A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones
Abstract
:1. Introduction
2. Materials and Methods
2.1. Proposed Object Detection Framework
2.2. Proposed High-Level System Architecture Incorporating the Framework
- Aerial LiDaR mapping needs to be performed in the surveyed field area using UAVs as a preprocessing step to create a 3D survey representation. LiDaR instruments can measure the Earth’s surface at sampling pulse rates greater than 200 KHz, offering a point cloud of highly accurate georeferenced elevation points.
- UAVs’ aerial RTK GPS receivers that receive the RTCM correction stream must be utilized. Then, point locations with 1–3 cm accuracy in real time (nominal) must be provided to georeferenced maps or APIs.
- The fundamental vertical accuracy (FVA) of the open data areas measured must be at a high confidence level above 92%.
- The absolute point altitude accuracies of the acquired LiDaR system elevation points must be in the range of 10–20 cm to offer drone flights close to the vines.
- UAV mappings of vegetation areas need to be performed with the concurrent utilization of multispectral cameras in the near-infrared band of 750–830 nm so as to detect and exclude non viticulture areas of low or high refractivity by performing post processing NDVI metric calculations [49] on map layers.
- Using EU-DEM [11] as a base altitude reference, appropriate altitude corrections must be made to the GIS path-planning map grid. Then, the surveying drones need to perform periodic HTTP requests to acquire sampling altitude corrections. If viticulture fields’ surface altitude variances are no more than 0.5–1 m in grid surface areas of at least 1–5 km, then with the exception of dense plantation areas (such as forests, provided by NDVI measurements), the altitude values can be set as a fixed vertical parameter value of drone altitude offset for the specific survey areas.
3. Experimental Scenario
Evaluation Metrics
4. Experimental Results and Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AS | application server |
CNN | convolutional neural network |
CPU | central processing unit |
DSS | decision support system |
EIRP | equivalent isotropic radiated power |
EXIF | exchangeable image file format |
GPS | Global Positioning System |
FVA | fundamental vertical accuracy |
HDFS | Hadoop Distributed File System |
HTML | hypertext markup language |
IoU | intersection over union |
LTE | telecommunication systems’ long-term evolution (4G) |
NTP | network time protocol |
PSRAM | pseudostatic memory - RAM |
R-CNN | Regions with CNN features |
RPN | Region Proposal Network |
RAM | random access memory |
RTC | real-time clock |
RTCM | Radio Technical Commission for Maritime |
PV | photovoltaic cell |
SSD | Single Shot Detector, object detection algorithm |
SPI | synchronous peripheral interface |
UID | unique identification |
Vitis Vinifera | common grape vine varieties in EU |
Plasmopara Viticola (P. Viticola) | downy mildew |
UAV | unoccupied aerial vehicle |
YOLO | You Only Look Once object detection algorithm |
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Trained Model | JPEG Compressed Image Input Sizes (W × H, MB) | No Trainable Parameters | Estimated Model Sizes (MB) |
---|---|---|---|
ResNet-152 | (640 × 640, 0.49) | 199,874,714 | 980 |
ResNet-101 | (640 × 640, 0.49) | 184,231,066 | 704 |
YOLOv3-Darknet | (640 × 640, 0.49) | 84,364,442 | 338.5 |
EfficientNet-b0 | (640 × 640, 0.49) | 84,141,782 | 337 |
FRCNN-VGG16 | (640 × 640, 0.49) | 43,877,530 | 168 |
ResNet-50 | (640 × 640, 0.49) | 41,304,286 | 159 |
YOLOv5-small (YOLOv5s) | (640 × 640, 0.49) | 7,468,160 | 101.5 |
SqueezeNet | (4128 × 4128, 3.6) | 29,876,890 | 98 |
ViTDet-tiny | (4128 × 4128, 3.6) | 22,786,382 | 87 |
MobileNetV3 | (4128 × 4128, 3.6) | 18,935,354 | 73 |
YOLOv8-nano (YOLOv8n) | (4128 × 4128, 3.6) | 3,151,904 | 29.8 |
Model | -20 Epochs | -50 Epochs | -100 Epochs |
---|---|---|---|
ResNet-152 | 0.9934 | 0.995 | 0.9951 |
ResNet-101 | 0.989 | 0.995 | 0.995 |
ResNet-50 | 0.65 | 0.92 | 0.9949 |
EfficientNet-b0 | 0.29 | 0.68 | 0.868 |
FRCNN-VGG16 | 0.9944 | 0.9949 | 0.995 |
YOLOv3-Darknet | 0.26 | 0.78 | 0.94 |
YOLOv5-small (YOLOv5s) | 0.92 | 0.92 | 0.96 |
SqueezeNet | 0.558 | 0.783 | 0.981 |
ViTDet-tiny | 0.16 | 0.55 | 0.901 |
MobileNetV3 | 0.37 | 0.64 | 0.99 |
YOLOv8-nano (YOLOv8n) | 0.86 | 0.91 | 0.94 |
Model | Achieved Mean FPS | |
---|---|---|
ResNet-152 | 0.262 | 0.9476 |
ResNet-101 | 0.321 | 0.9411 |
ResNet-50 | 0.511 | 0.940 |
FRCNN-VGG16 | 0.404 | 0.9106 |
YOLOv3-Darknet | 0.591 | 0.633 |
YOLOv5-small (YOLOv5s) | 1.28 | 0.812 |
SqueezeNet | 1.645 | 0.789 |
ViTDet-tiny | 0.517 | 0.622 |
MobileNetV3 | 3.24 | 0.876 |
YOLOv8-nano (YOLOv8n) | 2.08 | 0.84 |
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Kontogiannis, S.; Konstantinidou, M.; Tsioukas, V.; Pikridas, C. A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones. Information 2024, 15, 178. https://doi.org/10.3390/info15040178
Kontogiannis S, Konstantinidou M, Tsioukas V, Pikridas C. A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones. Information. 2024; 15(4):178. https://doi.org/10.3390/info15040178
Chicago/Turabian StyleKontogiannis, Sotirios, Myrto Konstantinidou, Vasileios Tsioukas, and Christos Pikridas. 2024. "A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones" Information 15, no. 4: 178. https://doi.org/10.3390/info15040178
APA StyleKontogiannis, S., Konstantinidou, M., Tsioukas, V., & Pikridas, C. (2024). A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones. Information, 15(4), 178. https://doi.org/10.3390/info15040178