Journal ArticleDOI
Non-Destructive Defect Detection of Apples by Spectroscopic and Imaging Technologies: A Review
Yuzhen Lu,Renfu Lu +1 more
TLDR
In this paper, the authors present an overview of common defects in apples, encompassing physiological disorders, mechanical damage, pathological disorders, and contamination, and conclude with remarks on the prospects of these techniques and research needs in the future.Abstract:
Apples are susceptible to a wide range of defects that can occur in the orchard and during the post-harvest period. Detection of these defects by non-destructive sensing techniques is of great importance for the apple industry and has been an intensive research topic over the past two decades. This review presents an overview of common defects in apples, encompassing physiological disorders, mechanical damage, pathological disorders, and contamination. Presented and discussed in this review is research progress on the detection of defects in apples using various non-destructive spectroscopic and imaging techniques, including visible/near-infrared spectroscopy, fluorescence spectroscopy and imaging, monochromatic and color imaging, hyperspectral and multispectral imaging, x-ray imaging, magnetic resonance imaging, thermal imaging, time-resolved and spatially resolved spectroscopy, Raman spectroscopy, biospeckle imaging, and structured-illumination reflectance imaging. This review concludes with remarks on the prospects of these techniques and research needs in the future.read more
Citations
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Journal ArticleDOI
On line detection of defective apples using computer vision system combined with deep learning methods
TL;DR: The overall results indicated that the proposed CNN-based classification model had great potential to be implemented in commercial packing line.
Journal ArticleDOI
Measurement of optical properties of fruits and vegetables: A review
TL;DR: The instrumentation and data analysis procedures for implementing several emerging optical techniques, including spatially resolved, time-resolved, and spatial-frequency domain, along with the standard integrating sphere method are presented.
Journal ArticleDOI
Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress
TL;DR: Different imaging modes (reflectance, transmittance, fluorescence and Raman) and their combinations, and the potential for real-time acquisition of hyperspectral images at industry relevant speeds are discussed in terms of their advantages and disadvantages.
Journal ArticleDOI
Early detection of decay on apples using hyperspectral reflectance imaging combining both principal component analysis and improved watershed segmentation method
TL;DR: It is demonstrated that multispectral images coupled with the improved watershed segmentation algorithm could be a potential approach for detection of early decay on apples.
Journal ArticleDOI
Machine learning applications to non-destructive defect detection in horticultural products
TL;DR: The recent advances in machine learning methods and their use with various sensing devices to detect defects in horticultural products are reviewed, the present limitations highlighted, and future prospects identified.
References
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Proceedings Article
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Rich feature hierarchies for accurate object detection and semantic segmentation
TL;DR: This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.
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Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
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