An Information Entropy Masked Vision Transformer (IEM-ViT) Model for Recognition of Tea Diseases
Abstract
:1. Introduction
2. Materials and Methods
2.1. IE of Tea Disease Images
2.2. Tea Leaf’s Disease Recognition
2.2.1. Vision Transformer
2.2.2. Self-Supervised Learning
2.2.3. Masked Visual Autoencoder
2.3. Dataset Description
2.4. Tea Disease Recognition
Algorithm 1: Image Fragmentation IE Weighting Algorithm |
Input patches (R G B), mask_ratio |
Output: input autoEncode model patches |
1. Entr = {0,0,…,0} |
2. L = len(patch) |
3. for patch →patches: |
4. I_R(x,y), I_G(x,y),I_B(x,y) = patch(R G B) |
5. img(x,y) = 1/3*I_R(x,y) + 1/3*I_G(x,y) + 1/3*I_B(x,y) |
6. hist = histogram(img, bins = range(0, 256)) |
7. hist = hist[hist > 0] |
8. res = −log2(hist/hist.sum()).sum() |
9. entr[i] = res |
10. len_keep = int(L * (1 − mask_ratio)) |
11. ids = argsort(random(L)* entr)[0:len_keep] |
12. return patch[ids] |
3. Experimental Results and Analysis
3.1. Model Parameters
3.2. Tea Disease Image Completion
3.3. Experimental Results
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value | Description |
---|---|---|
Target_size | 224 × 224 × 3 | Size of the image we fed into the model |
Batch_size | 16 | Number of images in a batch |
Mask_ratio | 0.75 | Masking ratio |
Patch_size | 16 × 16 × 3 | Size of the image patch |
Number_heads | 16 | Number of transformer heads |
Model | Accuracy (%) | Precision | Recall | Fi-Score |
---|---|---|---|---|
IEM-ViT (this work) | 93.78 | 0.9367 | 0.9380 | 0.9364 |
VGG16 | 73.40 | 0.7850 | 0.7694 | 0.7557 |
VGG19 | 71.75 | 0.7481 | 0.7429 | 0.7336 |
ResNet18 | 75.10 | 0.7750 | 0.7748 | 0.7620 |
Model | Reference | Types Evaluated | Accuracy (%) |
---|---|---|---|
Concatenated CNN | Krisnandi et al. [40] | 4 | 89.64 |
VGG16 | Hu et al. [41] | 3 | 90 |
LeafNet | Chen et al. [5] | 7 | 90.16 |
TLDR | Karmokar et al. [42] | 1 | 91 |
SVM | Hossain et al. [4] | 2 | 91 |
Improved deep CNN | Hu et al. [43] | 4 | 92.5 |
IEM-ViT | This paper | 7 | 93.78 |
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Zhang, J.; Guo, H.; Guo, J.; Zhang, J. An Information Entropy Masked Vision Transformer (IEM-ViT) Model for Recognition of Tea Diseases. Agronomy 2023, 13, 1156. https://doi.org/10.3390/agronomy13041156
Zhang J, Guo H, Guo J, Zhang J. An Information Entropy Masked Vision Transformer (IEM-ViT) Model for Recognition of Tea Diseases. Agronomy. 2023; 13(4):1156. https://doi.org/10.3390/agronomy13041156
Chicago/Turabian StyleZhang, Jiahong, Honglie Guo, Jin Guo, and Jing Zhang. 2023. "An Information Entropy Masked Vision Transformer (IEM-ViT) Model for Recognition of Tea Diseases" Agronomy 13, no. 4: 1156. https://doi.org/10.3390/agronomy13041156