Smart Agriculture Optimization: Integrating Edge Computing and AI for Enhanced Crop Management

Abstract

This paper introduces an innovative smart agriculture system aimed at enhancing farming efficiency, resulting in increased crop yields, reduced resource consumption, and minimized environmental impact. The proposed system integrates Recurrent Neural Networks (RNN) and edge computing within precision agriculture, leveraging the synergy of IoT drones and sensor fusion. By combining data from various sensors mounted on drones, including multispectral cameras and LiDAR, with ground based IoT devices such as pH sensors, soil moisture sensors, and temperature and humidity sensors, a comprehensive understanding of crop health, soil conditions, and environmental factors is achieved.