Article
AI-Based Aquaculture Recommendation System Using Machine Learning
Aquaculture productivity and sustainability are strongly influenced by water-quality conditions, making continuous monitoring and timely management essential. Conventional monitoring approaches often depend on periodic measurements and manual interpretation, limiting their ability to identify rapidly changing conditions. Recent advances in machine learning provide opportunities for water-quality prediction, forecasting, anomaly identification, and intelligent aquaculture management. This survey reviews machine-learning and deep-learning approaches for aquaculture water-quality assessment, considering parameters such as pH, temperature, dissolved oxygen, turbidity, and related environmental indicators. Existing approaches including traditional machine learning, neural networks, hybrid models, time-series forecasting, and sensor-assisted monitoring are examined with respect to their capabilities and limitations. Based on the surveyed literature, an integrated AI-based aquaculture recommendation framework is presented that connects data preprocessing, waterquality prediction, anomaly detection, and management-oriented recommendations. The survey further discusses evaluation requirements, research challenges, and future directions toward intelligent, adaptive, and sustainable aquaculture systems.
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