Next Article in Journal
Sewage Sludge Hydrochar: An Option for Removal of Methylene Blue from Wastewater
Previous Article in Journal
A Fast Frequency Domain Method for Steady-State Solution of Forced Vibration of System with Complex Damping
Open AccessReview

A Review of Convolutional Neural Network Applied to Fruit Image Processing

1
Laboratory of Technological Research in Pattern Recognition (LITRP), Universidad Católica del Maule, 3480112 Talca, Chile
2
Department of Agricultural Science, Universidad Católica del Maule, 3480112 Talca, Chile
3
Department of Economy and Administration, Universidad Católica del Maule, 3480112 Talca, Chile
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(10), 3443; https://doi.org/10.3390/app10103443
Received: 16 April 2020 / Revised: 12 May 2020 / Accepted: 13 May 2020 / Published: 16 May 2020
(This article belongs to the Section Food Science and Technology)
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. View Full-Text
Keywords: convolutional neural network; deep learning; fruit classification; fruit quality evaluation; fruit detection convolutional neural network; deep learning; fruit classification; fruit quality evaluation; fruit detection
Show Figures

Figure 1

MDPI and ACS Style

Naranjo-Torres, J.; Mora, M.; Hernández-García, R.; Barrientos, R.J.; Fredes, C.; Valenzuela, A. A Review of Convolutional Neural Network Applied to Fruit Image Processing. Appl. Sci. 2020, 10, 3443.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop