High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models...

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Bibliographic Details
Main Authors: Mashiro Okada, Clément Barras, Yusuke Toda, Kosuke Hamazaki, Yoshihiro Ohmori, Yuji Yamasaki, Hirokazu Takahashi, Hideki Takanashi, Mai Tsuda, Masami Yokota Hirai, Hisashi Tsujimoto, Akito Kaga, Mikio Nakazono, Toru Fujiwara, Hiroyoshi Iwata
Format: Article
Language:English
Published: American Association for the Advancement of Science (AAAS) 2024-01-01
Series:Plant Phenomics
Online Access:https://spj.science.org/doi/10.34133/plantphenomics.0244

Internet

https://spj.science.org/doi/10.34133/plantphenomics.0244

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