# Prototype of Hydrochemical Regime Monitoring System for Fish Farms

## TL;DR

This paper introduces a smart, energy-efficient system for monitoring water quality in fish farms using sensors and predictive algorithms.

## Contribution

A novel autonomous hydrochemical monitoring system using LoRaWAN and predictive modeling for aquaculture.

## Key findings

- The system can predict dissolved oxygen levels with an RMSE of 0.104 mg/L using minimal sensors.
- The design supports scalable, energy-efficient monitoring with reliable data fusion and forecasting.
- Preliminary tests confirm the system's potential for real-time hydrochemical assessment in aquaculture.

## Abstract

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with
RMSE
= 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments.