Blynk-Powered IoT System with Machine Learning for Personalized Plant Care

This research explores the development of a prototype Internet of Things (IoT)-enabled smart irrigation system and investigates its potential integration with machine learning (ML). The primary mechanism, constructed using Blynk, facilitates real-time sensor data monitoring (temperature, humidity, and moisture) and enables manual irrigation control via a mobile application. The secondary mechanism includes the integration of Machine Learning (ML) model, facilitating data-driven predictions for optimal watering schedules. This ML integrated IoT system has the potential to revolutionize irrigation practices, promoting water conservation and potentially enhancing plant health through precise watering based on real-time sensor data and historical trends.