Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications

Li Chen, Yu Wu, Ning Yang and Zongbao Sun ()
Additional contact information
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

Agriculture, 2025, vol. 15, issue 16, 1-30

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.

Keywords: hyperspectral imaging; diffraction imaging; agriculture; pest detection; crop disease identification; lens-free (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/16/1775/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/16/1775/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:16:p:1775-:d:1727566

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().