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Predicting Crop Yield with Machine Learning: An Extensive A..:

, In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium,

Pathak, Deepak ; Miranda, Miro ; Mena, Francisco... - p. 2767-2770 , 2023

Link: https://doi.org/10.1109/IGARSS52108.2023.10282318

RT 
T1 <cTitle>IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium</cTitle>: T1 <Title>Predicting Crop Yield with Machine Learning: An Extensive Analysis of Input Modalities and Models on a Field and Sub-Field Level</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10282318&Exemplar=1&LAN=DE
				
A1 Pathak, Deepak
A1 Miranda, Miro
A1 Mena, Francisco
A1 Sanchez, Cristhian
A1 Helber, Patrick
A1 Bischke, Benjamin
A1 Habelitz, Peter
A1 Najjar, Hiba
A1 Siddamsetty, Jayanth
A1 Arenas, Diego
A1 Vollmer, Michaela
A1 Charfuelan, Marcela
A1 Nuske, Marlon
A1 Dengel, Andreas
YR 2023
SN 2153-7003
K1 Analytical models
K1 Geoscience and remote sensing
K1 Machine learning
K1 Predictive models
K1 Soil
K1 Data models
K1 Satellite images
K1 Sentinel-2
K1 Multi-modal data
K1 Early fusion
K1 Precision Farming
K1 Yield Maps
SP 2767
OP 2770
LK http://dx.doi.org/https://doi.org/10.1109/IGARSS52108.2023.10282318
DO https://doi.org/10.1109/IGARSS52108.2023.10282318
SF ELIB - SuUB Bremen

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