Fruit classification for retail stores using deep learning Conference Paper

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Overview

abstract

• Payment of fruits or vegetables in retail stores normally require them to be manually identified. This paper presents an image classification method, based on lightweight Convolutional Neural Networks (CNN), with the goal of speeding up the checkout process in stores. A new dataset of images is introduced that considers three classes of fruits, inside or without plastic bags. In order to increase the classification accuracy, different input features are added into the CNN architecture. Such inputs are, a single RGB color, the RGB histogram, and the RGB centroid obtained from K-means clustering. The results show an overall 95%25 classification accuracy for fruits with no plastic bag, and 93%25 for fruits in a plastic bag. © Springer Nature Switzerland AG 2020.

authors

• Cuevas Tello Juan Carlos
• Núñez Varela José Ignacio
• Rangel-Ramirez G.
• Rojas-Aranda J.L.

publication date

• 2020-01-01

published in

• Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  Journal

Research

keywords

• Convolutional Neural Networks; Deep learning; Fruit classification Convolutional neural networks; Fruits; K-means clustering; Pattern recognition; Plastic containers; Retail stores; Classification accuracy; Classification methods; Input features; Plastic bags; Rgb colors; Deep learning

Identity

Digital Object Identifier (DOI)

• 10.1007/978-3-030-49076-8_1

Additional Document Info

start page

• 3

end page

• 13

volume

• 12088 LNCS