IoT-Based Weather Monitoring and Forecasting System for Smart Coconut Farming Using Blynk Application

Abstract

Coconut farming is highly dependent on climatic conditions such as temperature, humidity, rainfall, and wind speed. Unpredictable weather patterns can adversely affect crop yield and quality, making accurate weather monitoring and forecasting crucial for effective farm management. An IoT-based weather monitoring and forecasting system tailored for smart coconut farming, integrated with the Blynk platform for real-time data visualization and remote access. The proposed system employs a network of IoT-enabled sensors to measure environmental parameters, which are transmitted to a microcontroller for processing. The data is then uploaded to the Blynk cloud, enabling farmers to access live weather information and predictive insights through a mobile application. Additionally, weather forecasting algorithms are implemented to provide short-term climate predictions, allowing farmers to make informed decisions regarding irrigation scheduling, pest management, and harvesting. The system enhances resource utilization, reduces manual intervention, and supports sustainable agricultural practices. Field implementation demonstrates that the proposed solution offers accurate, cost-effective, and user-friendly weather monitoring and prediction, contributing to increased productivity and resilience in coconut cultivation.