Research Progress on Non-destructive Detection of Fruits Based on Infrared Thermal Imaging
DOI:
https://doi.org/10.70088/baza6v60Keywords:
Infrared thermal imaging, Non-destructive detection, Fruit detection, Image processing, Deep learningAbstract
Approximately one-third of fruits worldwide are lost or wasted during postharvest circulation, and non-destructive testing technologies play a vital role in mitigating such postharvest fruit losses. Featuring broad versatility, high detection efficiency and absence of ionizing radiation, infrared thermal imaging has become a key technical approach for non-destructive fruit inspection. This paper systematically reviews signal processing strategies and intelligent identification modeling algorithms for infrared thermal imaging based fruit quality testing in recent years. It elaborates the fundamental principles and practical performance of combining infrared thermograms with temperature difference thresholding, image processing, frequency domain analysis, conventional machine learning, and deep learning algorithms. Numerous studies have verified the capability of infrared thermal imaging to detect mechanical bruises, ripeness levels, pathological deterioration, pest infestation, and subsurface damage depth in fruits. Furthermore, this technique solves long-standing challenges in the non-destructive detection of dark-hued fruits. With continuous scientific research and technical improvements, infrared thermal imaging will further support smart agriculture and promote the fruit production processing industry toward intellectualization, higher efficiency, and large-scale deployment.References
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Copyright (c) 2026 Wenjing Wei, Yin Liu, Tianxin Fu, Jiabin Li, Qian Gong (Author)
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