A review of convolutional neural network applied to fruit image processing

Naranjo-Torres, José; Mora, Marco; Hernández-García, Ruber; Barrientos, Ricardo; Fredes, Claudio; Valenzuela-Keller, Andrés A.

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dc.contributor.author	Naranjo-Torres, José
dc.contributor.author	Mora, Marco
dc.contributor.author	Hernández-García, Ruber
dc.contributor.author	Barrientos, Ricardo
dc.contributor.author	Fredes, Claudio
dc.contributor.author	Valenzuela-Keller, Andrés A.
dc.date.accessioned	2020-10-26T20:58:02Z
dc.date.available	2020-10-26T20:58:02Z
dc.date.issued	2020
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3124
dc.description.abstract	Agriculture has always been an important economic and social sector for humans. Fruit production is especially essential, with a great demand from all households. Therefore, the use of innovative technologies is of vital importance for the agri-food sector. Currently artificial intelligence is one very important technological tool widely used in modern society. Particularly, Deep Learning (DL) has several applications due to its ability to learn robust representations from images. Convolutional Neural Networks (CNN) is the main DL architecture for image classification. Based on the great attention that CNNs have had in the last years, we present a review of the use of CNN applied to different automatic processing tasks of fruit images: classification, quality control, and detection. We observe that in the last two years (2019–2020), the use of CNN for fruit recognition has greatly increased obtaining excellent results, either by using new models or with pre-trained networks for transfer learning. It is worth noting that different types of images are used in datasets according to the task performed. Besides, this article presents the fundamentals, tools, and two examples of the use of CNNs for fruit sorting and quality control.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Applied Sciences, 10(10), 3443	es_CL
dc.subject	Convolutional neural network	es_CL
dc.subject	Deep learning	es_CL
dc.subject	Fruit classification	es_CL
dc.subject	Fruit quality evaluation	es_CL
dc.subject	Fruit detection	es_CL
dc.title	A review of convolutional neural network applied to fruit image processing	es_CL
dc.type	Article	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.doi	doi.org/10.3390/app10103443	es_CL