# Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications

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## Abstract

**:**

## 1. Introduction

## 2. Dataset

## 3. Methodology

#### 3.1. GAN

#### 3.2. WGAN

#### 3.3. WGAN-GP

#### 3.4. Progressive Training

#### 3.5. Residual Block

#### 3.6. Progressive WGAN-GP

#### 3.6.1. Residual Block

#### 3.6.2. Upsampling

#### 3.6.3. Batch Normalization Layer

#### 3.6.4. LeakyReLU

#### 3.7. Traditional Image Data Augmentation

## 4. Experiment

#### 4.1. Experimental Setup

#### 4.2. Evaluation Metrics

#### 4.3. Training Process

#### 4.4. Performance of the Data Augmentation Model

## 5. Results and Discussion

#### 5.1. Generating Image Quality

#### 5.2. Performance of the Data Enhancement Model

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 9.**TIDA approach, where (

**a**) is the original image, (

**b**) rotation, (

**c**) panning, (

**d**) scaling, (

**e**) brightness adjustment, (

**f**) contrast adjustment, and (

**g**) adding noise.

Categories | Numbers |
---|---|

Blast | 1654 |

Brown Spot | 1570 |

Blight | 1396 |

Healthy | 2563 |

Categories | Train Dataset | Test Dataset |
---|---|---|

Blast | 1323 | 331 |

Brown Spot | 1256 | 314 |

Blight | 1116 | 280 |

Healthy | 2050 | 513 |

Layer Name | Activation Function | Output Tensor |
---|---|---|

Latent vector | - | 512 × 1 × 1 |

Residual block | LeakyReLu | 512 × 4 × 4 |

Upsample | - | 512 × 8 × 8 |

Residual block | LeakyReLu | 512 × 8 × 8 |

Upsample | - | 512 × 16 × 16 |

Residual block | LeakyReLu | 512 × 16 × 16 |

Upsample | - | 128 × 32 × 32 |

Residual block | LeakyReLu | 128 × 32 × 32 |

Upsample | - | 64 × 64 × 64 |

Residual block | LeakyReLu | 64 × 64 × 64 |

Upsample | - | 32 × 128 × 128 |

Residual block | LeakyReLu | 32 × 128 × 128 |

Upsample | - | 16 × 256 × 256 |

Residual block | LeakyReLu | 16 × 256 × 256 |

Conv 1 × 1 | - | 3 × 256 × 256 |

Layer Name | Activation Function | Output Tensor |
---|---|---|

Intput image | - | 3 × 256 × 256 |

Conv 1 × 1 | LeakyReLU | 16 × 256 × 256 |

Residual block | LeakyReLU | 32 × 256 × 256 |

Downsample | - | 32 × 128 × 128 |

Residual block | LeakyReLU | 64 × 128 × 128 |

Downsample | - | 64 × 64 × 64 |

Residual block | LeakyReLU | 128 × 64 × 64 |

Downsample | - | 128 × 32 × 32 |

Residual block | LeakyReLU | 256 × 32 × 32 |

Downsample | - | 256 × 16 × 16 |

Residual block | LeakyReLU | 512 × 16 × 16 |

Downsample | - | 512 × 8 × 8 |

Residual block | LeakyReLU | 512 × 8 × 8 |

Downsample | - | 512 × 4 × 4 |

Avg pool, fc 1, softmax | - | 1 × 1 × 1 |

Method | Blast | Brown Spot | Blight | Healthy | FID Score Average |
---|---|---|---|---|---|

