@article{10272/17618,
year = {2019},
month = {10},
url = {http://hdl.handle.net/10272/17618},
abstract = {The automation of classifcation and grading of horticultural products attending to different
features comprises a major challenge in food industry. Thus, focused on the olive sector, which boasts of a
huge range of cultivars, it is proposed a methodology for olive-fruit variety classifcation, approaching it as
an image classifcation problem. To that purpose, 2,800 fruits belonging to seven different olive varieties
were photographed. After processing these initial captures by means of image processing techniques,
the resulting set of images of individual fruits were used to train, and continuedly to externally validate, the
implementations of six different Convolutional Neural Networks architectures. This, in order to compute
the classifers with which perform the variety categorization of the fruits. Remarkable hit rates were
obtained after testing the classifers on the corresponding external validation sets. Thus, it was yielded
a top accuracy of 95.91% when using the Inception-ResnetV2 architecture. The results suggest that the
proposed methodology, once integrated into industrial conveyor belts, promises to be an advanced solution
to postharvest olive-fruit processing and classifcation.},
publisher = {Institute of Electrical and Electronics Engineers},
keywords = {Computer vision},
keywords = {Convolutional neural network},
keywords = {Fruit variety},
keywords = {Food industry},
keywords = {Fruit classification},
keywords = {Image processing},
keywords = {Olive},
title = {Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks},
doi = {10.1109/ACCESS.2019.2947160},
author = {Ponce Real, Juan Manuel and Aquino Martín, Arturo and Andújar Márquez, José Manuel},
}