Predicting soybean seed germination using the tetrazolium test and computer intelligence
ABSTRACT Seed quality is critical to agricultural yield, and traditional testing can be time-consuming and subjective. Therefore, the use of machine learning can provide an efficient approach for predicting germination. The aim of this work was to investigate algorithms that, together with tetrazoli...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidade Federal do Ceará
2025-07-01
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| Series: | Revista Ciência Agronômica |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902025000100668&lng=en&tlng=en |
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