Davide Cammarano
researcher
Facts
7 properties- Instance of
- human
- Occupation
- researcher
- Employer
- University of Manchester, Purdue University, University of Dundee, James Hutton Institute, University of Florida, Aarhus University[2][3][4]
- Educated at
- University of Melbourne, University of Basilicata
🏷️ Also known as
en Davide Cammarano
ja ダヴィデ・カンマラーノ
🔖 Identifiers · 7
- ORCID iD
- 0000-0003-0918-550X
- VIAF cluster ID
- 33170925021190152636
- Library of Congress Authorities ID
- no2024018416
- IRIS UNISS author ID
- 64197
- J9U entity ID
- 987012722638005171
- Scopus author ID
- 29067487400
- Yale LUX ID
- person/e200b830-015f-49d9-a8b3-b34c5267e730
🔗 Connections
Author of 9
Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations,
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change,
Drought dynamics in California and Mississippi: A wavelet analysis of meteorological to agricultural drought transition,
Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework,
Improving wheat yield prediction integrating proximal sensing and weather data with machine learning,
Integrated UAV-Based Multi-Source Data for Predicting Maize Grain Yield Using Machine Learning Approaches,
Integrating machine learning with spatial analysis for enhanced soil interpolation: Balancing accuracy and visualization,
Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images
+1 more
← Authored by 9
Benchmark data set for wheat growth models: field experiments and AgMIP multi-model simulations,
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change,
Drought dynamics in California and Mississippi: A wavelet analysis of meteorological to agricultural drought transition,
Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework,
Improving wheat yield prediction integrating proximal sensing and weather data with machine learning,
Integrated UAV-Based Multi-Source Data for Predicting Maize Grain Yield Using Machine Learning Approaches,
Integrating machine learning with spatial analysis for enhanced soil interpolation: Balancing accuracy and visualization,
Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images
+1 more
Affiliated with 5
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