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A Machine Vision Approach for Grading of Onions

B. L. Benisha Bennet

Abstract


Grading of fruits and vegetables (on the basis of various external and internal quality attributes) is an important unit operation in the food industry. For products such as onions, grading is performed mainly on the basis of size. In the present study, a machine vision system was used to grade onions into three categories (extra-large, medium and small) based on variations in size. The machine vision system consisted of a CCD camera, illumination source, holding unit, sample platform, and computer. The geometric mean diameter of the sample was used as the classification parameter. It was observed that the system gave an overall classification efficiency of 93% with better results for extra-large grade. Similar approaches can be successfully implemented for classification of other agricultural products using various grading parameters.


Keywords


CCD camera, size sorting, machine vision, image processing, grading

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References


Anon. http://agritech.tnau.ac.in/horticulture/horti_vegetables_bellaryonion.html. As accessed on 01.04.14.

Hanan MW, Burks TF, Bulanon DM. A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection. CIGRE Journal. 2009; Vol. XI - on-line.

Wang XZ, Mao HP, Han X. Vision-based judgment of tomato maturity under growth conditions. African Journal of Biotechnology. 10(18): 3616–23p.

Rios-Cabrera R, Lopez-Juarez I, Sheng-Jen H. ANN analysis in a vision approach for potato inspection. Journal of Applied Research and Technology. 2008; 6(2): 106–19p.

Barnes M, Duckett T, Cielniak G. Boosting minimalist classifiers for blemish detection in potatoes. 24th International Conference Image and Vision Computing New Zealand. 2009. 6. Jarimopas B, Jaisin N. An experimental machine vision system for sorting sweet tamarind. Journal of Food Engineering. 2008; 89(3), 291–7p. 7. Kang SP, East AR, Trujillo FJ. Colour vision system evaluation of bicolour fruit: A case study with ‘B74’mango. Postharvest Biology and Technology. 2008; 49(1), 77–85p. 8. Lino ACL, Sanches J, Fabbro IMD. Image processing techniques for lemons and tomatoes classification. Bragantia. 2008; 67(3): 785–9p. 9. Riyadi S, Juraiza Ishak A, Mustafa MM, et al. Wavelet-based feature extraction technique for fruit shape classification. In ISMA 5th International Symposium on Mechatronics and Its Applications. IEEE. 2008; 1–5p.

Narendra VG, Hareesh KS. Quality inspection and grading of agricultural and food products by computer vision – A review. International Journal of Computer Applications. 2010; 2(1), 43–65p.


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