A Methodological Review of Fluorescence Imaging for Quality Assessment of Agricultural Products

Abdul Momin (), Naoshi Kondo, Dimas Firmanda Al Riza, Yuichi Ogawa and David Obenland
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Abdul Momin: Agricultural Engineering Technology, School of Agriculture, Tennessee Tech University, Cookeville, TN 38505, USA
Naoshi Kondo: Laboratory of Biosensing Engineering, Graduate School of Agriculture, Kyoto University, Kitashirakawa, Kyoto 6068267, Japan
Dimas Firmanda Al Riza: Department of Biosystems Engineering, Faculty of Agricultural Technology, Universitas Brawijaya, Jl. Veteran, Malang 65145, Indonesia
Yuichi Ogawa: Laboratory of Biosensing Engineering, Graduate School of Agriculture, Kyoto University, Kitashirakawa, Kyoto 6068267, Japan
David Obenland: USDA Agricultural Research Service, San Joaquin Valley Agricultural Sciences Center, 9611 S. Riverbend Ave., Parlier, CA 93648, USA

Agriculture, 2023, vol. 13, issue 7, 1-14

Abstract: Currently, optical imaging techniques are extensively employed to automatically sort agricultural products based on various quality parameters such as size, shape, color, ripeness, sugar content, and acidity. This methodological review article examined different machine vision techniques, with a specific focus on exploring the potential of fluorescence imaging for non-destructive assessment of agricultural product quality attributes. The article discussed the concepts and methodology of fluorescence, providing a comprehensive understanding of fluorescence spectroscopy and offering a logical approach to determine the optimal wavelength for constructing an optimized fluorescence imaging system. Furthermore, the article showcased the application of fluorescence imaging in detecting peel defects in a diverse range of citrus as an example of this imaging modality. Additionally, the article outlined potential areas for future investigation into fluorescence imaging applications for the quality assessment of agricultural products.

Keywords: agricultural products; citrus; quality; machine vision; fluorescence; image processing (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: 2023
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