Cloud Framework for Precision Agriculture: Applying 'Kernel Trick' Techniques in Support Vector Machines via MQTT and IoT

Precision agriculture is an essential tool that boosts efficiency and productivity in the agricultural industry. With the technological advancements in Internet of Things (IoT) and machine learning, intelligent solutions have emerged for monitoring and forecasting agricultural yields. In this paper, we present a cloud-based framework that uses advanced Support Vector Machines (SVM) techniques, with a 'Kernel Trick' for predicting yields in precision agriculture. Our framework analyzes data obtained from IoT sensors, which capture climatic variables, soil characteristics, and other relevant metrics. By implementing the MQTT protocol, we ensure real-time data transmission, improving the responsiveness of the framework. Our results demonstrate the ability of this method to optimize decision-making and increase agricultural yields, thus showcasing the synergies of SVMs and cloud technologies at the forefront of smart agriculture.