Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Construction
2.2. Experimental Platform Training Parameters
2.3. Evaluation Indicators
2.4. DenseNet Network
2.5. Regularization Network
3. Results
3.1. Comparison Study
3.2. Transfer Learning Experiments
3.3. Rice Disease Detection App
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Index | Disease Name | Images |
---|---|---|
1 | Bacterial leaf blight | 479 |
2 | Bacterial leaf streak | 629 |
3 | Bacterial panicle blight | 337 |
4 | Blast | 1738 |
5 | Brown spot | 965 |
6 | Dead Heart | 1442 |
7 | Downy mildew | 620 |
8 | Hispa | 1594 |
9 | Normal | 1764 |
10 | Tungro | 1088 |
11 | Smut | 373 |
12 | Rice sheath blight | 252 |
Configuration | Parameter |
---|---|
CPU | Intel(R) Xeon(R) CPU E5-2690 v4 |
GPU | NVIDIA GeForce RTX 3060 Ti, 8192 MB VRAM |
Operating system | Windows 10 |
CUDA accelerated environment | 2.0.1+cu117 |
Python | 3.10.12 |
Pytorch | 11.8 |
Lr | 0.0001 |
Batch-size | 16 |
Workers | 4 |
Epoch | 100 |
Optimizer | Adam |
Model Name | Accuracy% | Precision% | Recall% | F1 Score | Parameters/M |
---|---|---|---|---|---|
Vgg16 | 92.1 | 90.5 | 90.1 | 90.3 | 134.31 |
GoogleNet | 90.3 | 88.7 | 88.8 | 88.7 | 10.34 |
ResNet34 | 94.0 | 93.0 | 92.7 | 92.8 | 21.29 |
MobileNet v2 | 93.0 | 92.3 | 90.6 | 91.3 | 2.24 |
MobileNet v3 small | 91.9 | 90.3 | 89.8 | 90.0 | 1.53 |
MobileNet v3 large | 93.8 | 92.1 | 92.6 | 92.3 | 4.22 |
ShuffleNet v2 | 93.3 | 92.5 | 91.4 | 91.8 | 1.27 |
ShuffleNet v1 | 94.2 | 93.6 | 92.9 | 93.2 | 0.92 |
DenseNet | 95.7 | 95.3 | 94.8 | 95.0 | 6.97 |
EfficientNet v2 | 95.3 | 94.2 | 93.6 | 93.9 | 24.20 |
Swin Transformer tiny | 92.8 | 91.2 | 90.3 | 90.7 | 27.53 |
MobileVIT | 93.8 | 92.4 | 92.3 | 91.2 | 0.96 |
RegNet | 93.8 | 91.8 | 92.0 | 91.8 | 3.91 |
Vision Transformer | 91.8 | 90.5 | 90.8 | 90.6 | 85.81 |
Disease Name | MobileNet v3 Large | Shufflenet v1 | Shufflenet v2 | DenseNet 121 | MobileVIT | RegNet |
---|---|---|---|---|---|---|
Bacterial leaf blight | 93.7 | 96.8 | 92.6 | 95.8 | 92.6 | 91.6 |
Bacterial leaf streak | 88.0 | 89.6 | 90.4 | 92.0 | 88.8 | 88.0 |
Bacterial panicle blight | 98.5 | 97.0 | 97.0 | 97.0 | 98.5 | 97.0 |
Blast | 94.8 | 92.5 | 94.2 | 96.8 | 95.1 | 96.8 |
Brown spot | 92.7 | 93.8 | 94.3 | 94.8 | 95.8 | 91.2 |
Dead Heart | 98.9 | 99.7 | 98.9 | 99.3 | 97.9 | 97.9 |
Downy mildew | 90.3 | 88.7 | 83.9 | 92.7 | 86.3 | 91.9 |
Hispa | 93.4 | 94.9 | 91.2 | 95.3 | 92.4 | 94.6 |
