IoT Based Smart Agriculture Monitoring System with Predictive Analysis

Integrating Internet of Things (IoT) techniques into different fields and processing data produced within it can effectively shape the future. In Precision Agriculture, the use of the IoT features helps to manage crops production by optimizing productivity and reducing environmental concerns based on prediction models. In this paper, an IoT-based agricultural monitoring system is proposed, which integrates principal component analysis (PCA) feature selection methods and neural network classification techniques for crop productivity prediction. Furthermore, the model system allowed a sensing network to collect data of some crops (Tomatoes, Potatoes, Etc.). The experimental results show that our proposed model system can make decisions more accurately.