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Correction:A bimodal image dataset for seed classification from the visible and near-infrared spectrum | Synapse
March 3, 2026
Open Access
Correction:A bimodal image dataset for seed classification from the visible and near-infrared spectrum
MK
Maksim Kukushkin
Martin Luther University Halle-Wittenberg
MB
Martin Bogdan
German Research Centre for Artificial Intelligence
SG
Simon Goertz
Norddeutsche Pflanzenzucht Hans-Georg Lembke (Germany)
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Key Points
The study reveals effective seed classification from both visible and near-infrared spectra, enhancing accuracy.
Using a bimodal image dataset, classification accuracy reaches significant levels, potentially impacting agriculture.
Data were collected using advanced imaging technology, focusing on spectral responses from seed samples.
These findings highlight the need for further research to validate and apply this technique broadly within the agricultural sector.
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Kukushkin et al. (Fri,) studied this question.
synapsesocial.com/papers/69a76889badf0bb9e87e504b
https://doi.org/https://doi.org/10.1038/s41597-026-06771-w