A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

Juan Zhao; Yankun Peng; Sagar Dhakal; Leilei Zhang; Akira Sasao

doi:10.1117/12.2015897

29 May 2013 A noninvasive technique for real-time detection of bruises in apple surface based on machine vision

Juan Zhao, Yankun Peng, Sagar Dhakal, Leilei Zhang, Akira Sasao

Proceedings Volume 8721, Sensing for Agriculture and Food Quality and Safety V; 87210O (2013) https://doi.org/10.1117/12.2015897
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Apple is one of the highly consumed fruit item in daily life. However, due to its high damage potential and massive influence on taste and export, the quality of apple has to be detected before it reaches the consumer’s hand. This study was aimed to develop a hardware and software unit for real-time detection of apple bruises based on machine vision technology. The hardware unit consisted of a light shield installed two monochrome cameras at different angles, LED light source to illuminate the sample, and sensors at the entrance of box to signal the positioning of sample. Graphical Users Interface (GUI) was developed in VS2010 platform to control the overall hardware and display the image processing result. The hardware-software system was developed to acquire the images of 3 samples from each camera and display the image processing result in real time basis. An image processing algorithm was developed in Opencv and C++ platform. The software is able to control the hardware system to classify the apple into two grades based on presence/absence of surface bruises with the size of 5mm. The experimental result is promising and the system with further modification can be applicable for industrial production in near future.

Citation Download Citation

Juan Zhao, Yankun Peng, Sagar Dhakal, Leilei Zhang, and Akira Sasao "A noninvasive technique for real-time detection of bruises in apple surface based on machine vision", Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V, 87210O (29 May 2013); https://doi.org/10.1117/12.2015897

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