Robust seed germination prediction using deep learning and RGB image data
Abstract Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with wasteful disqualification of seed lots hol...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e6d8b5732c46423491df96fce6f6479b |
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| Sumario: | Abstract Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with wasteful disqualification of seed lots holding considerable amounts of good seeds and further translates to financial losses and supply chain insecurity. Here, we present the first-ever generic germination prediction technology that is based on deep learning and RGB image data and facilitates seed classification by seed germinability and usability, two facets of germination fate. We show technology competence to render dozens of disqualified seed lots of seven vegetable crops, representing different genetics and production pipelines, industrially appropriate, and to adequately classify lots by utilizing available crop-level image data, instead of lot-specific data. These achievements constitute a major milestone in the deployment of this technology for industrial seed sorting by germination fate for multiple crops. |
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