Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods

Li, Jiangbo; Rao, Xiuqin; Wang, Fujie; Wu, Wei

doi:10.1016/j.postharvbio.2013.02.016

Cited by 69 publications

(23 citation statements)

References 24 publications

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“…In this way, the reflection of each pixel was corrected according to the value given by the model, thereby achieving a similar reflection for the pixels at the top and near the edge. A similar strategy was followed by Li et al (2013) under the premise that the illumination component of the oranges is generally characterised by smooth spatial variations, while the reflectance component tends to vary abruptly. On the other hand, the edges can be removed simply from the analysis using morphological operations, as did Niphadkar et al (2013b).…”

Section: Principles Of Electronic Sortersmentioning

confidence: 99%

“…Best results were achieved in the HSI colour space with success rates ranging from 63% in the case of scale infestation to 100% in the case of stem-end breakdown. A different strategy was followed by Li et al (2013), who employed RGB image ratios to discriminate the stem from different defects in oranges, achieving a good score on classification of defects in images including and excluding the stem. However, the reduction in the prices and the popularisation of MIS and HIS, have increased research on identifying some particularly dangerous defects using this non-standard computer vision technology.…”

Section: Estimation Of External Properties Of the Fruitmentioning

confidence: 99%

“…Table 2 summarises the different works carried out for the application of computer vision in the citrus inspection in postharvest ordered by different topics chronologically. Table 2 Reference Achievement Estimation of properties of the fruit Blasco et al (2007a) Used an unsupervised segmentation method based on region growing to separate defects from sound skin Blasco et al (2007b) Tested different colour spaces to discriminate among eleven types of defects in the citrus peel and the stem Kim et al (2009) Introduced textural features in colour images to distinguish between some serious damages and other cosmetic defects (Blasco et al 2009) Introduced spectral and morphological information to distinguish between some serious damages and other cosmetic defects Omid et al (2010) Estimated the volume of the citrus using two cameras and computing the volume by dividing the fruit in a series of discs López-García et al (2010) Used multivariate image analysis introducing textural information and PCA to separate defects from sound skin López et al (2011) Used colour and texture features extracted in the RGB and HSI colour spaces to discriminate among seven common defects of citrus fruits Vijayarekha (2012a) Used several segmentation techniques to detect defects in citrus fruits Vijayarekha (2012b) Used several segmentation techniques to identify defects in citrus fruits Li et al (2013) Used RGB image ratios to discriminate the stem from different defects in oranges Cubero et al (2014b) Developed a robust method to detect stalks in different fruits, including oranges and mandarins Iqbal et al (2016) Investigated several supervised segmentation methods based on colour information Detection of decay lesions Gomez et al (2007) Used a Mahalanobis kernel to classify pixels as decay or sound skin in hyperspectral images Gómez-Sanchis et al (2008) Used correlation analysis, mutual information, stepwise, and genetic algorithms based on linear discriminant analysis (LDA) to select the most relevant bands of hyperspectral images, and classification and regression trees and LDA for pixels classification in decay or sound skin Kondo et al (2009) Studied the compounds involved in the fluorescence process to detect decay in oranges Kurita et al (2009) Innovative technique which alternatively switched on UV and white pulsed LED, thus allowing the inspection with both types of illumination, and hence allowed both a fluorescent and a colour image to be captured Slaughter et al (2008) Detect freeze-damages in the skin of trough fluorescence imaging Blanc et al (2010) Patented a commercial sorter for decay detection in citr...…”

Section: Ict In the Citrus Inspectionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

102

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Computer vision systems are becoming a scientific but also a commercial tool for food quality assessment. In the field, these systems can be used to predict yield, as well as for robotic harvesting or the early detection of potentially dangerous diseases. In postharvest handling, it is mostly used for the automated inspection of the external quality of the fruits and for sorting them into commercial categories at very high speed. More recently, the use of hyperspectral imaging is allowing not only the detection of defects in the skin of the fruits but also their association to certain diseases of particular importance. In the research works that use this technology, wavelengths that play a significant role in detecting some of these dangerous diseases are found, leading to the development of multispectral imaging systems that can be used in industry. This article reviews recent works that use colour and non-standard computer vision systems for the automated inspection of citrus. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of internal and external features of these fruits. Particular attention is paid to inspection for the early detection of some dangerous diseases like citrus canker, black spot, decay or citrus Huanglongbing.

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Section: Principles Of Electronic Sortersmentioning

confidence: 99%

Section: Estimation Of External Properties Of the Fruitmentioning

confidence: 99%

Section: Ict In the Citrus Inspectionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Cubero

Lee

Albert

et al. 2016

Food Bioprocess Technol

102

View full text Add to dashboard Cite

show abstract

“…Li et al [38] developd a lighting transform method based on a low-pass Butterworth filter with a cutoff frequency (i.e. the filter response will be maximally flat for D 0 <7).…”

Section: Fruit Disease Recognition and Classificationmentioning

confidence: 99%

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Dubey

Jalal

2015

Journal of Intelligent Systems

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Images are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed imageprocessing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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“…Therefore, the citrus processing plants always seek to implement the automated sorting of fruit for improving the quality of fresh citrus fruits. The fast and nondestructive detection of peel defects of citrus fruits was a challenging task (Li et al, 2013;Magwaza et al, 2012;Qin et al, 2012). Decay caused by fungal infection was one of the most serious damages affecting the marketing of fresh citrus fruits compared to common surface defects such as scars.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Classification for Identifying Decayed Oranges Infected by Fungi

Yin

Niu

et al. 2017

Emir J Food Agric

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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.

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Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods

Cited by 69 publications

References 24 publications

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

Application of Image Processing in Fruit and Vegetable Analysis: A Review

Hyperspectral Classification for Identifying Decayed Oranges Infected by Fungi

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