IoT-Based Precision Agriculture Using Smart Sensors and Cloud Computing for Crop Monitoring and Yield Optimization

The Internet of Thingsbased precision agriculture system plans to deliver the finest agricultural productivity by giving real-time monitoring and therefore informed decisions based on data. Data from the different environmental factors like temperature, soil and other aspects are evaluated by smart sensors and then subsequently sent to the AWS Cloud by a central microcontroller. Then, the XGBoost machine learning model is applied to predict agricultural output under several scenarios following all that has been done with the collected data. Hyperparameter tuning gives the best performance while feature selection enhances the accuracy of the model after normalization and preprocessing. The results of experiments give a 98.5% prediction accuracy, which is confirmed by an R-squared value of 0.92 and an error of 0.245. The system utilizes a new method to enhance farm productivity, reduce resource usage, and enable data-driven decisions in real time.