Normal | 94.3 | 95.7 | 96.6 | 97.4 | 96.3 | 96.9 |
Tungro | 84.0 | 84.0 | 76.0 | 88.0 | 80.0 | 84.0 |
Smut | 89.2 | 86.5 | 87.8 | 94.6 | 90.5 | 93.2 |
Rice sheath blight | 93.5 | 95.4 | 93.5 | 94.0 | 93.5 | 88.9 |
Disease Name | MobileNet v3 Large | Shufflenet v1 | Shufflenet v2 | DenseNet 121 | MobileVIT | RegNet |
---|---|---|---|---|---|---|
Bacterial leaf blight | 89.5 | 95.3 | 91.6 | 94.8 | 93.1 | 92.1 |
Bacterial leaf streak | 88.7 | 89.9 | 90.7 | 92.3 | 91.4 | 90.9 |
Bacterial panicle blight | 97.1 | 94.9 | 92.8 | 98.5 | 95.0 | 95.6 |
Blast | 95.2 | 94.1 | 94.2 | 96.1 | 95.0 | 95.6 |
Brown spot | 93.9 | 94.0 | 94.5 | 95.0 | 93.0 | 94.4 |
Dead Heart | 98.6 | 99.0 | 98.6 | 99.5 | 98.7 | 98.4 |
Downy mildew | 87.8 | 86.9 | 88.2 | 92.0 | 87.7 | 86.0 |
Hispa | 93.5 | 93.8 | 93.4 | 94.8 | 93.3 | 95.1 |
Normal | 95.2 | 95.7 | 93.1 | 97.0 | 95.3 | 94.9 |
Tungro | 83.9 | 89.4 | 82.6 | 90.7 | 81.6 | 86.6 |
Smut | 91.1 | 90.8 | 89.0 | 94.0 | 90.5 | 91.4 |
Rice sheath blight | 93.3 | 94.5 | 92.9 | 95.7 | 93.5 | 92.1 |
Disease Name | Vgg16 | GoogleNet | ResNet34 | EfficientNet v2 | Swin Transformer | Vision Transformer |
---|---|---|---|---|---|---|
Bacterial leaf blight | 82.1 | 82.1 | 91.6 | 92.6 | 84.2 | 89.5 |
Bacterial leaf streak | 87.2 | 86.4 | 88.0 | 92.8 | 88.8 | 90.4 |
Bacterial panicle blight | 94.0 | 92.5 | 97.0 | 97.0 | 94.0 | 98.5 |
Blast | 95.7 | 86.4 | 96.8 | 96.8 | 96.2 | 93.6 |
Brown spot | 89.1 | 87.5 | 92.0 | 96.4 | 93.3 | 91.7 |
Dead Heart | 99.0 | 98.3 | 97.9 | 99.3 | 98.6 | 95.1 |
Downy mildew | 85.5 | 89.5 | 91.9 | 88.7 | 82.2 | 87.9 |
Hispa | 94.0 | 90.2 | 94.6 | 94.3 | 91.2 | 89.9 |
Normal | 96.0 | 94.3 | 96.9 | 96.5 | 94.9 | 93.7 |
Tungro | 86.0 | 82.0 | 84.0 | 84.0 | 84.0 | 88.0 |
Smut | 86.5 | 85.1 | 93.2 | 87.8 | 79.7 | 81.1 |
Rice sheath blight | 89.4 | 90.8 | 88.9 | 96.8 | 96.8 | 90.8 |
Disease Name | Vgg16 | GoogleNet | ResNet34 | EfficientNet v2 | Swin Transformer | Vision Transformer |
---|---|---|---|---|---|---|
Bacterial leaf blight | 85.2 | 80.8 | 92.1 | 93.1 | 87.0 | 86.7 |
Bacterial leaf streak | 88.2 | 88.5 | 90.9 | 91.7 | 87.4 | 90.4 |
Bacterial panicle blight | 94.7 | 91.8 | 95.6 | 97.7 | 95.4 | 97.8 |
Blast | 93.0 | 90.7 | 95.6 | 96.0 | 94.5 | 93.3 |
Brown spot | 90.5 | 87.3 | 94.4 | 96.1 | 93.5 | 92.2 |
Dead Heart | 98.6 | 97.0 | 98.4 | 99.1 | 97.9 | 95.8 |
Downy mildew | 82.8 | 83.5 | 86.0 | 88.7 | 84.7 | 84.5 |
Hispa | 93.0 | 90.4 | 95.1 | 95.8 | 92.0 | 89.3 |
