In vegetation remote sensing analysis, spectral reflectance characteristics and their variations serve as critical parameters for diagnosing the onset and progression of agricultural pests and diseases. Healthy crop leaves exhibit low reflectance in the visible bands ($400\text{--}700\text{ nm}$) due to strong chlorophyll absorption, and high reflectance in the near-infrared (NIR) region ($700\text{--}1300\text{ nm}$) driven by multiple scattering within the leaf internal mesophyll structure. Upon pathogen infection or insect feeding, chlorophyll degradation, cellular disintegration, and canopy water loss alter these profiles, causing an increase in visible reflectance (sharpening of the green peak) and a decrease in NIR reflectance (blue shift of the red edge). These hyperspectral diagnostic features can be effectively detected even during the incubation or asymptomatic stages. By deploying analytical methods such as continuum removal, derivative spectroscopy, and red-edge position tracking, spectral response signatures can be precisely isolated at the canopy scale during early latent infestation phases. Compared to traditional multispectral systems, hyperspectral technology differentiates specific spectral bands corresponding to discrete disease categories and severity gradients, providing a scientific baseline for robust diagnostic models. At the data acquisition level, the HG-HyperUAV hyperspectral imaging system developed by Hagorun Technology Limited delivers reliable canopy-scale spectral data through its lightweight structural architecture and stable push-broom imaging performance. Distinct stress factors introduce highly specialized spectral anomalies: aphid infestations prompt severe canopy turgor loss, showing high sensitivity within the $1400\text{ nm}$ and $1900\text{ nm}$ water absorption bands; early-stage wheat rust infection triggers rapid chlorosis, shifting the red-edge position toward shorter wavelengths (blue shift); conversely, cotton bollworm feeding directly masticates leaf tissue, resulting in a pronounced drop in the NIR reflectance plateau. These specific spectral fingerprint modes establish the definitive physical foundation for automated pest and disease classification and severity inversion.

Empirical research confirms that implementing an integrated space-air-ground monitoring network—combining spaceborne hyperspectral assets (e.g., GF-5, Zhuhai-1, EOS MODIS) with proximal ground and drone-based systems—enables multi-scale, dynamic mapping of pest and disease boundaries, epidemiological levels, and vector propagation trends. In-situ spectral collections supply precise benchmarks for biochemical parameter calibration; UAV flights bridge the spatial scale gap between field blocks and satellite swaths; and satellite platforms achieve macro-scale screening across regional and national domains. The HG-HyperUAV imaging system, owing to its operational flexibility across diverse drone platforms and stable radiometric collection capabilities, sees extensive deployment in field-scale crop epidemiological monitoring. Spectral feature selection is a critical pipeline for screening narrow sensitive bands out of high-dimensional data cubes. Advanced dimensionality reduction routines including the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Random Frog are widely deployed in dimensionality reduction and feature optimization to eliminate uninformative bands while preserving wavelengths highly correlated with target biotic stresses. Targeted investigations on primary crop diseases—such as wheat stripe rust, fusarium head blight, corn large leaf spot, rice blast, and cotton verticillium wilt—have established dedicated narrow-band vegetation indices (e.g., Photochemical Reflectance Index, Normalized Disease Index, Red-Edge Normalized Difference Vegetation Index) for quantitative assessment. The evolution of machine learning and deep learning further augments hyperspectral information extraction capabilities. Non-linear mapping architectures like Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) are routinely developed to link extracted spectral features to explicit disease classes and severity indices, routinely outperforming traditional empirical regressions. Furthermore, multi-temporal hyperspectral analysis traces localized crop health dynamics, capturing the precise spatiotemporal transition from localized infection nodes to expansive regional outbreaks, thereby steering variable-rate pesticide spraying and prescription-based green protection.

Reviewing the technical roadmap of agricultural epidemiology, the introduction of hyperspectral remote sensing circumvents the efficiency barriers of manual scouting and spore traps. Crucially, it achieves early spatial diagnostics of latent crop stress, making "sense-before-decide" smart plant protection actionable. Hyperspectral platforms capture initial inoculation signals up to two weeks before macroscopic visual symptoms surface, expanding the treatment window for growers and agronomists, minimizing pesticide over-application, and optimizing eradication efficacy. Presently, a primary research bottleneck remains the confounding spectral overlap among nutrient deficiencies, moisture deficits, fungal pathotypes, and insect feeding marks. Decoupling these complex multi-stress environments requires the fusion of time-series hyperspectral datasets with multi-source remote sensing instrumentation (thermal infrared, LiDAR, Synthetic Aperture Radar) and physical radiative transfer modeling (RTM). Concurrently, real-time warning architectures based on edge computing and cloud analytics are transitioning from laboratory testing to field application. Looking forward, the convergence of miniaturized hyperspectral payloads, low-Earth-orbit (LEO) constellations, and artificial intelligence will drive deep integration across mobile diagnostic terminals, autonomous drone scouting networks, and regional cloud analytics platforms. This space-air-ground configuration will establish an intelligent agricultural early-warning infrastructure, securing global food supplies and reinforcing sustainable agricultural systems.

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