Yewle, Akshay Dagadu, Mirzayeva, Laman and Karakuş, Oktay  ORCID: https://orcid.org/0000-0001-8009-9319
2025.
Multi-modal data fusion and deep ensemble learning for accurate crop yield prediction.
Remote Sensing Applications: Society and Environment
38
, 101613.
10.1016/j.rsase.2025.101613
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Abstract
This study introduces RicEns-Net, a novel deep ensemble model for rice yield prediction in the Mekong Delta region of Vietnam, integrating diverse data sources through multi-modal data fusion. The model leverages synthetic aperture radar (SAR), optical remote sensing (Sentinel-1/2/3) and meteorological measurements (surface temperature, rainfall) to improve prediction precision. A comprehensive feature selection reduced over 100 potential predictors to 15 key features across 5 data modalities, mitigating the “curse of dimensionality” where the initial field data were acquired through Ernst & Young’s (EY) Open Science Challenge 2023. RicEns-Net outperforms previous state-of-the-art models (including winners of the EY Open Science Challenge), achieving a mean absolute error (MAE) of 336 kg/Ha, roughly 5%–6% of the lowest regional yield, and a high R2, indicating robust predictive capability. These results underscore the benefit of deep ensembles in precision agriculture and demonstrate the potential of multi-modal data integration for more accurate crop yield forecasting.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Elsevier |
| ISSN: | 2352-9385 |
| Funders: | N/A |
| Date of First Compliant Deposit: | 28 August 2025 |
| Date of Acceptance: | 28 May 2025 |
| Last Modified: | 29 Aug 2025 10:31 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/180702 |
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