Hyperspectral Remote Sensing for Monitoring Crop Disease: Applications, challenges, and perspectives

Crop disease presents significant threats to global food security and agricultural sustainability. Traditional monitoring methods, reliant on visual inspections and laboratory analyses, are labor intensive and unsuitable for large-scale implementation. Hyperspectral remote sensing has emerged as a promising tool for operational crop disease monitoring. Here, we provide a broad review, starting with a hyperspectral-based description of observable symptoms of common crop disease and then examining hyperspectral features, including spectral and textural features, pigment light absorption, solar-induced chlorophyll fluorescence (SIF), temporal information, and auxiliary data. We also analyze the algorithms used for disease detection, including traditional statistical methods, machine learning (ML)-based methods, and physically based methods. The review highlights the effectiveness of these methods in distinguishing various stressors, detecting early disease, assessing crop resistance, and monitoring large-scale disease. Additionally, we present two case studies of uncrewed aerial vehicle (UAV)-based hyperspectral imaging for maize leaf spot monitoring. Based on a quantitative literature review, we summarize current research trends. Future research should emphasize integrating physical models with deep learning (DL), ensuring the sensitivity and robustness of spectral features and promoting international data sharing.

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Publication Details

Journal
IEEE Geoscience and Remote Sensing Magazine
Published
2025-09-18
DOI
https://doi.org/10.1109/mgrs.2025.3603640
Citations
3
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
5.15
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article

Hyperspectral Remote Sensing for Monitoring Crop Disease: Applications, challenges, and perspectives

Matthew Reynolds, Bedir Teki̇nerdoğan, Qingzhi Liu, Dongxiao Zou et al.
3 citations
IEEE Geoscience and Remote Sensing Magazine
Remote Sensing in Agriculture
5.15
article

Hyperspectral Remote Sensing for Monitoring Crop Disease: Applications, challenges, and perspectives

Matthew Reynolds, Bedir Teki̇nerdoğan, Qingzhi Liu, Dongxiao Zou, Malcolm John Hawkesford, Clement Atzberger, Josep Peñuelas, Lalit Kumar, Chenwei Nie, Yang Song, T. Poblete, Dameng Yin, Pablo J. Zarco‐Tejada, Matthew F. McCabe, Shuaibing Liu, Xiuliang Jin, Yali Bai
article en
3 citations

Abstract

Crop disease presents significant threats to global food security and agricultural sustainability. Traditional monitoring methods, reliant on visual inspections and laboratory analyses, are labor intensive and unsuitable for large-scale implementation. Hyperspectral remote sensing has emerged as a promising tool for operational crop disease monitoring. Here, we provide a broad review, starting with a hyperspectral-based description of observable symptoms of common crop disease and then examining hyperspectral features, including spectral and textural features, pigment light absorption, solar-induced chlorophyll fluorescence (SIF), temporal information, and auxiliary data. We also analyze the algorithms used for disease detection, including traditional statistical methods, machine learning (ML)-based methods, and physically based methods. The review highlights the effectiveness of these methods in distinguishing various stressors, detecting early disease, assessing crop resistance, and monitoring large-scale disease. Additionally, we present two case studies of uncrewed aerial vehicle (UAV)-based hyperspectral imaging for maize leaf spot monitoring. Based on a quantitative literature review, we summarize current research trends. Future research should emphasize integrating physical models with deep learning (DL), ensuring the sensitivity and robustness of spectral features and promoting international data sharing.

IEEE Geoscience and Remote Sensing MagazineVol. 14(1)
The University of Melbourne (AU), Brookhaven National Laboratory (US), Centro Internacional de Mejoramiento de Maíz Y Trigo (MX), Rothamsted Research (GB), Melbourne School of Theology (AU), Centre for Research on Ecology and Forestry Applications (ES), Chinese Academy of Agricultural Sciences (CN), Institute of Crop Sciences (CN), Sichuan Academy of Agricultural Sciences (CN), Ecosystem Sciences (AU), King Abdullah University of Science and Technology (SA), University of New England (AU), Wageningen University & Research (NL), BOKU University (AT)
Openalex Percentile: Top 5%
Remote Sensing in Agriculture
5.15
DOIOpen Access PDFOpenAlex
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