Title:

AI-Powered Smart Agriculture System for Optimized Plant Care and Growth Management

Water overuse in agriculture is still a very big issue, with inefficient irrigation methods that lead to significant waste. To take care of this, we designed an affordable and smart farming system that uses real-time monitoring and reinforcement learning to optimize plant care. The system combines off the shelf components such as a microcontroller, pH, temperature, and humidity sensors, and a water pump, all integrated to track changes in the soil and adapt the watering behavior based on those changes. A Q-learning (a type of reinforcement learning) model processes live data and dynamically adjusts the irrigation schedules to minimize water waste. When dry conditions or changes in climate are detected, the system initiates water delivery, and ends up learning from each even to improve future decisions it has to make. This solution provides a path towards precision agriculture, with the next steps focusing on a big field deployment, some potential hardware refinement, and looking into vision-based enhancements using a camera.

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