IoT and Machine Learning based Precision Agriculture through the Integration of Wireless Sensor Networks

Abstract

In this study, the integration of IoT technology and machine learning (ML) algorithms with precision agriculture are studied, with an emphasis on optimizing irrigation management through wireless sensor networks. Data are collected in real time from various sensors that measure temperature, humidity, moisture in the soil and water levels. Such data is used to predict the demands for water and control pump operations based on them. They employed different machine learning (ML) models, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Naive Bayes (NB). The accuracy of pump operation predictions is then evaluated. SVM presents the highest overall accuracy. Followed closely by DT and RF. Confusion matrices can be used to glean insights into the misclassification patterns of each model, guiding the selection of the most effective ML approach for precision agriculture applications. IoT and ML technologies together enable real-time monitoring, adaptive control, and data-driven decision-making in agriculture, all of which can help towards efficiently using water resources in dealing with climate change and rapidly growing food demands.