Automated phenotyping of soybean stomatal responses to water deficit using YOLOv8
Abstract Artificial intelligence applied to plant phenotyping is crucial for consistent results, as stomata classification under stress impacts physiology, water use efficiency, and productivity. Manual analysis is laborious and error-prone, limiting the efficiency and accuracy of evaluations. In th...
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| Principais autores: | , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer Nature
2026-06-01
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| coleção: | Discover Plants |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/s44372-026-00642-9 |
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