DEVELOPMENT OF HUMIDITY MONITORING SYSTEM IN GREENHOUSE WITH ELECTROMAGNETIC X BAND AND ARTIFICIAL NEURAL NETWORKS

This paper presents a humidity monitoring system with X band electromagnetic transmission. The verification is performed by comparing the gain and phase difference of intermediate frequency between 10.2 GHz and 10.4 GHz. Measurement data are analyzed to classify relative humidity levels and make decisions with ANNs. The system is simulated with electromagnetic field simulation software to analyze the ability of humidity monitoring. The structure from the simulation is developed to be a prototype system, including transmitter and receiver modules. Each module consists of an antenna, a frequency synthesizer, and a frequency mixer. The different operation frequencies of the two modules are −200 MHz and +200 MHz. The obtained intermediate frequency by mixing signals from each module is introduced into the circuit to find the gain and phase difference to compare with a relative humidity level. Humidity monitoring experiment is set in a closed plastic box to control the environment. The relative humidity level is from 55% to 95%. The decrease in gain is associated with increased relative humidity. Results found that the phase difference decreases clearly at the relative humidity from 75% to 95%. Both gain and phase difference data are used to train ANNs to optimize ANNs structure. Data are divided into 50% for training and 50% for testing. The proposed ANNs structure with a learning rate of 0.05 provides 98.8% accuracy. The optimized ANNs structure is composed of two input nodes, eight hidden nodes, and four output nodes. Four output nodes represent the relative humidity in 11 levels. The simulated and experimental results show that the system is able to monitor humidity effectively for applying in the greenhouse.