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Authors
Zhai, Ruifang
Tang, Ning
Liu, Zhi
Tao, Sha
Huang, Yupu
Jiang, Xue
Du, Aobo
Wang, Jiashi
Luo, Tao
Liu, Jinbao
Garzon-Martınez, Gina A.
Corke, Fiona M.K.
Doonan, John H.
Yang, Wanneng https://orcid.org/0000-0003-1095-1355
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Funding
Funding for this research was provided by:
National Key Research and Development Program of China (2023YFF1000100)
National Key Research and Development Program of China (2022YFD2002304)
National Natural Science Foundation of China (U21A20205)
National Natural Science Foundation of China (32270281)
National Natural Science Foundation of China (32300281)
Key Agricultural Core Technology Research Project in Hubei Province (HBNYHXGG2023-9)
Natural Science Foundation of Henan Province (232300421028)
Natural Science Foundation of Henan Province (222300420107)
Fundamental Research Funds for the Central Universities (2662024ZKPY003)
Henan Provincial Science and Technology R&D Program Joint Fund (242301420130)
China-UK Joint Phenomics Consortium (BB/R02118X/1)
National Capability Grant in Plant Phenotyping (BBS/E/W/0012844A)
European Plant Phenotyping Network: Grant Agreement (731013)
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This article is maintained by: Elsevier
Article Title: APTES: a high-throughput deep learning–based Arabidopsis phenotypic trait estimation system for individual leaves and siliques
Journal Title: aBIOTECH
CrossRef DOI link to publisher maintained version: https://doi.org/10.1007/s42994-025-00239-y
Content Type: article
Copyright: Copyright © 2025 © 2025 THE AUTHORS. Published by Elsevier B.V. on behalf of Agricultural Information Institute of CAAS. Published by Elsevier B.V.
