Real-Time Multispectral Edge Computing on UAV Platforms: Bridging the Gap Between Visual Monitoring and Instantaneous Scientific Analytic

While modern Unmanned Aerial Vehicles (UAVs) frequently utilise real-time edge computing, they predominantly rely on standard RGB video streams for basic visual inspection. Integrating advanced sensing technologies for precise biophysical assessments requires multispectral sensors to detect physiological stress using non-visible bands such as Red-Edge and Near-Infrared. However, multispectral data analysis is typically constrained by computationally intensive offline processing, creating a network and computational bottleneck that delays the delivery of actionable intelligence. This paper presents a novel onboard edge-computing architecture that overcomes this latency by processing discrete multispectral snapshots in near real-time. Validated at the ICAR-Indian Agricultural Research Institute using an edge-computer-integrated MicaSense Rededge MX sensor, this cyber-physical system features a custom pipeline that generates fully calibrated reflectance layer stacks in-flight. This computational framework enables on-the-go calculation of the Normalised Difference Vegetation Index (NDVI) from imagery captured at altitudes ranging from 150m to 30m, as well as specialised soil assessments. Beyond basic indices, this architecture facilitates real-time assessment of crop nutrient requirements, early stress detection, and Soil Organic Carbon (SOC) levels. By transmitting lightweight, actionable algorithmic insights rather than gigabytes of raw imagery, this framework optimises bandwidth and eliminates the temporal gap between data acquisition and analysis. It fundamentally transforms UAVs from passive data collectors into active, intelligent cyber-physical systems, providing a scalable, scientifically rigorous computing foundation for time-critical interventions in precision agriculture, forestry management, and dynamic environmental monitoring.