A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties
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 ole...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/5/2471 |
| _version_ | 1797614412624822272 |
|---|---|
| author | Mikel Barrio-Conde Marco Antonio Zanella Javier Manuel Aguiar-Perez Ruben Ruiz-Gonzalez Jaime Gomez-Gil |
| author_facet | Mikel Barrio-Conde Marco Antonio Zanella Javier Manuel Aguiar-Perez Ruben Ruiz-Gonzalez Jaime Gomez-Gil |
| author_sort | Mikel Barrio-Conde |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:12:05Z |
| format | Article |
| id | doaj.art-fd081e6ba3e04e90b52752249410f0a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:12:05Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd081e6ba3e04e90b52752249410f0a02023-11-17T08:35:15ZengMDPI AGSensors1424-82202023-02-01235247110.3390/s23052471A Deep Learning Image System for Classifying High Oleic Sunflower Seed VarietiesMikel Barrio-Conde0Marco Antonio Zanella1Javier Manuel Aguiar-Perez2Ruben Ruiz-Gonzalez3Jaime Gomez-Gil4Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, SpainAgricultural Engineering Department, Federal University of Lavras, P.O. Box 3037, Lavras 37200-000, BrazilDepartamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, SpainDepartment of Electromechanical Engineering, Escuela Politécnica Superior, University of Burgos, Avda. Cantabria s/n, 09006 Burgos, SpainDepartamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, SpainSunflower 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.https://www.mdpi.com/1424-8220/23/5/2471classification systemconvolutional neural networkhigh oleic sunflower seed |
| spellingShingle | Mikel Barrio-Conde Marco Antonio Zanella Javier Manuel Aguiar-Perez Ruben Ruiz-Gonzalez Jaime Gomez-Gil A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties Sensors classification system convolutional neural network high oleic sunflower seed |
| title | A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties |
| title_full | A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties |
| title_fullStr | A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties |
| title_full_unstemmed | A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties |
| title_short | A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties |
| title_sort | deep learning image system for classifying high oleic sunflower seed varieties |
| topic | classification system convolutional neural network high oleic sunflower seed |
| url | https://www.mdpi.com/1424-8220/23/5/2471 |
| work_keys_str_mv | AT mikelbarrioconde adeeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT marcoantoniozanella adeeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT javiermanuelaguiarperez adeeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT rubenruizgonzalez adeeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT jaimegomezgil adeeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT mikelbarrioconde deeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT marcoantoniozanella deeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT javiermanuelaguiarperez deeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT rubenruizgonzalez deeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties AT jaimegomezgil deeplearningimagesystemforclassifyinghigholeicsunflowerseedvarieties |