A Novel AI-Enabled IoT Framework for Real-Time Soil Fertility Analysis and Adaptive Crop Recommendation in Smart Agriculture

Smart agriculture requires constant monitoring of environmental and soil parameters to enable decision-making in farming. However, conventional farming techniques depend on manual evaluations which are not precise and scalable thus, poor crop outputs and misuse of resources. This research offers a new Artificial Intelligence (AI) empowered Internet of things (IoT) design for studying real-time soil fertility as well as providing crop recommendations to farmers in an adaptive manner. The system includes a network of Internet of Things sensors that measure multi-dimensional soil data, including moisture, pH, nitrogen, phosphorus, and potassium levels, and sends it to an AI-enabled analytics engine. This engine uses machine learning models in agronomic datasets for determining soil health and forecasting the most suitable crops for cultivation with respect to particular environmental conditions. The framework supports smart decision-making, resource-efficient farming, and a higher turnout based on the specific planning of the crops according to the conditions in the field at the given moment. Experimental validation in testbed environments verifies that the system can provide high accuracy in fertility classification and crop recommendation and it works better than the static or rule-based methods. The suggested combination of AI-IoT provides a scalable and flexible solution to the modern-day challenges of agriculture.