Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area and Image Acquisition
2.1.1. Research Area
2.1.2. Data Information and Experimental Environment
2.1.3. Methodological Models Used in the Study
2.2. Convolution Neural Network
2.2.1. AlexNet
2.2.2. VGG
2.2.3. GoogleNet
2.2.4. ResNet
2.2.5. EfficientNet
2.3. Traditional Methods
2.4. Evaluation of Precision and Efficiency
3. Results
3.1. AlexNet Results
3.2. VGG Results
3.3. GoogleNet Results
3.4. ResNet Results
3.5. Efficientnet Results
3.6. Convolutional Neural Network Classification Efficiency Summary
3.7. Results of Traditional Classification Methods
3.8. Comparative Summary of Results for Different Methods
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Model | Average Length of Time Per Training Session (s) | Number of Trainings | Test Duration (s) | Total Duration (s) |
---|---|---|---|---|
AlexNet | 12 | 70 | 6 | 846 |
VGG | 19 | 180 | 7 | 3427 |
GoogleNet | 21 | 70 | 8 | 1478 |
ResNet | 23 | 30 | 8 | 698 |
EfficientnetV2 | 10 | 460 | 11 | 4611 |
Model | Precision (%) | Recall (%) | F1-Score (%) | Accuracy (%) |
---|---|---|---|---|
BP | 71.99 | 87.74 | 79.09 | 80.89 |
k-NN | 75.78 | 92.43 | 83.28 | 84.46 |
PCA | 58.70 | 94.89 | 72.53 | 77.72 |
LDA | 73.44 | 90.35 | 81.02 | 83.07 |
SVM | 68.74 | 93.40 | 79.19 | 81.58 |
Model | Training Duration (s) | Test Duration (s) | Total Duration (s) |
---|---|---|---|
BP | 2378 | 12 | 2390 |
k-NN | 178 | 11 | 189 |
PCA | 338 | 11 | 349 |
LDA | 569 | 12 | 581 |
SVM | 127 | 11 | 138 |
Model | Precision (%) | Recall (%) | F1-Score (%) | Accuracy (%) | Total Time |
---|---|---|---|---|---|
AlexNet | 96.81 | 87.57 | 91.96 | 91.58 | 846 |
VGG | 90.98 | 87.98 | 89.46 | 89.41 | 3427 |
GoogleNet | 87.10 | 95.23 | 90.98 | 90.99 | 1478 |
ResNet | 92.47 | 94.29 | 93.37 | 93.27 | 698 |
EfficientnetV2 | 96.35 | 91.44 | 93.83 | 93.47 | 4611 |
BP | 71.99 | 87.74 | 79.09 | 80.89 | 2390 |
k-NN | 75.78 | 92.43 | 83.28 | 84.46 | 189 |
PCA | 58.70 | 94.89 | 72.53 | 77.72 | 349 |
LDA | 73.44 | 90.35 | 81.02 | 83.07 | 581 |
SVM | 68.74 | 93.40 | 79.19 | 81.58 | 138 |
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Wu, J.; Li, X.; Shi, Z.; Li, S.; Hou, K.; Bai, T. Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery. Forests 2024, 15, 165. https://doi.org/10.3390/f15010165
Wu J, Li X, Shi Z, Li S, Hou K, Bai T. Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery. Forests. 2024; 15(1):165. https://doi.org/10.3390/f15010165
Chicago/Turabian StyleWu, Jingming, Xu Li, Ziyan Shi, Senwei Li, Kaiyao Hou, and Tiecheng Bai. 2024. "Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery" Forests 15, no. 1: 165. https://doi.org/10.3390/f15010165
APA StyleWu, J., Li, X., Shi, Z., Li, S., Hou, K., & Bai, T. (2024). Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery. Forests, 15(1), 165. https://doi.org/10.3390/f15010165