High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data

High-throughput phenotyping for non-destructive estimation of soybean fresh biomass using a machine learning model and temporal UAV data

Abstract Background Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding progr...

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Bibliographic Details
Main Authors: Predrag Ranđelović, Vuk Đorđević, Jegor Miladinović, Slaven Prodanović, Marina Ćeran, Johann Vollmann
Format: Article
Language:English
Published: BMC 2023-08-01
Series:Plant Methods
Subjects:
High-throughput phenotyping
Biomass
Soybean
Machine learning
UAV
Online Access:https://doi.org/10.1186/s13007-023-01054-6

Internet

https://doi.org/10.1186/s13007-023-01054-6

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