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Laser & Optoelectronics Progress, Volume. 60, Issue 2, 0210005(2023)

Lightweight Apple-Leaf Pathological Recognition Based on Multiscale Fusion

Dengzhun Wang1,2, fei Li1,2, Chunyu Yan1,2, Ruixin Liu1,2, Jianwei Yan3, Wenyong Zhang4, and Benliang Xie1,2、*
Author Affiliations
  • 1College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, Guizhou, China
  • 2Semiconductor Power Device Reliability Engineering Research Center of the Ministry of Education, Guiyang 550025, Guizhou, China
  • 3School of Mechanical Engineering, Guizhou University, Guiyang 550025, Guizhou, China
  • 4School of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
    Equations (5)
    References (27)
    Cited By (2)
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    Paper Information
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    Figures & Tables(14)
    Total model architecture

    Fig. 1. Total model architecture

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    Multiscale feature extraction network

    Fig. 2. Multiscale feature extraction network

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    Feature fusion block

    Fig. 3. Feature fusion block

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    Comparison between standard convolution and multiscale depth separable convolution. (a) Standard convolution; (b) multiscale depthwise separable convolution

    Fig. 4. Comparison between standard convolution and multiscale depth separable convolution. (a) Standard convolution; (b) multiscale depthwise separable convolution

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    Comparison between original residual block and improved residual block

    Fig. 5. Comparison between original residual block and improved residual block

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    Images of apple leaf disease. (a) Mosaic; (b) brown_spot; (c) rust; (d) grey_spot; (e) alternaria_boltch

    Fig. 6. Images of apple leaf disease. (a) Mosaic; (b) brown_spot; (c) rust; (d) grey_spot; (e) alternaria_boltch

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    Variation curve of model recognition accuracy and loss value. (a) Validation set accuracy; (b) validation set loss value

    Fig. 7. Variation curve of model recognition accuracy and loss value. (a) Validation set accuracy; (b) validation set loss value

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    Fusion matrix of test set in dataset

    Fig. 8. Fusion matrix of test set in dataset

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    Confusing leaf images of two apple diseases. (a) Alternaria_boltch; (b) grey_spot

    Fig. 9. Confusing leaf images of two apple diseases. (a) Alternaria_boltch; (b) grey_spot

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    • Table 1. Dataset distribution

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      Table 1. Dataset distribution

      Diseases category of apple leafTraining setValidation setTest set
      Mosaic2918978978
      Brown_spot337911381138
      Rust349710981098
      Grey_spot2886995995
      Alternaria_boltch321010661066
      Total1582752755275

    • Table 2. Experimental environment configuration

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      Table 2. Experimental environment configuration

      Operating systemUbuntu 18.0
      GPUNVIDIA Tesla V100
      Deep learning frameworkPytorch-GPU-1.7.1
      Programing languagePython 3.8.2
      GPU Acceleration libraryCUDA10.2，CUDNN8.0

    • Table 3. Output dimension number comparison experiment

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      Table 3. Output dimension number comparison experiment

      Diseases category of apple leafRecognition accuracy of the test set in dataset
      ResNet18Map（4）Map（8）Map（16）Map（32）
      Mosaic99.5499.6999.9099.5999.39
      Brown_spot99.3299.1299.2199.8299.91
      Rust94.6395.9997.1296.2295.57
      Grey_spot93.4795.4996.3995.2095.04
      Alternaria_boltch96.3494.9594.9393.8494.82
      Arc96.6697.0497.5196.9396.95

    • Table 4. Test results of different combinations of improvement strategies

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      Table 4. Test results of different combinations of improvement strategies

      Diseases category of apple leafRecognition accuracy of the Validation set in dataset
      Map（8）Mu-ds（3×3，5×5）+Map（8）Mu-ds（3×3，7×7）+Map（8）Mu-ds（5×5，7×7）+Map（8）Mu-ds（3×3，5×5，7×7）+Map（8）
      Mosaic99.9099.6999.8099.8999.29
      Brown_spot99.2199.9199.3899.2199.55
      Rust97.1297.3694.7797.2794.59
      Grey_spot96.3996.0095.8096.6195.79
      Alternaria_boltch94.9393.6096.6397.3597.84
      Arc97.5197.3197.2598.0597.41

    • Table 5. Comparison of recognition results of different algorithms

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      Table 5. Comparison of recognition results of different algorithms

      ModelTest set accuracy /%Parameters /MBFloating Point Operations /GB
      ResNet1896.6611.181.83
      ResNet5096.4523.524.12
      AlexNet95.1314.60.31
      VGG1695.8872.3415.44
      Inception_V497.2141.156.15
      MobileNet_V296.072.230.32
      Propose Model98.054.020.92
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    Dengzhun Wang, fei Li, Chunyu Yan, Ruixin Liu, Jianwei Yan, Wenyong Zhang, Benliang Xie. Lightweight Apple-Leaf Pathological Recognition Based on Multiscale Fusion[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0210005

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    Paper Information

    Category: Image Processing

    Received: Aug. 16, 2021

    Accepted: Nov. 15, 2021

    Published Online: Jan. 3, 2023

    The Author Email: Benliang Xie (blxie@gzu.edu.cn)

    DOI:10.3788/LOP212261

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology