ABSTRACT
Due to COVID-19 pandemic, the expenditures on pellets and feeds in broiler and fish industries increase every year, leading to price overshoots in various agricultural products. Azolla is an emerging protein source alternative for tilapia and other livestock breeders that is known for its fast reproduction. This study aims to enhance the yield production of Azolla ponds in Nevalga Farm, Brgy. Sala, City of Cabuyao, Laguna by employing wireless sensor network (WSN) technology and predictive machine-learning (ML) methods. LoRa-based WSN was designed to measure the parameters that affect the growth and reproduction of Azolla. Throughout the 24-day monitoring period, the average received signal strength indication (RSSI) and signal-to-noise ratio (SNR) of the packets from the three sensing nodes ranged from -50.86 dBm to -71.39 dBm and 8.92 dB to 9.81 dB, respectively. A total of 3582 data sets were obtained during the observation. Among the three regression ML models used, K-Nearest Neighbor algorithm outperformed Linear Regression and Support Vector Machine in predicting Azolla quantity parameters on both training and validation datasets by yielding the smallest values of root mean square error (RMSE) and absolute error on the seven quantity indicators and achieving squared correlation that varied from 0.935 to 0.997. © 2022 IEEE.