024,39(5):1−9
SHEN Y H, HE H B, CHEN X Y, et al. Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations [J]. Fujian Journal of Agricultural Sciences,2024,39(5):1−9
Citation: SHEN Y H, HE H B, CHEN X Y, et al. Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations [J]. Fujian Journal of Agricultural Sciences,2024,39(5):1−9

融合SKNet与MobilenetV3的芒果叶片病虫害分类方法

基金项目: 福建省技术创新重点攻关及产业化项目(2023G015)
详细信息
    作者简介:

    沈熠辉(1991 —),男,硕士,助教,主要从事人工智能应用、农业数据处理研究,E-mail:1723378551@qq.com

  • 中图分类号: TQ639.8;TP183

Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations

  • 摘要:   目的  针对芒果叶片病虫害缺少数据集和识别准确率低的问题,提高芒果叶病虫害分类准确率。  方法  提出使用去噪扩散模型进行病虫害数据增强,同时联合SKNet与MobilenetV3模型的芒果叶片病虫害分类方法。首先使用去噪扩散模型对数据集进行扩充,再采用多尺度结构相似性指标对生成的病虫害图像与拍摄的病虫害图像之间的相似程度进行评估,接着对DDIM与DCGAN网络训练和生成效果进行比对。在MobilenetV3模型中,将SE注意力模块替换为SKNet模块进行构建网络模型。  结果  使用DDIM生成的所有类型的病虫害图像与拍摄的病虫害图像的MS-SSIM指标均大于0.63,且都高于DCGAN。相较于其他注意力模块,联合SKNet与MobilenetV3的分类效果最佳,在98%以上。对添加CA、CBAM、ECA注意力模块进行平滑类激活图可视化,对比其他注意力模块,使用SKNet注意力分布区域更为集中在病虫害叶片上。  结论  该方法在病虫害叶片检测上具有良好的应用前景,能提升病虫害识别效率与精度,减少检测成本,同时可应用于移动式或者嵌入式设备。
  • 图  1  病虫害叶片数据集样本

    Figure  1.  Sample of diseased mango leaf images dataset

    图  2  加噪与去噪示意图

    Figure  2.  Schematic diagram of noise addition and removal

    图  3  融合SKNet的bneck模型结构

    ⊕—元素相加运算;⊗—元素相乘运算。

    Figure  3.  SKNet-integrated bneck model

    ⊕—Element-wise addition; ⊗—element-wise multiplication.

    图  4  学习率变化曲线

    Figure  4.  Change on learning rate

    图  5  0-1000步学习率曲线

    Figure  5.  Learning rate from 0 to 1000 step

    图  6  DDIM生成各类芒果叶片病虫害图

    Figure  6.  DDIM-generated mango leaf diseases

    图  7  迭代次数0-80下数据增强前后损失值与准确率曲线图

    Figure  7.  Loss and accuracy before and after data augmentation over Epochs 0-80

    图  8  原始图像集(上)、DCGAN数据增强(中)、DDIM数据增强(下)分类混淆矩阵

    Figure  8.  Classification confusion matrix on original (top), DCGAN data augmented (middle), and DDIM data augmented (bottom) image sets

    图  9  平滑类激活图(从上到下分别为添加SE、CA、CBAM、ECA、SKNet注意力模块的MobilenetV3模型)

    Figure  9.  Smooth Grad-CAM (from top to bottom: Mobilenet V3 with added SE, CA, CBAM, ECA, and SKNet attention modules, respectively)

