Deep learning for grape variety recognition
| Autor: | Marcin Hernes, Ingolf Roemer, Agata Kozina, Marcin Pietranik, Bogdan Franczyk, Martin Schieck, Adrianna Kozierkiewicz |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology Agricultural engineering Vineyard Variety (cybernetics) Identification (information) Work (electrical) Agriculture 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence Viticulture business General Environmental Science |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| Popis: | The production of food in an ecologically and economically sustainable manner is of significant importance today. Agricultural producers are increasingly being accompanied by elements of Agriculture 4.0 such as automation and decision-making support. This work shows an example of how the digitization of viticulture can be significantly supported by Deep Learning. The work presents an approach that can overcome the loss of human expertise in grape identification by using image-recognition-techniques and residual network architectures. Our developed model for grape identification at a vineyard reaches an accuracy of 99% of correctly recognized grape varieties. |
| Databáze: | OpenAIRE |
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