Detection of Hidden Bruise on Kiwi fruit Using Hyperspectral Imaging and Parallelepiped Classification

Lü, Qiang; Tang, Mingjie

doi:10.1016/j.proenv.2012.01.404

Cited by 78 publications

(25 citation statements)

References 11 publications

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“…The acquired hyperspectral image provides a spectral image for each spectral band and a spectral curve for each pixel in the image in a three‐dimension form called a “hypercube”; therefore, hyperspectral imaging technology is more reliable than traditional machine vision or spectroscopy techniques in analyzing the characteristics of objects . Currently, hyperspectral imaging technology is widely used in the quality inspection of agricultural products such as identification of hidden bruises on kiwi fruit, internal injury in almond nuts, common defects in jujube, and black spots in potatoes . An increasing amount of research on the detection of different types of apple bruises or diseases using hyperspectral imaging technology has also been reported .…”

Section: Introductionmentioning

confidence: 99%

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Tan

Sun

Yang

et al. 2018

Journal of Chemometrics

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The ability to determine if an apple is bruised and to provide quantitative and objective descriptions of the degree of bruising is not the only important basis for assessing apple quality but also has significance for improving the postharvest handling of apples. In this study, segmented principal component analysis for hyperspectral images in the spectral range of 401 to 1037 nm was carried out, and seven characteristic wavelengths were selected based on the weight coefficients of the principal component images. By using the principal component analysis operations with the selected wavelengths and image processing methods, an accurate recognition algorithm for apple bruises was proposed. For 40 intact samples and 160 bruised samples, the average correct recognition rate was 99.1%. Moreover, this paper obtained the average spectra of 157 segmented bruised regions by applying a binary mask. A characteristic wavelength selection method that combines competitive adaptive reweighted sampling with correlation coefficient methods and supports vector machine modeling methods based on grid parameter optimization was put forward for the classification and identification of the bruising degrees of apples. The results showed that the classification accuracy was as high as 97.5% for the test set. Overall, this study demonstrated that hyperspectral imaging technology can be used to accurately and effectively identify early bruises and determine the bruising degree of apples, which provides a new method for on‐line, nondestructive detection, and grading of early bruises in apples.

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Section: Introductionmentioning

confidence: 99%

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Tan

Sun

Yang

et al. 2018

Journal of Chemometrics

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“…There are six machine learning algorithms applied in classifying the satellite image such as Mahalanobis Distance (Chennai et al, 2015), Minimum Distance (Chennai et al, 2015), Maximum Likelihood (Ahmad and Quegan, 2012), Parallelepiped (Lü and Tang, 2012;Vanitha et al, 2013), Neural Network (Ojaghi et al, 2015;Mustapha et al, 2010) and Support Vector Machines (Petropoulos et al, 2011).…”

Section: Image Classificationmentioning

confidence: 99%

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

Manaf¹,

Mustapha²,

Sulaiman³

et al. 2016

Journal of Computer Science

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Shoreline is a very important element to identify exact boundary at the coastal areas of a country. However, in order to identify land-water boundary for a large region using traditional ground survey technique is very time consuming. Alternatively, shoreline can be extracted by using satellite images that minimizes the mapping errors. The trend of extracting shoreline has been shifted from image processing to machine learning and data mining techniques. By using machine learning technique, the satellite images could be classified into land and water classes in order to extract shoreline. However, the result is meaningless if it has cloud and shadow on the water-land boundary. In this study, we compare the accuracy and Kappa Coefficient of six machine learning techniques namely Maximum Likelihood, Minimum Distance, Mahalanobis Distance, Parallelepiped, Neural Network and Support Vector Machines on three type of images; single optical multispectral, single SAR and fused image. A case study for this research is done alongside Tumpat beach, located at the Northeast Coast of Peninsular Malaysia. All the machine learning techniques have been tested on the three types of images. The experimental results show that classification using SVM on single multispectral image has the highest accuracy among all. However, the classified of fused image using SVM is considered much more accurate because it can cater the cloud and shadow problem. Additionally, the classification on 5 and 10 m fused images also tested and the result shows that with the increase of spatial resolution of fused image, the classification accuracy also increases.

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“…The consistency of those models is evaluated based on the actual data. The most used supervised classification methods are: maximum likelihood classification (MLC) [12], parallelepiped method (PP) [13] and fuzzy sets [14], neural networks (NNs) [19,20], support vector machines (SVM) [21,22] and computational intelligence [23]. In other hand, the basic task of unsupervised learning methods is to develop classification labels automatically.…”

Section: Q4mentioning

confidence: 99%

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

Iounousse

Er‐Raki

Motassadeq

et al. 2015

Applied Soft Computing

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Detection of Hidden Bruise on Kiwi fruit Using Hyperspectral Imaging and Parallelepiped Classification

Cited by 78 publications

References 11 publications

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

The feasibility of early detection and grading of apple bruises using hyperspectral imaging

Comparison of Classification Techniques on Fused Optical and SAR Images for Shoreline Extraction: A Case Study at Northeast Coast of Peninsular Malaysia

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

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