Artificial Intelligence (AI) for Crop Yield Forecasting
This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Won...
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| Format: | Article |
| Language: | English |
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The University of Florida George A. Smathers Libraries
2022-04-01
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| Series: | EDIS |
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| Online Access: | https://journals.flvc.org/edis/article/view/129532 |
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| _version_ | 1825198630787612672 |
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| author | Clyde Fraisse Yiannis Ampatzidis Sandra Guzmán Wonsuk Lee Christopher Martinez Sanjay Shukla Aditya Singh Ziwen Yu |
| author_facet | Clyde Fraisse Yiannis Ampatzidis Sandra Guzmán Wonsuk Lee Christopher Martinez Sanjay Shukla Aditya Singh Ziwen Yu |
| author_sort | Clyde Fraisse |
| collection | DOAJ |
| description |
This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Wonsuk Lee, Christopher Martinez, Sanjay Shukla, Aditya Singh, and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, April 2022.
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| format | Article |
| id | doaj-art-ff0c035721f84c11bbad8202733837bd |
| institution | Kabale University |
| issn | 2576-0009 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | The University of Florida George A. Smathers Libraries |
| record_format | Article |
| series | EDIS |
| spelling | doaj-art-ff0c035721f84c11bbad8202733837bd2025-02-08T05:43:30ZengThe University of Florida George A. Smathers LibrariesEDIS2576-00092022-04-0120222Artificial Intelligence (AI) for Crop Yield ForecastingClyde Fraisse0https://orcid.org/0000-0001-9875-2187Yiannis Ampatzidis1Sandra Guzmán2https://orcid.org/0000-0003-0735-7179Wonsuk Lee3https://orcid.org/0000-0002-9420-4789Christopher Martinez4https://orcid.org/0000-0003-2959-807XSanjay Shukla5Aditya Singh6https://orcid.org/0000-0001-5559-9151Ziwen Yu7University of FloridaUniversity of FloridaUniversity of FloridaUniversity of FloridaUniversity of FloridaUniversity of FloridaUniversity of FloridaUniversity of Florida This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Wonsuk Lee, Christopher Martinez, Sanjay Shukla, Aditya Singh, and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, April 2022. https://journals.flvc.org/edis/article/view/129532yield predictioncornwheatstrawberrycitrus |
| spellingShingle | Clyde Fraisse Yiannis Ampatzidis Sandra Guzmán Wonsuk Lee Christopher Martinez Sanjay Shukla Aditya Singh Ziwen Yu Artificial Intelligence (AI) for Crop Yield Forecasting EDIS yield prediction corn wheat strawberry citrus |
| title | Artificial Intelligence (AI) for Crop Yield Forecasting |
| title_full | Artificial Intelligence (AI) for Crop Yield Forecasting |
| title_fullStr | Artificial Intelligence (AI) for Crop Yield Forecasting |
| title_full_unstemmed | Artificial Intelligence (AI) for Crop Yield Forecasting |
| title_short | Artificial Intelligence (AI) for Crop Yield Forecasting |
| title_sort | artificial intelligence ai for crop yield forecasting |
| topic | yield prediction corn wheat strawberry citrus |
| url | https://journals.flvc.org/edis/article/view/129532 |
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