IoT Enabled Aquaponics with AI Prospects: Advancing Automated Monitoring and Sustainable Food Production

Aquaponics, a symbiotic integration of aquaculture and hydroponics, offers a sustainable solution for food production by optimizing resource utilization. This research presents the design and implementation of an IoT-enabled aquaponics system that integrates pH and temperature sensors to monitor water quality in real-time. The system ensures optimal conditions for plant and fish growth through continuous environmental assessment. Additionally, an automated fish-feeding mechanism, controlled via Arduino microcontrollers, minimizes manual intervention and enhances system efficiency. The objective of the system is to perform real time monitoring of pH and temperature to assess ammonia toxicity and dissolved oxygen. To further improve productivity, an AI-driven machine learning CNN model is suggested for early detection of plant and fish diseases using an image-capturing system. By enabling timely interventions, the proposed system aims to mitigate losses and enhance yield. The integration of these technologies not only facilitates a self-sustaining ecosystem but also contributes to environmentally conscious farming practices. This study underscores the potential of intelligent aquaponics systems in revolutionizing sustainable agriculture while addressing food security challenges.