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Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications

Author

Listed:
  • Li Chen

    (Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yu Wu

    (Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Ning Yang

    (School of Electrical & Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zongbao Sun

    (Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

Hyperspectral imaging and diffraction imaging technologies, owing to their non-destructive nature, high efficiency, and superior resolution, have found widespread application in agricultural diagnostics. This review synthesizes recent advancements in the deployment of these two technologies across various agricultural domains, including the detection of plant diseases and pests, crop growth monitoring, and animal health diagnostics. Hyperspectral imaging utilizes multi-band spectral and image data to accurately identify diseases and nutritional status, while combining deep learning and other technologies to improve detection accuracy. Diffraction imaging, by exploiting the diffraction properties of light waves, facilitates the detection of pathogenic spores and the assessment of cellular vitality, making it particularly well-suited for microscopic structural analysis. The paper also critically examines prevailing challenges such as the complexity of data processing, environmental adaptability, and the cost of instrumentation. Finally, it envisions future directions wherein the integration of hyperspectral and diffraction imaging, through multisource data fusion and the optimization of intelligent algorithms, holds promise for constructing highly precise and efficient agricultural diagnostic systems, thereby advancing the development of smart agriculture.

Suggested Citation

  • Li Chen & Yu Wu & Ning Yang & Zongbao Sun, 2025. "Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications," Agriculture, MDPI, vol. 15(16), pages 1-30, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:16:p:1775-:d:1727566
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    References listed on IDEAS

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    1. Anton Terentev & Vladimir Badenko & Ekaterina Shaydayuk & Dmitriy Emelyanov & Danila Eremenko & Dmitriy Klabukov & Alexander Fedotov & Viktor Dolzhenko, 2023. "Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina," Agriculture, MDPI, vol. 13(6), pages 1-16, June.
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