AI-based Weather Monitoring System Using IoT Interface

Accurate and timely weather forecasting is essential for various sectors, including agriculture and environmental monitoring. Traditional weather monitoring systems often lack real-time data access, predictive accuracy, and portability, especially in remote or resource-limited areas. This paper presents Greenlink, an innovative AI-based weather monitoring system that integrates Internet of Things (IoT) technology with Artificial Intelligence (AI) to address these limitations. The system leverages a Raspberry Pi Pico W microcontroller to collect environmental data from sensors such as MQ135 (air quality), DHT11 (humidity), and BMP180 (temperature and pressure). These sensors feed data into Recurrent Neural Networks (RNNs), utilizing Long Short-Term Memory (LSTM) layers to predict temperature and humidity trends. Real-time data is visualized via the Blynk IoT platform, allowing users to monitor conditions remotely on laptops and smartphones. Powered by a compact LiPo battery, the system ensures continuous operation. Greenlink offers a cost-effective, reliable, and scalable solution for weather forecasting, making it particularly valuable for agricultural and environmental applications in remote locations. The results indicate that Greenlink enhances forecasting capabilities, enabling informed decision-making for sustainable practices and improved resource management.