A scalable web system for multi-index 3D point cloud visualization and real-time sensor monitoring in precision agriculture

New technologies are transforming precision agriculture by enabling real-time monitoring, data-driven decision-making, and resource optimization. We present a web-based visor system that integrates 3D point cloud visualization of multi-vegetative indices with live sensor streams to form a digital replica of agricultural fields. The Potree-based viewer overlays geolocated point clouds onto an OpenStreetMap layer. A Node.js/Express REST API ingests heterogeneous sensor data (XML, JSON, CSV) into MongoDB, with Redis caching for low-latency retrieval. A Three.js first-person module enables immersive field walkthroughs, while a lazy-load mechanism lets users toggle vegetative indices on demand. Historical data are rendered via Chart.js. Deployed on Kubernetes, the system scales dynamically and remains resilient. Future work includes advanced data normalization, WebSockets-based push updates, and AR overlays. This open-source platform demonstrates how monitoring systems can drive sustainable, high-yield agriculture.