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Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower
View ORCID ProfileEtienne David, View ORCID ProfileGaëtan Daubige, François Joudelat, Philippe Burger, View ORCID ProfileAlexis Comar, View ORCID ProfileBenoit de Solan, View ORCID ProfileFrédéric Baret
doi: https://doi.org/10.1101/2021.04.27.441631
Etienne David
1INRAe, UMR EMMAH, Avignon, France
2Arvalis – Institut du végétal, Avignon, France
Gaëtan Daubige
2Arvalis – Institut du végétal, Avignon, France
François Joudelat
3ITB – Institut Technique de la Betterave, Paris, France
Philippe Burger
4INRAe, UE Grandes Cultures Auzeville (GCA), Toulouse, France
Alexis Comar
5Hiphen, 22b Rue Charrue, Avignon, France
Benoit de Solan
2Arvalis – Institut du végétal, Avignon, France
Frédéric Baret
1INRAe, UMR EMMAH, Avignon, France
Posted March 25, 2022.
Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower
Etienne David, Gaëtan Daubige, François Joudelat, Philippe Burger, Alexis Comar, Benoit de Solan, Frédéric Baret
bioRxiv 2021.04.27.441631; doi: https://doi.org/10.1101/2021.04.27.441631
Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower
Etienne David, Gaëtan Daubige, François Joudelat, Philippe Burger, Alexis Comar, Benoit de Solan, Frédéric Baret
bioRxiv 2021.04.27.441631; doi: https://doi.org/10.1101/2021.04.27.441631
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