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Titel:

Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Genze, Nikita; Bharti, Richa; Grieb, Michael; Schultheiss, Sebastian J.; Grimm, Dominik G.
Nicht-TUM Koautoren:
ja
Kooperation:
national
Abstract:
Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with re...     »
Intellectual Contribution:
Discipline-based Research
Zeitschriftentitel:
Plant Methods
Jahr:
2020
Band / Volume:
16
Heft / Issue:
1
Nachgewiesen in:
Web of Science
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1186/s13007-020-00699-x
Verlag / Institution:
Springer Science and Business Media LLC
E-ISSN:
1746-4811
Status:
Verlagsversion / published
Publikationsdatum:
01.12.2020
Urteilsbesprechung:
0
Peer reviewed:
Ja
commissioned:
commissioned by company
Technology:
Ja
Interdisziplinarität:
Nein
Leitbild:
;
Ethics und Sustainability:
Nein
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