Normal | 95.1 | 92.6 | 94.9 | 97.0 | 95.1 | 94.4 |
Tungro | 85.1 | 83.7 | 86.6 | 85.7 | 80.8 | 84.6 |
Smut | 86.5 | 87.5 | 91.4 | 90.8 | 85.5 | 85.7 |
Rice sheath blight | 90.6 | 90.1 | 92.1 | 94.4 | 94.8 | 92.7 |
Model Name | Accuracy% | Precision% | Recall% | F1 Score | Parameters/M |
---|---|---|---|---|---|
Vgg16 | 93.5 | 93.4 | 92.8 | 93.1 | 134.31 |
GoogleNet | 91.0 | 89.0 | 90.0 | 89.2 | 10.34 |
ResNet34 | 96.7 | 96.0 | 95.7 | 95.8 | 21.29 |
MobileNet v2 | 71.6 | 73.4 | 71.0 | 71.7 | 2.24 |
MobileNet v3_small | 87.6 | 87.9 | 86.0 | 86.8 | 1.53 |
MobileNet v3_large | 91.8 | 91.6 | 91.0 | 91.3 | 4.22 |
Shufflenet v2 | 96.6 | 95.7 | 95.7 | 95.7 | 1.27 |
Shufflenet v1 | 94.4 | 93.7 | 92.2 | 92.9 | 0.92 |
DenseNet | 94.9 | 94.5 | 93.8 | 94.2 | 6.97 |
EfficientNet v2 | 95.6 | 94.2 | 94.3 | 94.2 | 24.20 |
Swin Transformer tiny | 94.0 | 93.2 | 92.6 | 92.9 | 27.53 |
MobileVIT | 92.6 | 91.1 | 91.3 | 91.1 | 0.96 |
RegNet | 96.8 | 96.2 | 95.9 | 96.0 | 3.91 |
Vision Transformer | 96.6 | 96.2 | 95.9 | 96.0 | 85.81 |
Disease Name | MobileNet v3 Large | ShuffleNet v1 | ShuffleNet v2 | DenseNet | MobileVIT | RegNet |
---|---|---|---|---|---|---|
Bacterial leaf blight | 92.6 | 94.7 | 94.7 | 97.9 | 93.6 | 93.7 |
Bacterial leaf streak | 92.0 | 94.4 | 96.0 | 92.8 | 89.6 | 96.8 |
Bacterial panicle blight | 94.0 | 86.5 | 98.5 | 97.0 | 97.0 | 98.5 |
Blast | 93.6 | 97.7 | 95.9 | 95.1 | 94.2 | 98.0 |
Brown spot | 87.5 | 95.8 | 98.9 | 94.8 | 89.6 | 97.4 |
Dead Heart | 97.2 | 98.2 | 99.3 | 98.9 | 98.2 | 99.3 |
Downy mildew | 86.3 | 90.3 | 93.5 | 86.3 | 88.7 | 93.5 |
Hispa | 89.0 | 94.0 | 95.6 | 93.4 | 92.4 | 96.2 |
Normal | 94.6 | 96.0 | 97.4 | 98.0 | 93.2 | 98.0 |
Tungro | 86.0 | 82.0 | 88.0 | 86.0 | 86.0 | 92.0 |
Smut | 89.2 | 86.5 | 93.2 | 91.9 | 81.1 | 91.9 |
Rice sheath blight | 89.4 | 90.3 | 97.2 | 94.0 | 91.2 | 95.8 |
Disease Name | MobileNet v3 Large | ShuffleNet v1 | ShuffleNet v2 | DenseNet | MobileVIT | RegNet |
---|---|---|---|---|---|---|
Bacterial leaf blight | 89.7 | 94.2 | 94.7 | 96.9 | 91.3 | 94.7 |
Bacterial leaf streak | 91.6 | 92.9 | 96.0 | 93.9 | 87.8 | 95.6 |
Bacterial panicle blight | 93.3 | 92.0 | 96.3 | 97.0 | 95.6 | 98.5 |
Blast | 92.3 | 95.0 | 96.5 | 94.8 | 92.7 | 96.7 |
Brown spot | 89.2 | 95.4 | 98.2 | 96.0 | 93.2 | 97.4 |
Dead Heart | 97.4 | 97.8 | 99.3 | 99.0 | 98.3 | 99.5 |
Downy mildew | 87.3 | 91.4 | 93.5 | 87.7 | 86.3 | 93.5 |
Hispa | 88.3 | 94.4 | 96.0 | 94.7 | 93.0 | 96.3 |
