Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach
Identification
- Clé publique
- radar:r-e231a06ac78394b81fba631c1bab323b
- Auteurs
- Adele Therias · Azarakhsh Rafiee · Stef Lhermitte · Philip van der Lugt · Roderik Lindenbergh
- Année déclarée
- 2025
- Langue déclarée
- en
- Type source
- article
- Première détection
- 2026-11-14T09:36:56.845252+00:00
- Dernière observation des métadonnées
- 2026-11-14T09:36:56.845252+00:00
- Dernière vérification HTTP
- Non effectuée
Institutions déclarées par la source
Delft University of Technology · KU Leuven
Pays d’affiliation déclarés
BE · NL
Résumé documentaire — langue originale
The production of cocoa beans contributes to 7.5% of European Union (EU) driven deforestation. As a result, the recent European Union Deforestation-free Regulation (EUDR) mandates producers to track cocoa farm extents comprehensively. While Remote Sensing has enormous capacity in dynamic crop monitoring, cocoa crop detection shows challenges due to cocoa complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous research on cocoa crop detection has mainly focused on pixel-based classification, disregarding spatial context. In this research we have performed a semantic segmentation approach to incorporate spatial configuration and enhance cocoa crop detection. We have applied Convolutional Neural Network (CNN) for the to semantic segmentation of cocoa parcels, considering both spectral and spatial characteristics. Additionally, we have evaluated the impact of combining Synthetic Aperture RADAR (SAR) and MSI (Multi-Spectral Imagery) data in the training of a CNN to demonstrate the importance of texture, moisture, and canopy characteristics in identifying cocoa canopies. The impact of MSI dataset stack with different SAR polarizations, seasons and temporality has been evaluated. The methodology is tested on Sentinel 1 and 2 data over an area of 100 x 100km in Ghana for which an extensive ground truth data set of almost 90,000 polygons was available for training and validation. The results show that the addition of single-day and temporal SAR to a single-day MSI image can improve the predictions, reaching an F1 score of 86.62%. This research demonstrates the influence of SAR measurements, seasons, polarization, and ground truth classes on the semantic segmentation of cocoa.
Identifiants
- OPENALEX
- https://openalex.org/W4411863238
- DOI
- 10.1016/j.rsase.2025.101652
Repères documentaires
- Famille documentaire
- article
- Types déclarés par les sources
- article
- Éditeurs
- Elsevier BV
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Sources
OpenAlex · Document 1
Citer cette référence
Adele Therias; Azarakhsh Rafiee; Stef Lhermitte; Philip van der Lugt; Roderik Lindenbergh. Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach. 2025. https://doi.org/10.1016/j.rsase.2025.101652
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- APA
- Adele Therias; Azarakhsh Rafiee; Stef Lhermitte; Philip van der Lugt; Roderik Lindenbergh. (2025). Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach. https://doi.org/10.1016/j.rsase.2025.101652
- Chicago
- Adele Therias; Azarakhsh Rafiee; Stef Lhermitte; Philip van der Lugt; Roderik Lindenbergh. Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach. 2025. https://doi.org/10.1016/j.rsase.2025.101652
- ISO 690
- Adele Therias; Azarakhsh Rafiee; Stef Lhermitte; Philip van der Lugt; Roderik Lindenbergh. Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach. 2025. https://doi.org/10.1016/j.rsase.2025.101652
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Source bibliographique / publication d’origine · https://doi.org/10.1016/j.rsase.2025.101652
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