AI-Enhanced Autonomous Systems for Detecting and Managing Soil Nutrient Imbalances with IoT Integration

To maximize agricultural production and secure sustainable farming methods, it is essential to control soil nutrient imbalances effectively. This research presents an autonomous system enhanced with artificial intelligence (AI). It uses neural networks and Internet of Things (IoT) technologies to identify and control soil nutrient imbalances. In real-time, the system uses a network of IoT sensors strategically placed in the ground to monitor vital signs, including soil moisture, pH, temperature, and nutrient levels. Powerful neural network algorithms process the data once it is sent in real time by these sensors to a central processing unit. Due to their training on massive soil conditions and crop response datasets, the neural network models can see trends and detect when nutrients aren't being applied in the right quantities. The system analyses sensor data and provides detailed suggestions for managing nutrients, such as changing fertilization and soil treatment methods. By being proactive may address nutrient imbalances, improve soil health, and increase crop yields with early treatments. An innovative step toward more sustainable farming practices, this autonomous system provides real-time data-driven insights to enhance soil quality.