Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with Machine Learning Models - researchr publication related

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Patrick Helber, Benjamin Bischke, Peter Habelitz, Cristhian Sanchez, Deepak Pathak, Miro Miranda, Hiba Najjar, Francisco Mena, Jayanth Siddamsetty, Diego Arenas, Michaela Vollmer, Marcela Charfuelan, Marlon Nuske, Andreas Dengel 0001. Crop Yield Prediction: An Operational Approach to Crop Yield Modeling on Field and Subfield Level with Machine Learning Models. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, Pasadena, CA, USA, July 16-21, 2023. pages 2763-2766, IEEE, 2023. [doi]

The following publications are possibly variants of this publication:

An Operational Approach to Large-Scale Crop Yield Prediction with Spatio-Temporal Machine Learning ModelsPatrick Helber, Benjamin Bischke, Carolin Packbier, Peter Habelitz, Florian Seefeldt. igarss 2024: 4299-4302 [doi]

Predicting Crop Yield with Machine Learning: An Extensive Analysis of Input Modalities and Models on a Field and Sub-Field LevelDeepak Pathak, Miro Miranda, Francisco Mena, Cristhian Sanchez, Patrick Helber, Benjamin Bischke, Peter Habelitz, Hiba Najjar, Jayanth Siddamsetty, Diego Arenas, Michaela Vollmer, Marcela Charfuelan, Marlon Nuske, Andreas Dengel 0001. igarss 2023: 2767-2770 [doi]

Explainability of Subfield Level Crop Yield Prediction Using Remote SensingHiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel 0001. staeors, 18:4141-4161, 2025. [doi]

Multi-Modal Fusion Methods with Local Neighborhood Information for Crop Yield Prediction at Field and Subfield LevelsMiro Miranda, Deepak Pathak, Marlon Nuske, Andreas Dengel 0001. igarss 2024: 4307-4311 [doi]

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