Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models
During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging du...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1370 |
| _version_ | 1797263909489475584 |
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| author | Samuel Domínguez-Cid Diego Francisco Larios Julio Barbancho Francisco Javier Molina Javier Antonio Guerra Carlos León |
| author_facet | Samuel Domínguez-Cid Diego Francisco Larios Julio Barbancho Francisco Javier Molina Javier Antonio Guerra Carlos León |
| author_sort | Samuel Domínguez-Cid |
| collection | DOAJ |
| description | During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications. |
| first_indexed | 2024-04-25T00:20:30Z |
| format | Article |
| id | doaj.art-d71456aca0794e3598bd13d6b1f361ce |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:20:30Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d71456aca0794e3598bd13d6b1f361ce2024-03-12T16:54:32ZengMDPI AGSensors1424-82202024-02-01245137010.3390/s24051370Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight ModelsSamuel Domínguez-Cid0Diego Francisco Larios1Julio Barbancho2Francisco Javier Molina3Javier Antonio Guerra4Carlos León5Department of Electronic Technology, Escuela Politecnica Superior, Universidad de Sevilla, 41011 Seville, SpainDepartment of Electronic Technology, Escuela Politecnica Superior, Universidad de Sevilla, 41011 Seville, SpainDepartment of Electronic Technology, Escuela Politecnica Superior, Universidad de Sevilla, 41011 Seville, SpainDepartment of Electronic Technology, Escuela Politecnica Superior, Universidad de Sevilla, 41011 Seville, SpainDepartment of Electronic Technology, Escuela Politecnica Superior, Universidad de Sevilla, 41011 Seville, SpainDepartment of Electronic Technology, Escuela Politecnica Superior, Universidad de Sevilla, 41011 Seville, SpainDuring the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications.https://www.mdpi.com/1424-8220/24/5/1370hyperspectral imagingolivesprecision agriculturemachine learningpattern recognition |
| spellingShingle | Samuel Domínguez-Cid Diego Francisco Larios Julio Barbancho Francisco Javier Molina Javier Antonio Guerra Carlos León Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models Sensors hyperspectral imaging olives precision agriculture machine learning pattern recognition |
| title | Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models |
| title_full | Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models |
| title_fullStr | Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models |
| title_full_unstemmed | Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models |
| title_short | Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models |
| title_sort | identification of olives using in field hyperspectral imaging with lightweight models |
| topic | hyperspectral imaging olives precision agriculture machine learning pattern recognition |
| url | https://www.mdpi.com/1424-8220/24/5/1370 |
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