Recognition of Sago Palm Trees Based on Transfer Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. UAV Imagery
2.3. Deep Learning and Transfer Learning Models
2.4. Performance Evaluation
- TP, the number of actual images that are displaying sago flowers (true) and are classified as sago flowers (predicted).
- FP, the number of actual images that are not displaying sago flowers (not true) and are classified as sago flowers (predicted).
- FN, the number of actual images that are displaying sago flowers (true) and are classified as a different class (predicted).
- TN, the number of actual images that are not displaying sago flowers (not true) and are classified as a different class (predicted).
3. Results
3.1. Dataset Development
3.2. Training and Testing Data Performance
3.3. Model Performance Evaluation
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Index | Specification |
---|---|
Dimensions | 42.4 × 35.4 × 11 cm |
Battery (life and weight) | Li-Ion 7100 mAh 82 Wh; 40 min; 360 g |
Video resolution | 6K (5472 × 3076) |
ISO range | Video-ISO 100-3200 Cr/100-6400 Manual, Photo-ISO100-3200 Car/100-12800 Manual |
Camera resolution | 20 Mpx; camera chip: 1′ CMOS IMX383 Sony |
Maximum flight time | 40 min (single charge) |
Field of view | 82° |
Gesture control, Wi-Fi, GPS, controller control, Mobile App, homecoming, anti-collision sensors, automatic propeller stop | Provided |
Speeds | 72 km/h to 5 km; winds of 62–74 km/h at up to 7000 m above sea level |
Appendix B
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Layer | Layer Name | Layer Type | Layer Details |
---|---|---|---|
1 | Data | Image input | 227 × 227 × 3 images with zero center normalization |
2 | Conv1 | Convolution | 96 11 × 11 × 3 convolutions with stride [4 4] and padding [0 0 0 0] |
3 | Relu1 | ReLU | ReLU |
4 | Norm1 | Cross channel normalization | Cross channel normalization with 5 channels per elemen |
5 | Pool1 | Max pooling | 3 × 3 max pooling with stride [2 2] and padding [0 0 0 0] |
6 | Conv2 | Grouped convolution | 2 groups of 128 5× 5 × 48 conv with stride [1 1] and padding [2 2 2 2] |
7 | Relu2 | ReLU | ReLU |
8 | Norm2 | Cross channel normalization | Cross channels normalization with 5 channels per element |
9 | Pool2 | Max pooling | 3 × 3 max pooling with stride [2 2] and padding [0 0 0 0] |
10 | Conv3 | Convolution | 384 3 × 3 × 256 convolutions with stride [1 1] and padding [1 1 1 1] |
11 | Relu | ReLU | ReLU |
12 | Conv4 | Grouped convolution | 2 groups of 192 3 × 3 × 192 convolutions with stride [1 1] and padding [1 1 1 1] |
13 | Relu4 | ReLU | ReLU |
14 | Conv5 | Grouped convolution | 2 groups of 128 3 × 3 × 192 convoutions with stride [1 1] and padding [1 1 1 1] |
15 | Relu5 | ReLU | ReLU |
16 | Pool5 | Max pooling | 3 × 3 max pooling with stride [2 2] and padding [0 0 0 0] |
17 | Fc6 | Fully connected | 4096 fully connected layer |
18 | Relu6 | ReLU | ReLU |
19 | Drop6 | Dropout | 50% dropout |
20 | Fc7 | Fully connected | 4096 fully connected layer |
21 | Relu7 | ReLU | ReLU |
22 | Drop7 | Dropout | 50% dropout |
23 | Fc_new | Fully connected | 9 fully connected layer |
24 | Prob | Softmax | |
25 | Classoutput | Classification output |
Network Name | Depth | Image Input Size | Parameters (Millions) | Total Memory (MB) |
---|---|---|---|---|
SqueezeNet | 18 | 227 × 227 × 3 | 1.24 | 5.20 |
ResNet-50 | 50 | 224 × 224 × 3 | 25.6 | 96 |
AlexNet | 8 | 227 × 227 × 3 | 61 | 227 |
Metric | Formula | Criteria |
---|---|---|
F1-score | Denotes a high value, which validates the model. | |
Precision | Examines the ability of the model to predict positive label. | |
Sensitivity (Recall) | Defines the ability of the model to detect instances of certain classes well. | |
Specificity | Defines the true negatives that are correctly identified by the model. | |
Accuracy | Examines the accurately in identifying the images to the classes. |
Parameter Name | Value |
---|---|
Epochs | 10 |
Initial learning rate | 0.0001 |
Validation frequency | 9 |
Learning rate weight coefficient | 10 |
Learning rate bias coefficient | 10 |
Learning rate schedule | Constant |
Momentum | 0.9 |
L2 Regulation | 0.0001 |
Min batch size | 10 |
Model | Training Accuracy (%) | Training Time | Image Input Size | Class | Recall (Sensitivity) | Precision | F1 Score |
---|---|---|---|---|---|---|---|
SqueezeNet | 76.60 | 3 min 39 s | 227 × 227 | CF CL CT OPF OPL OPT SF SL ST | 1.00 0.83 0.71 0.71 0.57 0.71 0.29 0.70 0.25 | 0.80 0.83 1.00 0.71 0.33 0.63 0.67 0.54 0.67 | 0.89 0.83 0.84 0.71 0.42 0.67 0.41 0.61 0.36 |
AlexNet | 76.60 | 5 min 8 s | 227 × 227 | CF CL CT OPF OPL OPT SF SL ST | 0.88 0.86 0.57 0.43 0.14 0.71 0.29 0.80 0.25 | 1.00 0.38 1.00 0.75 0.17 0.39 1.00 0.62 0.67 | 0.94 0.53 0.73 0.55 0.48 0.51 0.45 0.70 0.36 |
ResNet-50 | 82.98 | 18 min 29 s | 224 × 224 | CF CL CT OPF OPL OPT SF SL ST | 0.88 0.71 0.57 0.57 0.71 0.57 0.43 0.70 0.63 | 0.88 0.46 0.80 0.67 0.39 1.00 0.75 0.78 0.83 | 0.88 0.56 0.67 0.62 0.50 0.73 0.55 0.74 0.72 |
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Letsoin, S.M.A.; Purwestri, R.C.; Rahmawan, F.; Herak, D. Recognition of Sago Palm Trees Based on Transfer Learning. Remote Sens. 2022, 14, 4932. https://doi.org/10.3390/rs14194932
Letsoin SMA, Purwestri RC, Rahmawan F, Herak D. Recognition of Sago Palm Trees Based on Transfer Learning. Remote Sensing. 2022; 14(19):4932. https://doi.org/10.3390/rs14194932
Chicago/Turabian StyleLetsoin, Sri Murniani Angelina, Ratna Chrismiari Purwestri, Fajar Rahmawan, and David Herak. 2022. "Recognition of Sago Palm Trees Based on Transfer Learning" Remote Sensing 14, no. 19: 4932. https://doi.org/10.3390/rs14194932