Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges

Hyperspectral imaging (HSI) combines spatial and spectral information and has been widely investigated for nondestructive sensing in agriculture and food. This review examines HSI applications in crop growth monitoring, abiotic and biotic stress detection, soil assessment, and postharvest quality and safety inspection. Particular attention is given to how acquisition configuration, spectral preprocessing, feature extraction, and machine- or deep-learning models affect performance across laboratory, field, and production-line settings. Recent studies show that red-edge and shortwave-infrared features are useful for estimating chlorophyll, nitrogen, and water status, while learning-based models have been applied to disease detection, contaminant identification, and quality grading. However, transfer across years, sites, and instruments remains difficult, and practical deployment is further limited by calibration inconsistency, hardware cost, and operational complexity. We therefore discuss instrument-aware calibration and preprocessing, model generalization, lower-cost sensing, and deployment strategies that may improve reproducibility and field applicability.