This paper is published in Volume-6, Issue-2, 2020
Area
Image Processing
Author
S. Krishna Kumar
Co-authors
J. Kaviya, G. Dilip Prakash, K. Srinivasan
Org/Univ
Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, India
Pub. Date
05 March, 2020
Paper ID
V6I2-1148
Publisher
Keywords
Machine Vision, Image Processing, Convolutional Neural Network, Fruits Quality Detection

Citationsacebook

IEEE
S. Krishna Kumar, J. Kaviya, G. Dilip Prakash, K. Srinivasan. Fruit quality detection using machine vision techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
S. Krishna Kumar, J. Kaviya, G. Dilip Prakash, K. Srinivasan (2020). Fruit quality detection using machine vision techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 6(2) www.IJARIIT.com.

MLA
S. Krishna Kumar, J. Kaviya, G. Dilip Prakash, K. Srinivasan. "Fruit quality detection using machine vision techniques." International Journal of Advance Research, Ideas and Innovations in Technology 6.2 (2020). www.IJARIIT.com.

Abstract

Machine vision techniques are now widely used to detect the quality of fruits. Image processing is usually the first step in detecting the quality of fruits. The process starts by capturing the image of the fruits using raspberry pi. Then, the image is transmitted to the processing stage where it can extract the features of the fruit like shape, size, and color. These processes are done using image processing. It helps to identify and compare the fruit shape, size, and color with the trained datasets. This is done during the training and testing stage. A diversity of methods for automatic separation of fruits is developed. Artificial Neural Network is the one that helps to segregate the fruits based on the quality such as good, moderate and rotten fruit. The existing system can only separate the fruits into good and rotten one with an accuracy of 87.4% but our proposed system is capable of separating the fruits into good, moderate and rotten one with an accuracy of 94.12%.
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