The Aquaponics Farm Automation team focused on creating a robotic farming device that scanned an aquaponics bed of plants and analyzed the collected data. The collected data allowed us to identify plants and predict future growth. The previous team who worked on the project had already built the farm-bot and had started identification of the plants.

This project’s purpose was to address the growing need for better short-term solar energy planning by providing clear and reliable predictions. We developed a machine learning model to predict solar coverage of a given area for the next 5 to 10 minutes. The model was a convolutional neural network that took an image of overhead weather and solar radiation at the same time as inputs. Based on the input data, the model learned what features in the image related to varying solar radiation levels and then output a prediction of future solar radiation.

The ability to analyze the current-voltage (IV) characteristics of electronic components is critical for researchers, educators and engineers. Understanding these characteristics helps design reliable circuits and ensures proper functionality. We aimed to develop a compact, user-friendly IV Curve Tracer to simplify this process while maintaining accuracy and affordability.

This project involved building a robot capable of moving autonomously for the SoutheastCon 2025 robotics competition. The goal was to make a robot that could move around an arena, collect astral materials, put them into containers, position the containers on the correct pad, and place a team beacon in the beacon mast—all under three minutes. We earned more points by completing tasks correctly.