WGAN | 118.42 | 133.71 | 137.51 | 131.84 | 130.37 |

DCGAN | 95.37 | 107.26 | 101.68 | 90.39 | 98.68 |

Dual GAN | 70.13 | 86.78 | 92.24 | 64.20 | 78.34 |

WGAN-GP | 75.18 | 84.96 | 79.33 | 72.61 | 78.02 |

PWGAN-GP | 62.11 | 71.24 | 74.38 | 60.73 | 67.12 |

Method | Training Time (h) |
---|---|

WGAN | 45 |

DCGAN | 52 |

WGAN-GP | 59 |

PWGAN-GP | 88 |

Dual GAN | 97 |

Level | Blast | Brown Spot | Blight | Healthy |
---|---|---|---|---|

X0 | 83.21 | 79.76 | 80.11 | 82.62 |

X1 | 88.48 | 85.81 | 87.83 | 91.97 |

X2 | 93.77 | 89.28 | 91.35 | 90.31 |

X3 | 90.52 | 88.31 | 89.7 | 88.27 |

Max. Improve | 10.56 | 9.52 | 11.24 | 9.35 |

Level | Blast | Brown Spot | Blight | Healthy |
---|---|---|---|---|

X0 | 83.62 | 82.53 | 82.73 | 84.17 |

X1 | 94.84 | 94.03 | 93.84 | 94.16 |

X2 | 96.26 | 94.85 | 94.91 | 95.37 |

X3 | 95.53 | 95.01 | 94.69 | 95.21 |

Max. Improve | 12.64 | 12.48 | 12.18 | 11.20 |

Level | Blast | Brown Spot | Blight | Healthy |
---|---|---|---|---|

X0 | 84.21 | 82.09 | 83.53 | 85.07 |

X1 | 96.77 | 94.74 | 95.48 | 96.81 |

X2 | 98.25 | 95.22 | 95.94 | 97.19 |

X3 | 97.63 | 94.44 | 94.23 | 96.98 |

Max. Improve | 14.04 | 13.13 | 12.41 | 12.18 |

Method | Blast | Brown Spot | Blight | Healthy | Avg. |
---|---|---|---|---|---|

Actual data | 83.21 | 79.76 | 80.11 | 82.62 | 81.03 |

TIDA | 88.15 | 88.04 | 88.71 | 89.16 | 88.27 |

PWGAN-GP | 93.77 | 89.28 | 91.35 | 90.31 | 91.47 |

Method | Blast | Brown Spot | Blight | Healthy | Avg. |
---|---|---|---|---|---|

Actual data | 83.62 | 82.53 | 82.73 | 84.17 | 82.96 |

TIDA | 91.44 | 90.57 | 91.43 | 92.46 | 91.48 |

PWGAN-GP | 96.26 | 94.85 | 94.91 | 95.37 | 95.34 |

Method | Blast | Brown Spot | Blight | Healthy | Avg. |
---|---|---|---|---|---|

Actual data | 84.21 | 82.09 | 83.53 | 85.07 | 83.28 |

TIDA | 93.12 | 93.31 | 93.18 | 93.83 | 93.36 |

PWGAN-GP | 98.25 | 95.22 | 95.94 | 97.19 | 96.47 |

Hyperparameter | Condition |
---|---|

learning rate | 0.001, 0.005, 0.01, 0.05, 0.1 |

batch size | 16, 32, 64, 128, 256 |

optimizer | SGD, Adam, RMSProp |

Dataset Type | Average Accuracy (%) |
---|---|

Balanced dataset | 98.04 |

Imbalanced dataset | 98.33 |

Categories | Numbers |
---|---|

Blast | 56 |

Brown Spot | 62 |

Blight | 60 |

Healthy | 60 |

Model | Average Accuracy (%) |
---|---|

ResNet-50 | 81.55 |

TIDA+ResNet-50 | 94.84 |

PWGAN-GP+ResNet-50 | 97.03 |

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## Share and Cite

**MDPI and ACS Style**

Lu, Y.; Tao, X.; Zeng, N.; Du, J.; Shang, R. Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications. *Remote Sens.* **2023**, *15*, 1789.
https://doi.org/10.3390/rs15071789

**AMA Style**

Lu Y, Tao X, Zeng N, Du J, Shang R. Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications. *Remote Sensing*. 2023; 15(7):1789.
https://doi.org/10.3390/rs15071789

**Chicago/Turabian Style**

Lu, Yang, Xianpeng Tao, Nianyin Zeng, Jiaojiao Du, and Rou Shang. 2023. "Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications" *Remote Sensing* 15, no. 7: 1789.
https://doi.org/10.3390/rs15071789