Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Eleanna Vali"'
Autor:
Nikos Mylonas, Ioannis Malounas, Sofia Mouseti, Eleanna Vali, Borja Espejo-Garcia, Spyros Fountas
Publikováno v:
Smart Agricultural Technology, Vol 2, Iss , Pp 100028- (2022)
In modern agriculture, visual recognition systems based on deep learning are arising to allow autonomous machines to execute field operations in crops. However, for obtaining high performances, these methods need high amounts of data, which are usual
Externí odkaz:
https://doaj.org/article/538b7cb068d9449b9d4e0b673a6acaa6
Publikováno v:
AI, Vol 2, Iss 1, Pp 34-47 (2021)
In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, t
Externí odkaz:
https://doaj.org/article/d9f8c72668554c7cb12b5a9ea2d3ec68
Publikováno v:
Frontiers in Plant Science, Vol 12 (2021)
The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality
Externí odkaz:
https://doaj.org/article/c3e6542ed8ba48b787516e67d622d29a
Publikováno v:
Biosystems Engineering. 204:79-89
In recent years, automatic weed control has emerged as a promising alternative for reducing the amount of herbicide applied to the field, instead of conventional spraying. The use of artificial intelligence through the implementation of deep learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c2297f2240e09003fd16759b0b8b864
https://doi.org/10.1016/j.biosystemseng.2021.01.014
https://doi.org/10.1016/j.biosystemseng.2021.01.014
Publikováno v:
AI, Vol 2, Iss 4, Pp 34-47 (2021)
AI
Volume 2
Issue 1
Pages 4-47
AI
Volume 2
Issue 1
Pages 4-47
In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37e486921687d8cd7e7fc5a03247c229
https://www.mdpi.com/2673-2688/2/1/4
https://www.mdpi.com/2673-2688/2/1/4
Autor:
Evaggelos Spyrou, Antonios Papadakis, Phivos Mylonas, Ioannis Vernikos, Eleanna Vali, George Pikramenos, Eirini Mathe
Publikováno v:
Neural Computing and Applications. 32:17181-17195
The collection of video data for action recognition is very susceptible to measurement bias; the equipment used, camera angle and environmental conditions are all factors that majorly affect the distribution of the collected dataset. Inevitably, trai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49a6e89b29e9b600f64745debbf6b946
https://doi.org/10.1007/s00521-020-05162-5
https://doi.org/10.1007/s00521-020-05162-5
Publikováno v:
Frontiers in Plant Science, Vol 12 (2021)
Frontiers in Plant Science
Frontiers in Plant Science
The most common method for determining wine grape quality characteristics is to perform sample-based laboratory analysis, which can be time-consuming and expensive. In this article, we investigate an alternative approach to predict wine grape quality
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3581a650b9d9ccf6a0d7e1da1044c8d8
https://www.frontiersin.org/articles/10.3389/fpls.2021.683078/full
https://www.frontiersin.org/articles/10.3389/fpls.2021.683078/full