A Cloud Computing Framework for Space Farming Data Analysis
Abstract
1. Introduction
- Development of a webserver as a monitoring hub;
- Establishment of edge-to-cloud communications using AWS IoT Core;
- Data collection and transmission from 2U CubeSat using the ESP-NOW technology;
- Development and deployment of a machine vision model for germination detection using Roboflow.
1.1. Review of Related Studies
1.2. The Proposed Framework for Space Farming Data Analysis
2. Materials and Methods
2.1. Data Collection Using the ESP32-Based System Simulation
2.2. Development of the Asynchronous Local Webserver on ESP32
2.3. Establishment of Data Monitoring and Saving Using AWS IoT
2.4. Development of Roboflow Model for Germination Detection
3. Results and Discussions
3.1. Local Webserver Based on ESP32
3.2. Secure Remote Monitoring Using AWS IoT
3.3. Performance and Evaluation of the Roboflow 3.0 Model for Germination Detection
4. Conclusions and Recommendations
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Library Header | Function |
---|---|
WiFi.h | Provides functions to connect the ESP32 to a Wi-Fi network, enabling network communication. |
WebServer.h | Allows the ESP32 to act as a web server, handling HTTP requests and serving web pages and files. |
WebSocketsServer.h | Enables real-time, bidirectional communication between the ESP32 and the client via WebSockets. |
FS.h | Provides file system functionality for managing files on the ESP32’s internal flash memory. |
LittleFS.h | A lightweight file system is used for storing files (e.g., images) on the ESP32’s internal storage. |
ArduinoJson.h | Used to format and parse JSON data for easy transmission between the ESP32 and the client. |
Category | Parameter | Details |
---|---|---|
Preprocessing | Auto-Orient | Applied |
Resize | Stretch to 640 × 640 | |
Augmentations | Outputs per Training Example | 2 |
Grayscale | Apply to 100% of images | |
Saturation | Between −72% and +72% | |
Brightness | Between −38% and +38% | |
Blur | Up to 2.1px | |
Noise | Up to 1.6% of pixels |
Statistics Parameters | Training Dataset | Actual Seedling | Detected Seedling |
---|---|---|---|
Mean | 67.5 | 2.947761 | 2.947761 |
Standard Error | 3.354102 | 0.172552 | 0.172552 |
Median | 67.5 | 3 | 3 |
Mode | N/A | 3 | 3 |
Standard Deviation | 38.82654 | 1.997431 | 1.997431 |
Sample Variance | 1507.5 | 3.989732 | 3.989732 |
Kurtosis | −1.2 | −1.251445 | −1.251445 |
Skewness | 2.03 × 10−17 | −0.001745 | −0.001745 |
Range | 133 | 6 | 6 |
Minimum | 1 | 0 | 0 |
Maximum | 134 | 6 | 6 |
Sum | 9045 | 395 | 395 |
Count | 134 | 134 | 134 |
Confidence Level (98.0%) | 7.897945 | 0.40631 | 0.40631 |
Statistics Parameters | Validation Dataset | Actual Seedling | Detected Seedling |
---|---|---|---|
Mean | 152.5 | 2.65 | 2.6 |
Standard Error | 1.322876 | 0.334625 | 0.327671 |
Median | 152.5 | 3 | 3 |
Mode | N/A | 4 | 1 |
Standard Deviation | 5.91608 | 1.496487 | 1.46539 |
Sample Variance | 35 | 2.239474 | 2.147368 |
Kurtosis | −1.2 | −1.168687 | −1.021609 |
Skewness | 0 | −0.060463 | 0.00446 |
Range | 19 | 5 | 5 |
Minimum | 143 | 0 | 0 |
Maximum | 162 | 5 | 5 |
Sum | 3050 | 53 | 52 |
Count | 20 | 20 | 20 |
Confidence Level (98.0%) | 3.35942 | 0.849774 | 0.832116 |
Statistics Parameters | Testing Dataset | Actual Seedling | Detected Seedling |
---|---|---|---|
Mean | 138.5 | 4 | 4 |
Standard Error | 0.866025 | 0.944911 | 0.944911 |
Median | 138.5 | 5.5 | 5.5 |
Mode | N/A | 6 | 6 |
Standard Deviation | 2.44949 | 2.672612 | 2.672612 |
Sample Variance | 6 | 7.142857 | 7.142857 |
Kurtosis | −1.2 | −1.01584 | −1.01584 |
Skewness | 0 | −0.957864 | −0.957864 |
Range | 7 | 6 | 6 |
Minimum | 135 | 0 | 0 |
Maximum | 142 | 6 | 6 |
Sum | 1108 | 32 | 32 |
Count | 8 | 8 | 8 |
Confidence Level (98.0%) | 2.596302 | 2.832798 | 2.832798 |
Actual Seedling | Background | |
---|---|---|
predicted seedling | 395 | 1 |
background | 1 | 0 |
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Janairo, A.G.; Concepcion, R., II; Guillermo, M.; Fernando, A. A Cloud Computing Framework for Space Farming Data Analysis. AgriEngineering 2025, 7, 149. https://doi.org/10.3390/agriengineering7050149
Janairo AG, Concepcion R II, Guillermo M, Fernando A. A Cloud Computing Framework for Space Farming Data Analysis. AgriEngineering. 2025; 7(5):149. https://doi.org/10.3390/agriengineering7050149
Chicago/Turabian StyleJanairo, Adrian Genevie, Ronnie Concepcion, II, Marielet Guillermo, and Arvin Fernando. 2025. "A Cloud Computing Framework for Space Farming Data Analysis" AgriEngineering 7, no. 5: 149. https://doi.org/10.3390/agriengineering7050149
APA StyleJanairo, A. G., Concepcion, R., II, Guillermo, M., & Fernando, A. (2025). A Cloud Computing Framework for Space Farming Data Analysis. AgriEngineering, 7(5), 149. https://doi.org/10.3390/agriengineering7050149