A NOVEL VISION USING IMAGING ANALYSIS FOR TOMATO QUALITY DETECTION

Taha, Ahmed T.; Gharib, Aya

doi:10.21608/mjae.2015.98635

A NOVEL VISION USING IMAGING ANALYSIS FOR TOMATO QUALITY DETECTION

Document Type : Original Article

Authors

Ahmed T. Taha ¹
Aya Gharib ²

¹ Assistant Prof of Agric. Eng. Dep., Fac. of Agric., Minoufiya Univ., Egypt.

² Post graduate student, Egypt.

10.21608/mjae.2015.98635

Abstract

The quality of products is very important for the human health. Sorting tons of fruits and vegetables manually is a slow, costly, and an inaccurate process. The objective of this study was to develop a computer vision and image analysis program to serve as a simple and suitable technique for external fruit inspection and for predicting orange fruits maturity through the image analysis technique. The MATLAB software package was used image processing tools to analysis image of tomato. The study also investigated the effectiveness of some color bands, average intensity of RGB bands and HSI. The results revealed that the computer vision and image analysis program could be used to differentiate tomato maturity stages. The results also showed that there is a strong response between both RGB band and HSI of tomato fruits and maturity stage also storage period during 21 days. Automatic sorting of food products is an important process to get high quality food. Vision based sorting system is an accurate and fast process compared to manual sorting. The accuracy of this system can be improved by increasing the dataset of images.

Keywords

References

Ayman H. Amer Eissa and Ayman A. Abdel Khalik (2012). Understanding Color Image Processing by Machine Vision for Biological Materials. Structure and Function of Food Engineering", book edited by Ayman Amer Eissa, ISBN 978-953-51-0695-1, Published: August 22, 2012 under CC BY 3.0 license.

Brennan, J. G. (2006). Food Processing Handbook. Chapter (1) Postharvest Handling and Preparation of Foods for Processing. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

FAOSTAT (2013). Orange production. Available from /http://faostat.fao.orgs.

Fouda, T. and Salah, Sh. (2014). Using Imaging Analyses to Predict Chemical Properties Of Orange Fruits. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 14, (3): 83-86

Helmy, M.A., A.A. Abd El Rahman and H. E. Hassan (2003). Classification of tomato fruits using color image analysis technique. Misr J. Ag. Eng., 20 (3): 70-82.

Kondo, N. (2009). Automation on fruit and vegetable grading system and food traceability”, Trends in Food Science & Technology, doi: 10.1016/j.tifs.

Krutz, G. W., Gibson, H.G., Cassens, D. L., Zhang, M., (2000). Colour vision in forest and wood engineering. Landwards 55, 2-9.

Walailak, J. S. and Tech. (2006). Automated Chokun Orange Maturity Sorting by Color Grading, 3(2): 195-205.