Deep Learning-Based Hyperspectral Imaging for Early Detection and Severity Assessment of Crop Diseases
Keywords:
precision agriculture; hyperspectral imaging; deep learning; crop disease detection; severity assessment; system architecture; edge intelligence; data governance; fairness; sustainabilityAbstract
Global food security is increasingly threatened by crop diseases that cause substantial yield losses and compromise agricultural supply chains. Early detection and accurate severity assessment are critical for timely intervention, yet conventional visual inspection remains limited in scalability, objectivity, and sensitivity to pre-symptomatic physiological changes. Hyperspectral imaging captures rich spectral signatures that encode plant health status well before visible symptoms appear, and recent advances in deep learning have demonstrated remarkable capability in extracting discriminative features from high-dimensional hyperspectral data. This paper adopts a systems-level perspective to examine the integration of deep learning-based hyperspectral imaging for crop disease management, moving beyond algorithmic performance to address the full sociotechnical infrastructure required for real-world deployment. We analyze the architecture of end-to-end sensing and computing pipelines, including unmanned aerial vehicle platforms, edge-cloud data flows, sensor calibration, and model training paradigms that accommodate spatial, temporal, and spectral variability. Particular attention is given to structural trade-offs in model compression, federated learning, domain adaptation, and edge inference under resource constraints, as well as robustness against environmental noise and distribution shift. The discussion extends to data governance, bias and fairness across crop types and farming communities, environmental sustainability of large-scale AI training, and the alignment of technological design with agricultural policy and regulatory frameworks. Through a synthesis of recent research and cross-domain insights, we argue that sustainable impact depends on co-designing the technical system with institutional and governance structures that ensure equitable access, interpretability, and long-term ecological viability. The paper concludes with a forward-looking research agenda highlighting digital twins, explainable AI, and resilient multi-modal fusion as essential directions for next-generation precision agriculture systems.
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