Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Alexandra A. Tamouridou"'
Publikováno v:
Computers and Electronics in Agriculture. 156:96-104
The presented approach demonstrates an automated way of crop disease identification on various leaf sample images corresponding to different crop species employing Local Binary Patterns (LBPs) for feature extraction and One Class Classification for c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b248d1e3c8ea04a1793a045bc16da5a
https://doi.org/10.1016/j.compag.2018.11.005
https://doi.org/10.1016/j.compag.2018.11.005
Autor:
Alexandra A. Tamouridou, Anastasia L. Lagopodi, Dimitrios Moshou, Xanthoula Eirini Pantazi, Thomas Alexandridis, Javid Kashefi
Publikováno v:
Computers and Electronics in Agriculture. 139:224-230
Remote sensing has been used for species discrimination and for operational weed mapping. In the study presented here, the detection and mapping of Silybum marianum using a hierarchical self-organising map is reported. A multispectral camera (green-r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1e7643e8d31e15efae27c2664ee81f07
https://doi.org/10.1016/j.compag.2017.05.026
https://doi.org/10.1016/j.compag.2017.05.026
Autor:
Giorgos Kontouris, Alexandra A. Tamouridou, Xanthoula Eirini Pantazi, Thomas Alexandridis, Anastasia L. Lagopodi, Dimitrios Moshou
Publikováno v:
Computers and Electronics in Agriculture. 137:130-137
Identification of a smut fungus with no visual signs on leaves using non-destructive spectroscopy.The method was applied in-situ on live plants using low cost tools.Three innovative classifiers were tested to evaluate their performance.An independent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::021fdf32efdb1d56fe4d412713e7d8c6
https://doi.org/10.1016/j.compag.2017.03.017
https://doi.org/10.1016/j.compag.2017.03.017
Autor:
Javid Kashefi, Anastasia L. Lagopodi, Thomas Alexandridis, Dimitrios Moshou, Alexandra A. Tamouridou, Xanthoula Eirini Pantazi
Publikováno v:
International Journal of Remote Sensing. 38:2246-2259
Silybum marianum L. Gaertn weed has the tendency to grow in patches. In order to perform site-specific weed management, determining the spatial distribution of weeds is important for its eradication. Remote sensing has been used to perform species di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ccc49576a80ef2153c3406d346956ae2
https://doi.org/10.1080/01431161.2016.1252475
https://doi.org/10.1080/01431161.2016.1252475
Autor:
Alexandra A. Tamouridou, Thomas Alexandridis, Dimitrios Kasampalis, Javid Kashefi, Anastasia L. Lagopodi, Dimitrios Moshou, Giorgos Kontouris, Xanthoula Eirini Pantazi
Publikováno v:
Sensors (Basel, Switzerland)
Sensors; Volume 17; Issue 10; Pages: 2307
Sensors, Vol 17, Iss 10, p 2307 (2017)
Sensors; Volume 17; Issue 10; Pages: 2307
Sensors, Vol 17, Iss 10, p 2307 (2017)
Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63419d563c872d66ffe3a32677541e8e
https://pubmed.ncbi.nlm.nih.gov/29019957
https://pubmed.ncbi.nlm.nih.gov/29019957
Publikováno v:
IFIP Advances in Information and Communication Technology ISBN: 9783319449432
AIAI
AIAI
The current application concerns a new approach for disease recognition of vine leaves based on Local Binary Patterns (LBPs). The LBP approach was applied on color digital pictures with a natural complex background that contained infected leaves. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::041bb4ab4b630ec5c8a412b9c95f4928
https://doi.org/10.1007/978-3-319-44944-9_27
https://doi.org/10.1007/978-3-319-44944-9_27
