Water Quality Monitoring for Smart Farming Using Machine Learning Approach

Abstract

Water quality in fish farming environments has been a topic of research investigation for numerous years. While most studies have concentrated on managing water quality in fish ponds, there is a lack of research on implementing these practices on a commercial scale. Maintaining good water quality helps prevent disease, stress, and death in fish, resulting in higher yields and profits in fish farming operations. In our study, we gathered weekly data from two fish ponds in the Lintangsongo smart farming area over six months. To deal with the limited dataset, we utilized methods for reducing dimensionality, like the pairwise comparison of correlation matrices to eliminate the highest correlated predictors. We used techniques of feature selection, including XGBoost classification, and apart from that, we used Recursive Feature Elimination (RFE) to determine the importance of features. This analysis identified ammonium and calcium as the top two predictors. These nutrients played a vital role in maintaining the paired cultivation system and promoting the robust development of Nile tilapia fish and water spinach. This process of detecting and distributing nutrients persists until the desired quantities of ammonium and calcium are reached. During each cycle, 0.7 g of ammonium sulfate and calcium nitrate are distributed, and the nutrient levels are assessed. Vernier sensors were employed for assessing nutrient values, and a system of actuators was integrated to supply the necessary nutrients to the smart farming environment using the closed-loop concept. This research investigates water quality management practices in fish farming, assesses their impact on fish health and profitability, identifies key water quality predictors, and implements a closed-loop system for nutrient delivery.