Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines

Wen, Jiang; Yang, Tao

doi:10.1016/s0957-4174(98)00079-7

Cited by 82 publications

(41 citation statements)

References 7 publications

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“…Several methods based on more specific image acquisition methods are reported in the literature. Wen and Tao (1999) developed a nearinfrared vision system for automating apple defect inspection. It was made of a monochrome CCD camera attached with a 700 nm long-pass filter.…”

Section: Introductionmentioning

confidence: 99%

Development of a multi-spectral vision system for the detection of defects on apples

Kleynen

Leemans

Destain

2005

Journal of Food Engineering

174

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A method to sort ÔJonagoldÕ apples based on the presence of defects was proposed. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed. Multi-spectral images of sound and defective fruits were acquired tending to cover the whole colour variability of this bicolour apple variety. Defects were grouped into four categories: slight defects, more serious defects, defects leading to the rejection of the fruit and recent bruises. Stem-ends/calyxes were detected using a correlation pattern matching algorithm. The efficiency of this method depended on the orientation of the stem-end/calyx according to the optical axis of the camera. Defect segmentation consisted in a pixel classification procedure based on the BayesÕ theorem and non-parametric models of the sound and defective tissue. Fruit classification tests were performed in order to evaluate the efficiency of the proposed method. No error was made on rejected fruits and high classification rates were reached for apples presenting serious defects and recent bruises. Fruits with slight defects presented a more important misclassification rate but those errors fitted however the quality tolerances of the European standard. Considering an actual ratio of sound fruits of 90%, less than 2% of defective fruits were classified into the sound ones.

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Section: Introductionmentioning

confidence: 99%

Development of a multi-spectral vision system for the detection of defects on apples

Kleynen

Leemans

Destain

2005

Journal of Food Engineering

174

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“…Inspection of the latter group by image processing is more problematic because of color transition areas. Among the works that used ordinary machine vision for inspection of apples, Wen and Tao (1999) introduced a single-spectral system to grade bi-colored apples into two categories by rule-based decision. Their system was confused by stem/calyx areas and performed around 85-90%.…”

Section: Introductionmentioning

confidence: 99%

Automatic grading of Bi-colored apples by multispectral machine vision

Ünay

Gosselin

Kleynen

et al. 2011

Computers and Electronics in Agriculture

136

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In this paper we present a novel application work for grading of apple fruits by machine vision. Following precise segmentation of defects by minimal confusion with stem/calyx areas on multispectral images, statistical, textural and geometric features are extracted from the segmented area. Using these features, statistical and syntactical classifiers are trained for two-and multi-category grading of the fruits. Results showed that feature selection provided improved performance by retaining only the important features, and statistical classifiers outperformed their syntactical counterparts. Compared to the state-of-the-art, our two-category grading solution achieved better recognition rates (93.5% overall accuracy). In this work we further provided a more realistic multi-category grading solution, where different classification architectures are evaluated. Our observations showed that the single-classifier architecture is computationally less demanding, while the cascaded one is more accurate.

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“…Unfortunately, no numerical result was provided by the authors for identification of SC's. Wen and Tao (1999) developed a rules-based NIR system and used histogram densities to discriminate SC's of 'Red Delicious' apples from defected areas. Recognition rates of stems and calyxes were 81.4 % and 87.9 %, respectively.…”

Section: Introductionmentioning

confidence: 99%