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A deep learning framework to discern and count microscopic nematode eggs
In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybean yield loss is negatively correlated with the de...
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| Publicado no: | Sci Rep |
|---|---|
| Main Authors: | , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6002363/ https://ncbi.nlm.nih.gov/pubmed/29904135 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-018-27272-w |
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