Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network
Abstract
1. Introduction
2. Related Work
3. Dataset
3.1. Image Collection of Citrus Pests
3.2. Image Collection of Citrus Diseases
3.3. Data Augmentation
4. Weakly DenseNet Architecture
4.1. The 1 × 1 Convolution for Feature Refinement
4.2. Feature Reuse
4.3. Network Architecture
5. Experiments and Results
5.1. Training
Algorithm 1. Learning Rate Schedule |
Input: Patience P, decay , validation loss L Output: Learning rate 1: Initialize L = L0, 2: i ← 0 3: while i < P do 4: if L Li then 5: i = i + 1 6: else 7: L = Li 8: i = i + 1 9: end if 10: end while 11: if L = L0 then 12: 13: end if |
5.2. Test
6. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
Appendix A
Appendix B
Appendix C
References
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Class ID | Common Name | Scientific Name | Number of Samples |
---|---|---|---|
Citrus Pests | |||
8 | Mediterranean fruit fly | Ceratitis capitata | 558 |
0 | Asian citrus psyllid | Diaphorina citri Kuwayama | 359 |
5 | Citrus longicorn beetle | Anoplophora chinensis | 597 |
7 | Brown marmorated stink bug | Halyomorpha halys | 606 |
3 | Southern green stink bug | Nezara viridula | 488 |
4 | Fruit sucking moth | Othreis fullonica | 600 |
1 | Citrus swallowtail | Papilio demodocus | 600 |
15 | Citrus flatid planthopper | Metcalfa pruinosa (Say) | 555 |
9 | Citrus mealybug | Planococcus citri | 495 |
13 | Aphids | Toxoptera citricida | 514 |
11 | Citrus soft scale | Hemiptera: Coccidae | 497 |
12 | False codling moth | Thaumatotibia leucotreta | 511 |
14 | Root weevil | Diaprepes abbreviatus, Pachnaeus opalus | 378 |
2 | Forktailed bush katydid | Scudderia furcata | 600 |
10 | Cicada | Cicadoidea | 508 |
6 | Garden snail | Cornu aspersum | 618 |
16 | Glassy-winged sharpshooter | Homalodisca vitripennis | 567 |
Total | 9051 | ||
Citrus Diseases | |||
17 | Anthracnose | Colletotrichum gloeosporioides | 467 |
18 | Canker | Xanthomonas axonopodis | 598 |
20 | Melanose | Diaporthe citri | 532 |
21 | Scab | Elsinoë fawcettii | 503 |
19 | Leaf miner | Liriomyza brassicae | 427 |
22 | Sooty mold | Capnodium spp | 568 |
23 | Pest hole | 415 | |
Total | 3510 |
Operation | Value |
---|---|
Rotation | [, ] |
Width shift | [0, 0.2] |
Height shift | [0, 0.2] |
Shear | [0, 0.2] |
Zoom | [0.8, 1.2] |
Horizontal flip | - |
Block | Output Size |
---|---|
Initial Block (a) | 56 56 32 |
Intermediate Block (b) | 56 56 96 |
Intermediate Block (c) | 28 28 192 |
Intermediate Block (b) | 28 28 384 |
Intermediate Block (c) | 14 14 768 |
1 1 conv, stride 1 | 14 14 512 |
1 1 conv, stride 1 | 14 14 512 |
2 2 max pool, stride 2 | 7 7 512 |
Classification Block (d) | 1 1 24 |
Model Name | Training Accuracy | Validation Accuracy | Model Size (MB) | Training Time (ms)/Batch Size |
---|---|---|---|---|
MobileNet-v1 | 99.23 | 85.45 | 25 | 152 |
MobileNet-v2 | 99.28 | 87.97 | 33.9 | 198 |
ShuffleNet-v1 | 99.13 | 83.58 | 28.8 | 145 |
ShuffleNet-v2 | 98.72 | 83.58 | 42 | 144 |
VGG-16 | 99.82 | 93 | 120.2 | 303 |
SENet-16 | 99.10 | 88.71 | 19.5 | 138 |
NIN-16 | 99.63 | 91.84 | 19.6 | 137 |
WeaklyDenseNet-16 | 99.83 | 93.42 | 30.5 | 138 |
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Xing, S.; Lee, M.; Lee, K.-k. Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network. Sensors 2019, 19, 3195. https://doi.org/10.3390/s19143195
Xing S, Lee M, Lee K-k. Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network. Sensors. 2019; 19(14):3195. https://doi.org/10.3390/s19143195
Chicago/Turabian StyleXing, Shuli, Marely Lee, and Keun-kwang Lee. 2019. "Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network" Sensors 19, no. 14: 3195. https://doi.org/10.3390/s19143195
APA StyleXing, S., Lee, M., & Lee, K.-k. (2019). Citrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network. Sensors, 19(14), 3195. https://doi.org/10.3390/s19143195