Timely detection of aqueous analytes is essential for informed decision-making in agriculture, particularly in controlled environments such as greenhouses, vertical farms, and space-based cultivation systems. Traditional aqueous sensing technologies typically depend on single-point measurements, capturing data at fixed times and locations. This constraint limits their ability to detect analytes that may emerge elsewhere in the system or at different intervals. In response, we present an innovative, low-cost sensor platform featuring a 3D-printed housing integrated with a mass-manufactured, nanotextured diffraction surface. This housing includes a lighting element and a camera sensor to enable continuous image-based analysis of water quality. Designed for seamless integration into hydroponic lines, the sensor units are both affordable and easily reproducible, allowing for deployment at multiple points within a system to provide real-time monitoring. Our results demonstrate the sensor’s capability to detect and quantify a range of aqueous analytes—including visible and UV-absorbing compounds, dust particles, and various microalgae species. Our sensor performs similarly to a commercial UV-Vis instrument, often used to measure contaminants present in water. Specifically, calibration curves derived from increased concentrations of a simulated contaminant had a calculated R2 value of 0.998 from the UV-Vis instrument and 0.996 from our device. Performance is further enhanced through machine learning algorithms that improve detection and classification. This scalable and cost-effective sensing system offers a practical solution for real-time water quality assessment across controlled environment agriculture, greenhouse systems, and extraterrestrial farming applications—particularly in contexts where labor is limited and rapid response is critical.
