Smart Agriculture: Leveraging IoT and Machine Learning in Wireless Sensor Networks for Precision Farming

This research explores the challenge of incorporating IoT with Machine Learning (ML) applications to Precision Agriculture, particularly the use of wireless sensors for irrigation control. Information from sensors that monitor key parameters including temperature, humidity, moisture, and water levels are used to forecast water requirements and control pumps. Support vector machine (SVM), Decision tree (DT), Random Forest (RF) and Naïve Bayes (NB) techniques are adopted for the analysis to find out their accuracy levels. It is observed from all the above-performed models that among all, the SVM has the maximum prediction rate of the given dataset and is slightly outperformed by only the DT and RF. The confusion matrix which points to the misclassification patterns of each model gives an insight into identifying the best ML approach for use. The convergence of IoT and ML guarantees flexibility and responsiveness when controlling and designing agricultural systems. It is this approach that has the potential to enhance water resources management as well as address issues precipitated by climate change and growing worldwide food demand.