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Flood detection is difficult in rural areas with little or no monitoring infrastructure. Smaller streams and flood-prone regions often remain unmonitored, which leaves communities vulnerable. Commercial systems cost much and use proprietary designs, so many communities cannot use them. This work presents AquaCam, a low-cost and open-source flood detection system that uses a Raspberry Pi and a camera to measure stream water levels automatically. AquaCam captures images and trains a lightweight convolutional neural network (YOLOv8) with the collected data. The model learns to recognize water in natural backgrounds and measure water height. To test whether AquaCam can adapt to new environments, we evaluated the trained model at a different site with no retraining. The system still identified water levels accurately. This shows that the approach is practical and generalizable. AquaCam moves flood detection toward being affordable, accessible, and adaptable for the communities that need it.