Optimizing Light Detection with Photodiode Sensor Arrays using Linear Regression

Photodiode sensors are widely used in various applications such as light intensity measurement, optoelectronic devices, and automation. In improving the quality of measurement and automation systems, more sophisticated technology is needed such as photodiode sensor arrays, which allow more accurate data collection from multiple sensors simultaneously. This research aims to design a photodiode sensor array with high sensitivity. The system design consists of six photodiode sensors combined with a summing amplifier circuit and a non-inverting amplifier as a signal conditioner which is then processed by a microcontroller. After that, the linear regression function is determined through the calibration process and experiments carried out. Two linear regression functions are obtained and implemented in two operating modes: normal mode and sensitive mode. Experimental results yield two linear regression functions applied to a photodiode sensor array in normal and sensitive modes. Normal mode shows 82.50% accuracy with a 36.69% coefficient of variation, while sensitive mode boasts 94.05% accuracy and 49.81% coefficient of variation. Both modes cater to different light conditions, with sensitive mode excelling in detecting light intensity. Linear regression implementation proves precise and accurate for light detection.