Smart Aquaponics System

Abstract

Aquaponic systems, an integration of aquaculture and hydroponics, have untapped potential due to challenges in water quality control, nutrient deficiency detection, feed management, and the lack of comprehensive market feasibility analysis. This study introduces an innovative approach that employs sensor technology, internet of things and machine learning to effectively address these issues. The Internet of things system is developed to monitor status of water and A Neural Network training model is developed to predict water quality parameters proactively before they reach critical levels, optimizing system efficiency, yield, and sustainability. By applying a deep learning-based model, nutrient deficiencies are detected early, using a convolutional neural network that classifies crops based on nutrient content. This gap-filling measure provides valuable insights for nutrient management in aquaponic systems. A novel automated fish-feeding mechanism, leveraging machine learning, eliminates the drawbacks of manual control and enhances system performance, product quality, and profitability. Additionally, a market feasibility analysis model, absent in prior systems, helps to forecast, and reduce the risk of overselling or unsold products. These advancements contribute significantly to the commercial and sustainable potential of aquaponic farming, providing a robust framework for future research and development.