    表  1  MobilenetV3模型结构

    Table  1.   Structure of Mobilenet V3

    输入尺寸
    Input shape
    操作算子
    Operation
    扩展尺寸
    Expand size
    输出通道
    Output channel
    SE模块
    SE module
    激活函数
    Activation function
    步长
    Stride
    2242 conv2d,3×3 16 HS 2
    1122×16 bneck,3×3 16 16 RE 2
    562×16 bneck,3×3 72 24 RE 2
    282×24 bneck,3×3 88 24 RE 1
    282×24 bneck,5×5 96 40 HS 2
    142×40 bneck,5×5 240 40 HS 1
    142×40 bneck,5×5 240 40 HS 1
    142×40 bneck,5×5 120 48 HS 1
    142×48 bneck,5×5 144 48 HS 1
    142×48 bneck,5×5 288 96 HS 2
    72×96 bneck,5×5 576 96 HS 1
    72×96 bneck,5×5 576 96 HS 1
    72×96 conv2d,1×1 576 HS 1
    72×576 pool,7×7 1
    12×576 conv2d1×1,NBN 1280 HS 1
    12×1024 conv2d1×1,NBN 7 1
    conv2d—二维卷积;bneck—瓶颈模块;pool—池化层;NBN—不使用批量归一化;HS—硬切线激活函数;RE—修正线性单元;√—使用SE模块。
    conv2d: 2D convolution; bneck: bottleneck module; pool: pooling layer; NBN: no batch normalization; HS: hard swish activation function; RE: ReLU (rectified linear unit); √: SE module applied.
    下载: 导出CSV

    表  2  图片生成前后数据量比较

    Table  2.   Data volumes before and after image generation

    类型
    Type
    原始图数量
    original dataset
    增强后图像数量
    Augmented dataset
    炭疽病 Colletotrichum gloeosporioides 320 1800
    细菌性角斑病
    Xanthomonas campestris pv. mangiferaeindicae
    324 1800
    切叶象甲 Myllocerus viridanus 320 1800
    枯萎病 Fusarium oxysporum 320 1800
    瘿蚊 Erosomyia mangiferae 544 1800
    白粉病 Oidium mangiferae 320 1800
    煤污病 Capnodium mangiferae 320 1800
    下载: 导出CSV

    表  3  DDIM与DCGAN网络模型训练指标对比

    Table  3.   Network model training metrics of DDIM and DCGAN

    指标 Index DDIM DCGAN
    模型大小 Model size/MB 117 89
    训练时间 Training time /h 48 36
    收敛速度(训练轮次) Convergence speed (epochs) 19 57
    训练中损失函数的标准偏差 Standard deviation 0.05 0.15
    总耗时(秒) Total time (seconds) 392.6 457.2
    下载: 导出CSV

    表  4  生成的病虫害图像与拍摄的病虫害图像MS-SSIM值

    Table  4.   MS-SSIM values on virtually generated and camera-captured images of diseased leaves

    图像类型
    Image type
    MS-SSIM
    (DDIM)
    MS-SSIM
    (DCGAN)
    炭疽病 Colletotrichum gloeosporioides 0.6312 0.5992
    细菌性角斑病
    Xanthomonas campestris pv. mangiferaeindicae
    0.7298 0.6912
    切叶象甲 Myllocerus viridanus 0.6754 0.6413
    枯萎病 Fusarium oxysporum 0.7123 0.6805
    瘿蚊 Erosomyia mangiferae 0.7459 0.6990
    白粉病 Oidium mangiferae 0.7211 0.6853
    煤污病 Capnodium mangiferae 0.6671 0.6396
    下载: 导出CSV

    表  5  引入注意力模块的MobilenetV3模型实验对比

    Table  5.   Experimental results on Mobilenet V3 model introduced with attention modules

    算法
    Algorithm
    准确率
    Accuracy/%
    参数量
    Params/M
    乘加运算数
    MACs/G
    MobilenetV3(SE) 97.24 2.54 0.06
    MobilenetV3+CA 95.75 2.18 0.06
    MobilenetV3+CBAM 97.69 2.59 0.09
    MobilenetV3+ECA 97.51 2.08 0.06
    MobilenetV3+SKNet 98.21 2.58 0.06
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-04-18
  • 修回日期:  2024-05-05
  • 网络出版日期:  2024-06-26

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