Home // GEOProcessing 2025, The Seventeenth International Conference on Advanced Geographic Information Systems, Applications, and Services // View article
Authors:
Alba Gómez Liébana
Ruth María Córdoba Ortega
Juan José Cubillas Mercado
Lidia Ortega Alvarado
Keywords: Hyperspectral Imaging (HSI); Deep Learning (DL); Convolutional Neural Networks (CNN); Precision Agriculture; Olive Variety Classification; UAV-based Imaging; Spectral-Spatial Analysis; Arbequina; Picual.
Abstract:
The automated classification of olive varieties plays a crucial role in Precision Agriculture, enabling optimized resource allocation, improved irrigation strategies, and enhanced olive oil quality. This study explores the integration of Hyperspectral Imaging (HSI) and Deep Learning (DL) to classify olive varieties, focusing on Picual. Utilizing drone-acquired hyperspectral data, a Convolutional Neural Network (CNN) was employed to analyze leaf reflectance and extract spectral-spatial features with high accuracy. The Unmanned Aerial Vehicle (UAV)-based HSI system captures high-resolution spectral data, allowing for the detection of subtle differences in reflectance patterns that are imperceptible to traditional sensors. The study demonstrates that the proposed deep learning approach achieves an accuracy of approximately 90% in classifying olive varieties, significantly outperforming traditional machine learning methods. These findings highlight the potential of hyperspectral deep learning in agricultural applications, paving the way for scalable, efficient, and sustainable orchard management.
Pages: 11 to 16
Copyright: Copyright (c) IARIA, 2025
Publication date: May 18, 2025
Published in: conference
ISSN: 2308-393X
ISBN: 978-1-68558-269-2
Location: Nice, France
Dates: from May 18, 2025 to May 22, 2025