Normal | 93.8 | 95.4 | 96.8 | 95.5 | 94.1 | 97.7 |
Tungro | 88.6 | 84.5 | 89.8 | 87.7 | 81.1 | 90.2 |
Smut | 91.7 | 88.3 | 93.8 | 93.1 | 88.2 | 95.1 |
Rice sheath blight | 91.7 | 93.3 | 97.2 | 93.3 | 90.8 | 97.4 |
Disease Name | Vgg16 | GoogleNet | ResNet34 | EfficientNet v2 | Swin Transformer | Vision Transformer |
---|---|---|---|---|---|---|
Bacterial leaf blight | 84.2 | 81.0 | 96.8 | 98.9 | 90.5 | 96.8 |
Bacterial leaf streak | 91.2 | 87.2 | 96.8 | 92.8 | 87.2 | 95.2 |
Bacterial panicle blight | 100 | 97.0 | 100 | 97.0 | 97.0 | 100 |
Blast | 94.5 | 91.6 | 98.5 | 96.2 | 97.7 | 98.8 |
Brown spot | 94.3 | 91.7 | 95.3 | 94.3 | 95.8 | 96.4 |
Dead Heart | 98.6 | 95.1 | 99.6 | 99.6 | 99.3 | 99.3 |
Downy mildew | 86.3 | 86.3 | 93.5 | 91.1 | 87.1 | 90.3 |
Hispa | 91.8 | 93.4 | 94.6 | 94.6 | 94.0 | 94.6 |
Normal | 97.7 | 92.0 | 97.7 | 97.1 | 93.4 | 97.7 |
Tungro | 94.0 | 88.0 | 86.0 | 80.0 | 88 | 90.0 |
Smut | 95.6 | 82.4 | 91.9 | 93.2 | 90.5 | 95.9 |
Rice sheath blight | 86.6 | 88.9 | 97.2 | 95.8 | 90.3 | 95.8 |
Disease Name | Vgg16 | GoogleNet | ResNet34 | EfficientNet v2 | Swin Transformer | Vision Transformer |
---|---|---|---|---|---|---|
Bacterial leaf blight | 86.9 | 86.1 | 94.7 | 93.9 | 92.4 | 95.8 |
Bacterial leaf streak | 93.4 | 86.5 | 95.6 | 93.1 | 88.9 | 96.3 |
Bacterial panicle blight | 97.1 | 91.5 | 98.5 | 94.9 | 97.0 | 97.8 |
Blast | 92.7 | 91.7 | 96.7 | 96.3 | 95.0 | 97.0 |
Brown spot | 92.6 | 90.2 | 97.4 | 95.8 | 95.1 | 97.4 |
Dead Heart | 98.9 | 96.6 | 99.5 | 99.7 | 99.3 | 99.3 |
Downy mildew | 87.7 | 82.3 | 93.5 | 90.4 | 90.0 | 92.1 |
Hispa | 92.8 | 92.1 | 96.3 | 95.3 | 92.6 | 95.1 |
Normal | 95.5 | 94.0 | 97.7 | 97.2 | 94.3 | 97.0 |
Tungro | 94.0 | 84.6 | 90.2 | 83.3 | 84.6 | 91.8 |
Smut | 95.2 | 85.3 | 95.1 | 95.1 | 92.4 | 94.6 |
Rice sheath blight | 89.5 | 88.9 | 97.4 | 95.2 | 92.6 | 97.6 |
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Li, Y.; Chen, X.; Yin, L.; Hu, Y. Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images. Agronomy 2024, 14, 1879. https://doi.org/10.3390/agronomy14091879
Li Y, Chen X, Yin L, Hu Y. Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images. Agronomy. 2024; 14(9):1879. https://doi.org/10.3390/agronomy14091879
Chicago/Turabian StyleLi, Yuhai, Xiaoyan Chen, Lina Yin, and Yue Hu. 2024. "Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images" Agronomy 14, no. 9: 1879. https://doi.org/10.3390/agronomy14091879