All Publications

Peer-reviewed work on crop-yield prediction, multimodal learning, and trustworthy AI. For the latest updates, see my Google Scholar profile.

M. Miranda, D. Pathak, P. Helber, B. Bischke, H. Najjar, F. Mena, C. Sanchez, A. Pai, D. Arenas, M. Valdenegro Toro, M. Charfuelan, M. Nuske, A. Dengel (2026). YieldSAT: A multimodal benchmark dataset for high-resolution crop yield prediction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026.
Project DOI
F. Mena, D. Pathak, H. Najjar, C. Sanchez, P. Helber, B. Bischke, P. Habelitz, M. Miranda, J. Siddamsetty, M. Nuske, M. Charfuelan, D. Arenas, M. Vollmer, A. Dengel (2025). Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction. Remote Sensing of Environment, Vol. 318, 2025.
DOI
H. Najjar, D. Pathak, M. Nuske, A. Dengel (2025). Intrinsic explainability of multimodal learning for crop yield simulation. Computers and Electronics in Agriculture, Vol. 239, 2025.
DOI
M. Miranda, D. Pathak, M. Nuske, A. Dengel (2024). Multi-modal fusion methods with local neighborhood information for crop yield prediction at field and subfield levels. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2024.
DOI
C. Sanchez, D. Pathak, M. Miranda, M. Charfuelan, P. Helber, M. Nuske, B. Bischke, P. Habelitz, N. Rahman, F. Mena, H. Najjar, J. Siddamsetty, D. Arenas, M. Vollmer, A. Dengel (2023). Influence of data cleaning techniques on sub-field yield predictions. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023.
DOI
D. Pathak, M. Miranda, F. Mena, C. Sanchez, P. Helber, B. Bischke, P. Habelitz, H. Najjar, J. Siddamsetty, D. Arenas, M. Vollmer, M. Charfuelan, M. Nuske, A. Dengel (2023). Predicting crop yield with machine learning: An extensive analysis of input modalities and models on a field and sub-field level. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023.
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