Olive-Fruit Variety Classification by Means of Image Processing and Convolutional Neural Networks

The automation of classification 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 classification, appr...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.147629-147641
Main Authors: Ponce, Juan M., Aquino, Arturo, Andujar, Jose M.
Format: Article
Language:English
Subjects:
Agriculture
Artificial neural networks
Automation
Belt conveyors
Classification
Classifiers
Computer architecture
Computer vision
convolutional neural network
Convolutional neural networks
Deep learning
Feature extraction
food industry
Food processing industry
fruit classification
fruit variety
Fruits
Image classification
Image processing
Neural networks
olive
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The automation of classification 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 classification, approaching it as an image classification 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 classifiers with which perform the variety categorization of the fruits. Remarkable hit rates were obtained after testing the classifiers 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 classification.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2947160