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Developing a learning mechanism for a spatial decision support system for medfly control in citrus
Year:
2007
Authors :
Alchanatis, Victor
;
.
Cohen, Avihu
;
.
Cohen, Yafit
;
.
Hetzroni, Amots
;
.
Volume :
Co-Authors:
Cohen, A., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel, Division of Agricultural Engineering, Faculty of Civil and Environmental Engineering, Technion -Israel Institute of Technology, Haifa, Israel
Cohen, Y., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel
Broday, D., Division of Agricultural Engineering, Faculty of Civil and Environmental Engineering, Technion -Israel Institute of Technology, Haifa, Israel
Hetzroni, A., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel
Alchanatis, V., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel
Timar, D., PPMB - Citrus Division, Israel Cohen Institute for Biological Control, Israel
Gazit, Y., PPMB - Citrus Division, Israel Cohen Institute for Biological Control, Israel
Facilitators :
From page:
723
To page:
730
(
Total pages:
8
)
Abstract:
An initial Spatial Decision Support System (SDSS) for Medfly infestation was developed to improve spraying actions. Beside the spray model that produces a spraying recommendations map, the SDSS has a learning mechanism that aims at realizing the ability of tuning the spray model or indicating data gaps. The learning mechanism compares between the SDSS recommendations and the expert decision and selects appropriate cases for learning. The learning mechanism algorithm is described and initial results are presented. The results indicate a high degree of agreement between the SDSS recommendations and the expert decisions. However, disagreements between the SDSS and the expert decision support the need for such a learning model and indicate ways of tuning the SDSS.
Note:
Related Files :
Agriculture
Artificial intelligence
Data gap
Decision support systems
Learning models
Spray model
Show More
Related Content
More details
DOI :
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
19790
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:31
Scientific Publication
Developing a learning mechanism for a spatial decision support system for medfly control in citrus
Cohen, A., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel, Division of Agricultural Engineering, Faculty of Civil and Environmental Engineering, Technion -Israel Institute of Technology, Haifa, Israel
Cohen, Y., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel
Broday, D., Division of Agricultural Engineering, Faculty of Civil and Environmental Engineering, Technion -Israel Institute of Technology, Haifa, Israel
Hetzroni, A., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel
Alchanatis, V., Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Bet Dagan, Israel
Timar, D., PPMB - Citrus Division, Israel Cohen Institute for Biological Control, Israel
Gazit, Y., PPMB - Citrus Division, Israel Cohen Institute for Biological Control, Israel
Developing a learning mechanism for a spatial decision support system for medfly control in citrus
An initial Spatial Decision Support System (SDSS) for Medfly infestation was developed to improve spraying actions. Beside the spray model that produces a spraying recommendations map, the SDSS has a learning mechanism that aims at realizing the ability of tuning the spray model or indicating data gaps. The learning mechanism compares between the SDSS recommendations and the expert decision and selects appropriate cases for learning. The learning mechanism algorithm is described and initial results are presented. The results indicate a high degree of agreement between the SDSS recommendations and the expert decisions. However, disagreements between the SDSS and the expert decision support the need for such a learning model and indicate ways of tuning the SDSS.
Scientific Publication
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