Open Access

Abdul Qadir Mughal

• This thesis presents the design and construction of a prototype automated plant monitoring system that integrates hyperspectral imaging with a custom linear motion structure for greenhouse applications. Controlled by an Arduino-driven stepper motor, the system positions two hyperspectral cameras (covering visible–near-infrared and short-wave infrared ranges) and a stereo RGB camera along a linear guide to capture consistent and repeatable measurements. Calibration and data acquisition are managed by specialized software, enabling detection of early drought stress indicators such as pigment and water content changes before visible symptoms occur. Assembled and tested at Fraunhofer IGD, the prototype demonstrated reliable operation and consistent spectral data collection, providing a versatile foundation for future enhancements including automated workflows, machine learning–based analysis, and application in diverse agricultural settings.