Artículo
Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models
Autor/es | Domínguez Cid, Samuel
![]() ![]() ![]() ![]() ![]() Larios Marín, Diego Francisco ![]() ![]() ![]() ![]() ![]() ![]() ![]() Barbancho Concejero, Julio ![]() ![]() ![]() ![]() ![]() ![]() ![]() Molina Cantero, Francisco Javier ![]() ![]() ![]() ![]() ![]() Guerra Coronado, Javier Antonio ![]() ![]() ![]() ![]() ![]() ![]() León de Mora, Carlos ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
Departamento | Universidad de Sevilla. Departamento de Tecnología Electrónica |
Fecha de publicación | 2024 |
Fecha de depósito | 2024-04-01 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Universidad de Sevilla |
Identificador del proyecto | PYC20 RE 090 US
![]() 802C2000097 ![]() 2021/C005/0014786 ![]() |
Cita | Domínguez Cid, S., Larios Marín, D.F., Barbancho Concejero, J., Molina Cantero, F.J., Guerra Coronado, J.A. y León de Mora, C. (2024). Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models. Sensors, 24 (5), Article number 1370. https://doi.org/10.3390/s24051370. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Idenfifications of olives.pdf | 4.391Mb | ![]() | Ver/ | |