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Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms
Cita
Flores Peña, Pablo 
ORCID: https://orcid.org/0009-0005-7219-6879, Ale Isaac Khoueini, Mohammad Sadeq ORCID: https://orcid.org/0000-0001-8773-7184, Gifu, Daniela ORCID: https://orcid.org/0000-0001-8116-053X, Pechlivani, Eleftheria Maria ORCID: https://orcid.org/0000-0001-6385-2815 and Ragab, Ahmed Refaat ORCID: https://orcid.org/0000-0002-6897-6048
(2025).
Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms.
"Drones", v. 9
(n. 2);
pp. 1-17.
ISSN 2504-446X.
https://doi.org/10.3390/drones9020129.
ORCID: https://orcid.org/0009-0005-7219-6879, Ale Isaac Khoueini, Mohammad Sadeq ORCID: https://orcid.org/0000-0001-8773-7184, Gifu, Daniela ORCID: https://orcid.org/0000-0001-8116-053X, Pechlivani, Eleftheria Maria ORCID: https://orcid.org/0000-0001-6385-2815 and Ragab, Ahmed Refaat ORCID: https://orcid.org/0000-0002-6897-6048
(2025).
Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms.
"Drones", v. 9
(n. 2);
pp. 1-17.
ISSN 2504-446X.
https://doi.org/10.3390/drones9020129.
Descripción
| Título: | Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms |
|---|---|
| Autor/es: |
|
| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Drones |
| Fecha: | 11 Febrero 2025 |
| ISSN: | 2504-446X |
| Volumen: | 9 |
| Número: | 2 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Artificial intelligence (AI) in agriculture; Precision agriculture; Soil texture mapping; System; UAV-based hyperspectral imaging; Vegetation indexes |
| Escuela: | E.T.S.I. Industriales (UPM) |
| Departamento: | Otro |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.
Proyectos asociados
Tipo
Código
Acrónimo
Responsable
Título
Horizonte Europa
No. 101157922
E-SPFdigit
Sin especificar
Emergent soil, plant and food onsite digital services on chemical and biological contaminants.
Más información
| ID de Registro: | 94773 |
|---|---|
| Identificador DC: | https://oa.upm.es/94773/ |
| Identificador OAI: | oai:oa.upm.es:94773 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/10360816 |
| Identificador DOI: | 10.3390/drones9020129 |
| URL Oficial: | https://www.mdpi.com/2504-446X/9/2/129 |
| Depositado por: | iMarina Portal Científico |
| Depositado el: | 13 Mar 2026 14:58 |
| Ultima Modificación: | 13 Mar 2026 14:58 |
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