Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges.
- 1College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou 466001, China.
- 2School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang 212013, China.
- 3School of Physics and Telecommunications Engineering, Zhoukou Normal University, Zhoukou 466001, China.
Abstract
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.
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BibTeX
@article{wen2026hyperspectral,
title = {{Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges.}},
author = {Dingbao Wen and Yifan Dong and Lingwei Hu and Dawei Cao and Jitao Li},
journal = {Foods (Basel, Switzerland)},
year = {2026},
month = sep,
doi = {10.3390/foods15193396},
url = {https://europepmc.org/article/MED/42883503},
note = {CC BY},
}
RIS
TY - JOUR
TI - Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges.
AU - Dingbao Wen
AU - Yifan Dong
AU - Lingwei Hu
AU - Dawei Cao
AU - Jitao Li
PY - 2026
DA - 2026/09/23
JO - Foods (Basel, Switzerland)
DO - 10.3390/foods15193396
UR - https://europepmc.org/article/MED/42883503
AB - 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.
SN - 2304-8158
ER -