RT info:eu-repo/semantics/article
T1 A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties
A1 Barrio-Conde, Mikel
A1 Zanella, Marco Antonio
A1 Aguiar-Perez, Javier Manuel
A1 Ruiz González, Rubén
A1 Gómez Gil, Jaime
K1 Classification system
K1 Convolutional neural network
K1 High oleic sunflower seed
K1 Ingeniería de sistemas
K1 Systems engineering
AB Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.
PB MDPI
SN 1424-8220
YR 2023
FD 2023-03
LK http://hdl.handle.net/10259/9680
UL http://hdl.handle.net/10259/9680
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 04-dic-2024