Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms
Abstract
:1. Introduction
- (1)
- We present a new method to identify pine wood nematode disease with high accuracy using UAV images.
- (2)
- An SIRM is used to retain spatial information to obtain low-level features, and a CIM can expand the receptive field to obtain high-level features.
- (3)
- The method can also be used to identify trees with single pine wood nematode disease.
2. Methods
2.1. The Network Structure
2.2. Spatial Information Retention Module
2.3. Context Information Module
2.4. Evaluation Index
3. Data and Experiments
3.1. Data Information
3.2. Dataset Details
4. Results and Analysis
4.1. Identification of Pine Wood Nematode Disease
4.2. Comparisons with Related Networks
4.3. Comparison with Ablation Experiments
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Images | Huangshan-1 | Huangshan-2 | Wuhan | Yantai |
---|---|---|---|---|
Locate | Anhui Province | Anhui Province | Hubei Province | Shandong Province |
Flight height | 150 m | 150 m | 200 m | 160 m |
Spatial resolution | 0.1 m | 0.1 m | 0.125 m | 0.1 m |
Flight date | 2019-08-12 | 2019-08-13 | 2019-8-21 | 2019-10-16 |
Centre coordinates | E118°19′12″ N29°47′19″ | E118°18′44″ N29°47′33″ | E114°29′36″ N27°37′03″ | E121°55′18″ N37°27′30″ |
Wavelength (nm) | Blue: 475 nm Green: 560 nm Red: 670 nm RedEdge: 720 nm Near IR: 840 nm |
Images | Total Visual Interpretation | Total Number of Identification | Correct Number | Overall Accuracy | Precision | Recall |
---|---|---|---|---|---|---|
Huangshan-1 | 186 | 203 | 174 | 80.93% | 0.85 | 0.94 |
Wuhan | 515 | 521 | 453 | 77.70% | 0.87 | 0.88 |
Yantai | 1005 | 1090 | 927 | 79.37% | 0.85 | 0.92 |
Mean | - | - | - | 79.33% | 0.86 | 0.91 |
Images | Total Number of Checkpoint | Correct Number | Recall | Missing Alarm |
---|---|---|---|---|
Huangshan-1 | 59 | 55 | 0.93 | 0.07 |
Images | Models | Total Visual Interpretation | Number of Identification | Correct Number | Accuracy | Precision | Recall |
---|---|---|---|---|---|---|---|
Huangshan-1 | SCANet | 186 | 203 | 174 | 80.93% | 0.86 | 0.94 |
Deeplab V3+ | 186 | 173 | 146 | 68.54% | 0.84 | 0.78 | |
HRNet | 186 | 175 | 152 | 72.73% | 0.87 | 0.82 | |
DenseNet | 186 | 189 | 160 | 74.42% | 0.85 | 0.86 | |
Wuhan | SCANet | 515 | 521 | 453 | 77.70% | 0.87 | 0.88 |
Deeplab V3+ | 515 | 801 | 385 | 41.53% | 0.48 | 0.75 | |
HRNet | 515 | 460 | 326 | 50.23% | 0.71 | 0.63 | |
DenseNet | 515 | 691 | 405 | 50.56% | 0.59 | 0.79 | |
Yantai | SCANet | 1005 | 1090 | 927 | 79.37% | 0.85 | 0.92 |
Deeplab V3+ | 1005 | 1098 | 787 | 59.80% | 0.72 | 0.78 | |
HRNet | 1005 | 900 | 617 | 47.80% | 0.69 | 0.61 | |
DenseNet | 1005 | 1340 | 660 | 39.17% | 0.49 | 0.65 |
Images | Models | Total Visual Interpretation | Number of Identification | Correct Number | Accuracy | Precision | Recall |
---|---|---|---|---|---|---|---|
Huangshan-1 | SCANet | 186 | 203 | 174 | 80.93% | 0.86 | 0.94 |
SNet | 186 | 297 | 169 | 53.82% | 0.57 | 0.91 | |
CANet | 186 | 126 | 65 | 26.31% | 0.58 | 0.35 | |
Wuhan | SCANet | 515 | 521 | 453 | 77.70% | 0.87 | 0.88 |
SNet | 515 | 711 | 462 | 60.47% | 0.65 | 0.90 | |
CANet | 515 | 367 | 205 | 30.28% | 0.56 | 0.40 | |
Yantai | SCANet | 1005 | 1090 | 927 | 79.37% | 0.85 | 0.92 |
SNet | 1005 | 1368 | 900 | 61.09% | 0.66 | 0.90 | |
CANet | 1005 | 565 | 326 | 26.21% | 0.57 | 0.32 |
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Qin, J.; Wang, B.; Wu, Y.; Lu, Q.; Zhu, H. Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sens. 2021, 13, 162. https://doi.org/10.3390/rs13020162
Qin J, Wang B, Wu Y, Lu Q, Zhu H. Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sensing. 2021; 13(2):162. https://doi.org/10.3390/rs13020162
Chicago/Turabian StyleQin, Jun, Biao Wang, Yanlan Wu, Qi Lu, and Haochen Zhu. 2021. "Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms" Remote Sensing 13, no. 2: 162. https://doi.org/10.3390/rs13020162
APA StyleQin, J., Wang, B., Wu, Y., Lu, Q., & Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sensing, 13(2), 162. https://doi.org/10.3390/rs13020162