Precision agriculture is widely applied in indoor farming to optimize resource use and
improve sustainability. Spectral technology has limitations in operation in plant health monitoring in
indoor farming. A concept of plant physiology, plant electrical signals, is able to be developed as a basic
principle in plant health monitoring systems. This research investigates the design of a plant monitoring
system based on plant electrical signals. The system integrates Ag wire electrodes for acquiring plant
electrical signals. Low-pass filters and operational amplifiers are utilized signal processing, while
microcontrollers and data loggers handle data storage and analysis. Calibration for this system needs a
function generator. The calibration result is analyzed using statistical methods such as MAPE. The
system will apply various advanced analysis techniques such as time domain, frequency domain, and
machine learning methods. The goal of such analysis is to improve early detection of plant stress
contributing to more efficient crop management in indoor farming systems. This monitoring system
potentially improves plant health and supports sustainable agricultural practices. By leveraging the rapid
response of plant electrical signals to environmental changes, the system is the first step for optimizing
plant growth by providing real-time monitoring and environmental recommendations.
