Predicting spatial and temporal variability in soybean yield using deep learning and open source data.
ABSTRACT.- Spatial crop yield prediction provides valuable insights for supporting sustainable and precise crop management decisions. This study assessed the capabilities of advanced Deep Learning (DL) architectures in predicting within-field soybean yields using spectral bands from Sentinel-2 (RS-I...
I tiakina i:
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| Ētahi atu kaituhi: | , , , , |
| Hōputu: | article |
| Reo: | Ingarihi |
| I whakaputaina: |
2025
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| Ngā marau: | |
| Urunga tuihono: | https://ainfo.inia.uy/consulta/busca?b=pc&id=65041&biblioteca=vazio&busca=65041&qFacets=65041 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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Ngā tūemi rite: Predicting spatial and temporal variability in soybean yield using deep learning and open source data.
- Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model.
- Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability.
- Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction.
- Machine learning prediction of the Madden-Julian oscillation
- Respuesta del rendimiento de soja a la densidad de siembra en ambientes de productividad contrastante. [Response of Soybean Yield to Planting Density in Environments of Contrasting Productivity ].
- First report of Diaporthe miriciae and Diaporthe masirevicii causing soybean stem canker in Uruguay.