Enhancing Nutrient Regulation in Aquaponics Using CatBoost and LightGBM

Aquaponics is an innovative farming method that integrates aquaculture, the raising of fish and other aquatic organisms, with hydroponics, the cultivation of plants in nutrient-rich water without soil. This closed-loop system operates on a natural cycle, where fish provide nutrients for plants, and plants purify the water for fish. It is increasingly recognized for its sustainability, offering benefits such as efficient nutrient recycling and reduced water consumption. However, maintaining precise nutrient balance remains a critical challenge, as imbalances can disrupt fish health, hinder plant growth, and reduce overall productivity. To address this, the study combines advanced machine learning techniques with real-time monitoring. A network of sensors tracks key parameters, such as nutrient levels and environmental conditions, while machine learning models like CatBoost and LightGBM analyze the data to predict and implement necessary adjustments. This automated approach minimizes manual intervention, enhances accuracy in nutrient management, and increases the system's scalability. The findings highlight the potential of integrating smart technologies into aquaponics to create efficient, low-maintenance farming systems. By addressing critical issues in nutrient regulation, the approach supports sustainable agriculture, contributing to resource conservation, food security, and environmental stewardship while advancing precision farming practices.