<p>Current hyperspectral imaging methods face significant challenges: deep learning approaches, while accurate, require expensive computational resources and large training datasets, whereas traditional methods often struggle with precision in complex agricultural landscapes. To overcome these limitations, we introduce HyperFast, an efficient and robust framework specifically designed for high-resolution UAV-borne hyperspectral imagery, focusing on precision crop monitoring. HyperFast integrates spectral and spatial features through a novel hybrid approach, that combines guided filtering (GF) for pre-processing with edge-preserving filtering (EPF) for post-processing. At its core, the Random Forest (RF) classifier balances computational efficiency and classification accuracy, effectively reducing pixel-level misclassifications while preserving critical object boundaries. Unlike deep learning methods that require GPU acceleration, HyperFast enables fast hyperspectral data processing on standard CPUs, making it well-suited for deployment on edge computing devices for in-field analysis and real-time decision support in precision agriculture. Extensive evaluations on three high-resolution hyperspectral datasets demonstrate that HyperFast consistently outperforms state-of-the-art deep learning models. HyperFast achieves overall accuracies of 99.94%, 99.89%, and 99.92%, while reducing processing time to just 9–21 seconds on CPUs—compared to several minutes or hours on GPUs for CNN-based methods. These results highlight HyperFast’s potential as a practical, scalable, and resource-efficient solution for real-time agricultural monitoring, particularly in resource-constrained environments.</p>