Design and implementaion of the edge-enabled UAV assisted sensing and analytics framework for precision agriculture

The pressing need for revolutionary farming practices is highlighted by the growing challenges posed climate change and increasing need for food worldwide. The combination of IoT sensors, drones, machine learning, and cloud computing is reviving traditional farming techniques, which are becoming less and less effective. In order to maximize irrigation, fertilization, and pest control, this study suggests a centralized system that makes use of real-time data on crop health, soil conditions, and environmental factors. Critical field data and high-resolution imagery are captured by UAVs outfitted with IoT sensors and high-resolution cameras, while Convolutional neural networks and support vector machines are two examples of machine learning algorithms that use trend analysis to forecast yield potential, disease outbreaks, and nutrient deficiencies. The results demonstrate significant improvements in crop productivity, resource efficiency, and environmental sustainability. The complexity of data integration is one issue that edge computing solutions address, enabling localized data processing and speedier decision-making. The study replicates the value of intelligent systems, such as targeted drones for spraying and analytics powered by AI in cutting waste and increasing accuracy. By bridging the gap between technology and practical farming needs, this approach fosters resilient, scalable agricultural systems that can satisfy the demands of future food security. The findings demonstrate the importance of affordable innovations and legal backing in democratizing small-scale farmers' access and advancing sustainable practices around the world.