Energy-Efficient and High-Performance IoT-Based WSN Architecture for Precision Agriculture Monitoring Using Machine Learning Techniques

Abstract

Traditional irrigation systems for agricultural lands are expensive, time-consuming, and labor-intensive. Utilizing cutting-edge technology like machine learning, the internet of things, and wireless sensor networks, smart farming addresses current issues with agricultural sustainability while boosting the quantity and quality of crop production from the fields to fulfill the rising food demand. Soil moisture and temperature sensors are used to create a low-cost, real-time IoT-based automatic irrigation system. Two groups have been formed with the sensor information such as “require water” and “not require water” and saved on the server. The device intelligently determines whether the field needs water and automatically turns “ON” or “OFF” the motor. Machine learning based models such as k-nearest neighbor, support vector machines, decision tree, and naive bayes are applied to decide irrigation requirements. Performance metrics show that the KNN classifier performs better than the other two models. The suggested framework allows for better field monitoring and visualization.