The application of convolutional neural networks (CNNs) to challenging visual recognition tasks has been shown to be highly effective and robust compared to traditional machine vision techniques. The recent development of small, powerful GPUs has enabled embedded systems to incorporate real-time, CNN-based, visual inference. Agriculture is a domain where this technology could be hugely advantageous. One such application within agriculture is precision spraying where only weeds are targeted with herbicide. This approach promises weed control with significant economic and environmental benefits from reduced herbicide usage. While existing research has validated that CNN-based vision methods can accurately discern between weeds and crops, this paper explores how such detections can be used to actuate a prototype precision sprayer that incorporates a CNNbased weed detection system and validates spraying performance in a simplified scenario.