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pro vyhledávání: '"Miranda, Miro"'
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making processes of t
Externí odkaz:
http://arxiv.org/abs/2407.08274
Autor:
Mena, Francisco, Pathak, Deepak, Najjar, Hiba, Sanchez, Cristhian, Helber, Patrick, Bischke, Benjamin, Habelitz, Peter, Miranda, Miro, Siddamsetty, Jayanth, Nuske, Marlon, Charfuelan, Marcela, Arenas, Diego, Vollmer, Michaela, Dengel, Andreas
Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, and industry stakeholders. However, this task is complex and depends on multiple factors, such as environmental conditions, soil prope
Externí odkaz:
http://arxiv.org/abs/2401.11844
Autor:
Pathak, Deepak, Miranda, Miro, Mena, Francisco, Sanchez, Cristhian, Helber, Patrick, Bischke, Benjamin, Habelitz, Peter, Najjar, Hiba, Siddamsetty, Jayanth, Arenas, Diego, Vollmer, Michaela, Charfuelan, Marcela, Nuske, Marlon, Dengel, Andreas
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and ma
Externí odkaz:
http://arxiv.org/abs/2308.08948
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