Cloud Computing Framework for Space Farming Data Analysis

The study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. The research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the ESP-NOW technology, communications between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through 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 to an 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 done 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.