A Cloud Computing Framework for Space Farming Data Analysis
This study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/5/149 |
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| _version_ | 1849711085712572416 |
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| author | Adrian Genevie Janairo Ronnie Concepcion Marielet Guillermo Arvin Fernando |
| author_facet | Adrian Genevie Janairo Ronnie Concepcion Marielet Guillermo Arvin Fernando |
| author_sort | Adrian Genevie Janairo |
| collection | DOAJ |
| description | This study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted in a mean average precision (mAP), precision, and recall of 99.5%, 99.9%, and 100.0%, respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real time through a web dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming. |
| format | Article |
| id | doaj-art-d2e79e92288f46a9b47576fabe1cf23c |
| institution | DOAJ |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-d2e79e92288f46a9b47576fabe1cf23c2025-08-20T03:14:42ZengMDPI AGAgriEngineering2624-74022025-05-017514910.3390/agriengineering7050149A Cloud Computing Framework for Space Farming Data AnalysisAdrian Genevie Janairo0Ronnie Concepcion1Marielet Guillermo2Arvin Fernando3Gokongwei College of Engineering, De La Salle University, Manila 1004, PhilippinesGokongwei College of Engineering, De La Salle University, Manila 1004, PhilippinesGokongwei College of Engineering, De La Salle University, Manila 1004, PhilippinesGokongwei College of Engineering, De La Salle University, Manila 1004, PhilippinesThis study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted in a mean average precision (mAP), precision, and recall of 99.5%, 99.9%, and 100.0%, respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real time through a web dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming.https://www.mdpi.com/2624-7402/7/5/149cloud computingcomputer visioncrop germinationCubeSatInternet of Space Thingsmicrogravity |
| spellingShingle | Adrian Genevie Janairo Ronnie Concepcion Marielet Guillermo Arvin Fernando A Cloud Computing Framework for Space Farming Data Analysis AgriEngineering cloud computing computer vision crop germination CubeSat Internet of Space Things microgravity |
| title | A Cloud Computing Framework for Space Farming Data Analysis |
| title_full | A Cloud Computing Framework for Space Farming Data Analysis |
| title_fullStr | A Cloud Computing Framework for Space Farming Data Analysis |
| title_full_unstemmed | A Cloud Computing Framework for Space Farming Data Analysis |
| title_short | A Cloud Computing Framework for Space Farming Data Analysis |
| title_sort | cloud computing framework for space farming data analysis |
| topic | cloud computing computer vision crop germination CubeSat Internet of Space Things microgravity |
| url | https://www.mdpi.com/2624-7402/7/5/149 |
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