Monitoring and forecasting volcanic processes are crucial for detecting, characterising, and assessing associated hazards. However, advanced monitoring tools, such as thermal cameras, can be expensive and unavailable in many monitoring networks. To address this, a cost-effective visible wavelength camera network has been developed to capture high resolution imagery for volcano monitoring and research. This network provides continuous, automated image acquisition from different locations around a volcanic edifice. Each camera node is equipped with a Raspberry Pi computer board, one or two Raspberry Pi Camera Module 3 cameras, and a waterproof and dustproof box for deployment in challenging outdoor environments, with an estimated cost of approximately EUR 152–257 per unit, depending on the hardware configuration. The system supports diverse configurations, including adjustable capture intervals and resolutions, to meet various observational needs. The network adapts its operation based on environmental conditions, automatically selecting the shutter speed during daylight and using specific settings at night. Acquired data can be transferred in near real time to a server for archival and, potentially, analysis for monitoring purposes. In this study, we present a long-term camera monitoring network deployed at Mt. Etna, together with short-term deployments at Stromboli, the Geneva Jet d'Eau as a calibrated experiment, and laboratory experiments. These case studies demonstrate the long-term reliability of the system for capturing images over extended periods under variable lighting and weather conditions, including high temperatures, snow, rain, and tephra fallout, as well as its suitability as a temporary observation tool for field campaigns or laboratory setups. By using open source hardware and software, this camera network provides a flexible and accessible tool for volcano monitoring and research applications. When paired with cellular connectivity and solar panels or battery packs, the system could be integrated into multisensor monitoring networks, including in remote areas and challenging outdoor environments. Although developed for volcano monitoring, the framework can be adapted to other natural and environmental processes requiring persistent or high temporal resolution visual observations.