Harnessing YOLO‐Powered Drones for Cloud‐Based Weed Density Mapping Focusing Agri 4.0

This chapter explores the integration of Internet of Things (IoT) and Machine Learning (ML), particularly in drones, focusing on smart agricultural. This chapter presents a drone-based weed density mapping system that combines advanced ML algorithms, such as YOLO (You Only Look Once), with high-resolution drone-mounted cameras for efficient weed detection and classification. By embedding computational capabilities directly on the drone, the system minimizes reliance on bandwidth-intensive cloud processing, enhancing resource efficiency. The proposed approach incorporates geolocation data using GPS (Global Positioning System) modules and transmits essential insights to Google Cloud IoT Core via MQTT (Message Queuing Telemetry Transport) for visualization and analysis. Weed density data are further processed to generate heat maps, enabling targeted herbicide application and reducing environmental impact. This approach contributes to sustainable agriculture aligned with Agri 4.0. Through innovative hardware–software integration, the system addresses key challenges such as varying soil conditions, environmental factors, and weed–crop differentiation. This study underscores the potential of IoT and ML in revolutionizing sustainable agriculture by delivering a scalable, real-time solution for automated weed management and resource optimization.