Tiny Machine Learning for Real-Time Aquaculture Monitoring: A Case Study in Morocco

TL;DR

This paper explores the use of Tiny Machine Learning on low-power edge devices to enable real-time, automated aquaculture monitoring, improving water quality management and operational efficiency in Moroccan fish farms.

Contribution

It introduces a TinyML-based system for real-time aquaculture monitoring, addressing sensor integration, algorithm design, and hardware constraints for sustainable farming.

Findings

Demonstrated feasibility of TinyML in aquaculture monitoring

Enabled real-time alerts for water quality anomalies

Potential for reducing operational costs and improving resource management

Abstract

Aquaculture, the farming of aquatic organisms, is a rapidly growing industry facing challenges such as water quality fluctuations, disease outbreaks, and inefficient feed management. Traditional monitoring methods often rely on manual labor and are time consuming, leading to potential delays in addressing issues. This paper proposes the integration of low-power edge devices using Tiny Machine Learning (TinyML) into aquaculture systems to enable real-time automated monitoring and control, such as collecting data and triggering alarms, and reducing labor requirements. The system provides real-time data on the required parameters such as pH levels, temperature, dissolved oxygen, and ammonia levels to control water quality, nutrient levels, and environmental conditions enabling better maintenance, efficient resource utilization, and optimal management of the enclosed aquaculture space. The system enables alerts in case of anomaly detection. The data collected by the sensors over time can serve for important decision-making regarding optimizing water treatment processes, feed distribution, feed pattern analysis and improve feed efficiency, reducing operational costs. This research explores the feasibility of developing TinyML-based solutions for aquaculture monitoring, considering factors such as sensor selection, algorithm design, hardware constraints, and ethical considerations. By demonstrating the potential benefits of TinyML in aquaculture, our aim is to contribute to the development of more sustainable and efficient farming practices.