This project integrates artificial intelligence and hardware to develop a smart home-based aquaponics system. By leveraging AI-driven automation, we aim to optimize the growth of both fish and plants in a self-sustaining environment. This system utilizes fish waste as organic fertilizer, creating a natural ecosystem that enhances efficiency and sustainability.

• AI-Driven Monitoring & Control
  AI optimizes water quality, nutrient levels, and environmental conditions in real time.
• Closed-Loop Ecosystem
  Fish feces are processed into natural fertilizer, providing essential nutrients to plants.
• Smart Sensor Integration
  Sensors measure pH, ammonia, nitrate levels, and temperature, ensuring ideal conditions.
• Automated Water Filtration
  A filtering mechanism cleans water and cycles it back into the fish tank, promoting sustainability.
• Machine Learning for Yield Optimization
  AI models analyze data to enhance fish growth and improve crop production.

• AI Module: Uses machine learning models to analyze environmental data and optimize system parameters.
• Sensor Data Processing: Collects real-time data on water quality, temperature, and nutrient levels.
• Automated Control System: Adjusts feeding, water flow, and other parameters based on AI predictions.
• Aquaponics System: Houses fish and plants in a closed-loop setup that recycles nutrients efficiently.

• Microcontrollers & Sensors: Raspberry Pi / Arduino, pH sensors, ammonia detectors, temperature sensors.
• AI Processing Unit: NVIDIA Jetson Nano or similar AI-capable hardware.
• Water Pump & Filtration System: Maintains clean water and efficient nutrient cycling.
• LED Grow Lights: Provides optimal lighting for plant growth.

1. Fish are fed and produce waste.
2. Waste is broken down by beneficial bacteria into nutrients.
3. Plants absorb the nutrients, purifying the water.
4. Clean water is cycled back to the fish tank.
5. AI continuously optimizes the process based on sensor data.

• Water Quality Prediction: Detects potential issues before they affect fish or plants.
• Growth Rate Optimization: Uses past data to improve fish and crop yield.
• Energy Efficiency Management: Optimizes pump cycles and lighting schedules.

• IoT Dashboard for remote monitoring and control.
• Integration with renewable energy sources (e.g., solar power).
• Enhanced AI models for better decision-making.

This project is licensed under the MIT License.

The project is dependent on the Project X.