Precision Agriculture Using IoT-Enabled Drones and Edge Computing for Crop Health Monitoring

Abstract

Precision agriculture has emerged as a transformative solution to address the growing demand for sustainable farming practices. The integration of Internet of Things (IoT)-enabled drones and edge computing technologies offers a novel approach for real-time crop health monitoring. This research explores the application of IoT-enabled drones equipped with multispectral sensors and the use of edge computing for on-site data processing, aiming to enhance the efficiency of crop health assessment and decision-making in agriculture. The study investigates the performance of these technologies in capturing key crop health indicators, including vegetation indices (e.g., NDVI), soil moisture levels, and temperature variations. Results demonstrate that the use of IoT-enabled drones significantly improves the accuracy and timeliness of crop monitoring, while edge computing ensures rapid data analysis, reducing reliance on cloud-based processing and enhancing operational efficiency. The findings highlight the potential for large-scale adoption of this integrated system to optimize crop management, increase yield prediction accuracy, and contribute to sustainable farming practices. The implications of these technologies are significant for both large-scale commercial farming and smallholder agriculture, where timely interventions can significantly reduce resource wastage and enhance productivity