Smart Aquaponics: Forecasting Maintenance Needs Using Artificial Neural Networks and Time Series Analysis
Muqaddam Aaqil Sheriff, Kiruba Wesley, Saranya. V, Senthil Pandi S
Abstract
The sustainable method known as aquaponics, which blends hydroponics and aquaculture, relies on a careful balancing act between biological and environmental factors. Effective maintenance is essential to system stability, but traditional approaches are reactive and resource-intensive. This paper presents a clever aquaponics framework that anticipates maintenance needs using machine learning. Our model analyses real-time sensor data, such as water temperature, pH, ammonia levels, and dissolved oxygen, to predict potential system failures and schedule maintenance tasks before performance deteriorates. We evaluate several supervised learning algorithms to identify the optimal approach in terms of prediction accuracy and operational efficiency. The findings demonstrate that machine learning can significantly reduce system downtime, improve yield quality, and enable intelligent, scalable, and low-maintenance aquaponics systems.