IoT-Based Smart Agricultural Monitoring Using WSN and Predictive Analytics with Artificial Intelligence (AI)

Smart agriculture leverages Internet of Things (IoT) technology to improve crop yield, resource efficiency, and environmental sustainability. This study presents an IoT-based smart agricultural monitoring system that integrates Wireless Sensor Networks (WSNs) with predictive analytics to monitor key environmental parameters, such as soil moisture, temperature, humidity, and light intensity, in real-time. The system utilizes WSNs to gather data from distributed sensor nodes and employs machine learning models for predictive analytics, enabling proactive decision-making for optimized irrigation, fertilization, and pest control. Experimental results demonstrate that the proposed system enhances resource usage by 40% and increases crop yield by 30% compared to traditional farming methods with Artificial Intelligence (AI). Additionally, the predictive analytics component achieves an accuracy of 92% in forecasting environmental conditions, aiding in timely interventions and minimizing crop stress. This IoT-based solution supports sustainable farming practices and offers scalability for various agricultural applications, including precision farming and greenhouse monitoring.