本團隊運用了人工智慧的尖端科技，並實際應用於水產養殖之監控，能透過水下鏡頭及各式感測器的輔助下，讓我們在原本混濁的水下能夠拍攝出清晰的影像畫面，並利用機器學習的技術來辨識出魚蝦及餌料。此外，可依據各種環境參數（水質混濁程度、溫度等），動態調整嵌入在感測裝置的神經網路模型硬體加速器運算相關參數，達到低功耗省電的即時魚蝦和餌料辨識偵測運算系統，並可根據運算結果，自動開啟智慧監控系統之其他周邊控制設備（如增氧機、自動餵餌機等）。

This project applies cutting-edge artificial intelligence technology to the smart aquaculture monitor system . With the aid of underwater lenses and various sensors and using machine learning techniques, we are able to generate clear images under the original turbid water and identify fish, shrimp, and bait in acqua-culture ponds. In addition, according to various environmental parameters (remained battery energy, water turbidity, temperature, etc.), the neural network hardware accelerator embedded in the edge devices (camera/sensors) can be dynamically adjusted to achieve low power and real-time processing speed for fish/shrimp/bait detection in the underwater monitor system. Furthermore, the computation results can be used to activate the peripheral control equipment in the smart underwater aqua-culture system.