Hyperspectral near-infrared imaging for the detection of physical damages of pear

Lee, Wang‐Hee; Kim, Moon S.; Lee, Hoonsoo; Delwiche, Stephen R.; Bae, Hyeun‐Jong; Kim, Dae-Yong; Cho, Byoung‐Kwan

doi:10.1016/j.jfoodeng.2013.12.032

Cited by 122 publications

(53 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[6] ElMasry et al [7] investigated a hyperspectral imaging system based on a spectral region between 400 and 1000 nm for early detection of bruises on 'McIntosh' apples, which could detect apple bruises on different background colors (green, red, and green reddish). Lee et al [8] used hyperspectral imaging in the spectral region from 950 nm to 1650 nm for detecting physical damages of pears. The result demonstrated that the hyperspectral imaging technique with the best threshold waveband ratio algorithm could detect pear bruises accurately.…”

Section: Introductionmentioning

confidence: 99%

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Zhang

2018

International Journal of Food Properties

View full text Add to dashboard Cite

Hyperspectral imaging technique (400-1000 nm) was used for rapid and nondestructive recognition of bruises of apples. A total of 324 hyperspectral images were collected from 108 Fuji apples and the average spectral reflectance was extracted from the region of interest (ROI) of each image. The classification results of AdaBoost for the data pretreated by various existing methods were compared. Then, the correlation-based feature selection (CFS) algorithm was used to obtain characteristic wavelengths for reducing data redundancy. After pretreating with multiplicative scatter correction (MSC) and CFS, the average accuracy of the selected wavelengths was 97.63%. Then, an image processing algorithm based on the characteristic wavelengths selected before was proposed for the visual discrimination of bruises. This algorithm performed independent component analysis (ICA) transformation of the selected wavelengths, and chose the third component image of the ICA transform, then used adaptive threshold segmentation to obtain the bruise region of apples. The results showed that hyperspectral imaging technology could discriminate apple bruise, and this study can help to develop an online apple bruises detection system. ARTICLE HISTORY

show abstract

Section: Introductionmentioning

confidence: 99%

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Zhang

2018

International Journal of Food Properties

View full text Add to dashboard Cite

show abstract

“…Recently, hyperspectral imaging technology, which can provide both spectral and spatial information of object simultaneously, has been used to identify some slight damages such as chilling injury (ElMasry et al, 2009;Sun et al, 2017) and slight bruise (Lee et al, 2014, Huang et al, 2015. And, in some resent studies, applications of this technique to detect the decay caused by fungi were also reported and showed that it was a powerful tool to identify the decayed citrus fruits (Lorente et al, 2013;Gómez-Sanchis et al, 2013.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Classification for Identifying Decayed Oranges Infected by Fungi

Yin

Niu

et al. 2017

Emir J Food Agric

View full text Add to dashboard Cite

Fast and nondestructive detection of early decay caused by fungal infection in citrus fruit was a challenging task for the citrus industry during the postharvest fruit processing. In general, workers relied on the ultraviolet induction fluorescence technique to detect and remove the decayed citrus fruits in fruit packing houses. However, this operation was harmful for human health, and was also very inefficient. In this study, navel oranges were used as research object. A novel method combining with hyperspectral imaging technology in the wavelength region between 400 and 1100 nm wavelength was proposed to solve this problem. First, normalization approaches were applied to decrease the variation of spectral reflectance intensity due to natural curvature of navel orange surface. Then, the spectral data of regions of interest (ROIs) from normal and decayed tissues was analyzed by principal component analysis (PCA) for investigating the performance of visible and near infrared (Vis-NIR) hyperspectral data to discriminate these two kinds of tissues. Next, six characteristic wavelength images were obtained by analyzing the loadings of the first principal component (PC1). And, a multispectral image was established by using the corrected six characteristic wavelength images. On basis of the multispectral image, pseudo-color image processing with intensity slicing was utilized to produce a two-dimensional color image with clear contrast between decayed and normal tissues. Finally, an image segmentation algorithm by combining the pseudo-color processing method and a global threshold method was proposed for fast identification of decayed navel oranges. For 240 independent samples, the success rates were 100 and 97.5% for decayed navel oranges infected by Penicillium digitatum and normal navel oranges, respectively. In particular, the proposed algorithm was also applied to detect the decayed navel oranges infected by Penicillium italicum (samples not used for the development of algorithm) and obtained a 91.7% identification accuracy, indicating a well generalization ability and actual application value of the proposed algorithm.

show abstract

“…As spectrometers have been applied to post-harvest processes as non-destructive tools, its data, i.e., spectra, which represent characteristics of target material, have been statistically modeled to sort and evaluate the quality of the product. This technique has been utilized for many agricultural and food products, such as fruits (ElMasry et al, 2008;Lee et al, 2014;Xing and De Baerdemaeker, 2005), crops (Nicolaï et al, 2007), vegetables (Pereira et al, 2008), animal products (Cho et al, 2009;Lohumi et al, 2016), and even seeds (Ambrose et al, 2016). However, these studies did not use mathematical models for agriculture and foods themselves, but to analyze spectral data.…”

Section: Introductionmentioning

confidence: 99%

A novel approach in analyzing agriculture and food systems: Review of modeling and its applications

Kim¹,

Cho²,

Lee³

2016

Korean Journal of Agricultural Science

Self Cite

View full text Add to dashboard Cite

For the past decades, advances in computational devices have propelled mathematical modeling to become an effective tool for solving the black box of complex biological systems because of its prominent analytical power and comprehensive insight. Nevertheless, modeling is still limitedly used in the fields of agriculture and food which generally concentrate on producing experimental data rather than processing them. This study, hence, intends to introduce modeling in terms of its procedure types of structure, formulation, analyses, and software, with reviews of current notable studies from micro to macro scales so as to propose the modeling technique as a novel approach in discerning conundrums in agriculture and food systems. We expect this review to provide an eligible source for researchers who are willing to apply modeling techniques into the unexplored fields related to bio-systems that comprehensively include biology, nutrition, agriculture, food, animal science, and ecology.

show abstract

Hyperspectral near-infrared imaging for the detection of physical damages of pear

Cited by 122 publications

References 17 publications

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Visual detection of apple bruises using AdaBoost algorithm and hyperspectral imaging

Hyperspectral Classification for Identifying Decayed Oranges Infected by Fungi

A novel approach in analyzing agriculture and food systems: Review of modeling and its applications

Contact Info

Product

Resources

About