Leveraging edge artificial intelligence for sustainable agriculture

Effectively feeding a burgeoning world population is one of the main goals of sustainable agricultural practices. Digital technology, such as edge artificial intelligence (AI), has the potential to introduce substantial benefits to agriculture by enhancing farming practices that can improve agricult...

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Main Authors: El Jarroudi, M., Kouadio, L., Delfosse, P., Bock, C. H., Mahlein, A. K., Fettweis, X., Mercatoris, B., Adams, F., Lenné, J. M., Hamdioui, S.
Format: Journal Article
Language:Inglés
Published: 2024
Subjects:
Online Access:https://hdl.handle.net/10568/162970
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author El Jarroudi, M.
Kouadio, L.
Delfosse, P.
Bock, C. H.
Mahlein, A. K.
Fettweis, X.
Mercatoris, B.
Adams, F.
Lenné, J. M.
Hamdioui, S.
author_browse Adams, F.
Bock, C. H.
Delfosse, P.
El Jarroudi, M.
Fettweis, X.
Hamdioui, S.
Kouadio, L.
Lenné, J. M.
Mahlein, A. K.
Mercatoris, B.
author_facet El Jarroudi, M.
Kouadio, L.
Delfosse, P.
Bock, C. H.
Mahlein, A. K.
Fettweis, X.
Mercatoris, B.
Adams, F.
Lenné, J. M.
Hamdioui, S.
author_sort El Jarroudi, M.
collection Repository of Agricultural Research Outputs (CGSpace)
description Effectively feeding a burgeoning world population is one of the main goals of sustainable agricultural practices. Digital technology, such as edge artificial intelligence (AI), has the potential to introduce substantial benefits to agriculture by enhancing farming practices that can improve agricultural production efficiency, yield, quality and safety. However, the adoption of edge AI faces several challenges, including the need for innovative and efficient edge AI solutions and greater investment in infrastructure and training, all compounded by various environmental, social and economic constraints. Here we provide a roadmap for leveraging edge AI at the intersection of food production and sustainability.
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spelling CGSpace1629702025-12-08T10:11:39Z Leveraging edge artificial intelligence for sustainable agriculture El Jarroudi, M. Kouadio, L. Delfosse, P. Bock, C. H. Mahlein, A. K. Fettweis, X. Mercatoris, B. Adams, F. Lenné, J. M. Hamdioui, S. Artificial intelligence sustainable agriculture Effectively feeding a burgeoning world population is one of the main goals of sustainable agricultural practices. Digital technology, such as edge artificial intelligence (AI), has the potential to introduce substantial benefits to agriculture by enhancing farming practices that can improve agricultural production efficiency, yield, quality and safety. However, the adoption of edge AI faces several challenges, including the need for innovative and efficient edge AI solutions and greater investment in infrastructure and training, all compounded by various environmental, social and economic constraints. Here we provide a roadmap for leveraging edge AI at the intersection of food production and sustainability. 2024-06-10 2024-12-03T11:39:06Z 2024-12-03T11:39:06Z Journal Article https://hdl.handle.net/10568/162970 en Limited Access El Jarroudi, M., Kouadio, L., Delfosse, P., Bock, C. H., Mahlein, A. K., Fettweis, X., Mercatoris, B., Adams, F., Lenné, J. M. and Hamdioui, S. 2024. Leveraging edge artificial intelligence for sustainable agriculture. Nature Sustainability 7(7):846-854.
spellingShingle Artificial intelligence
sustainable agriculture
El Jarroudi, M.
Kouadio, L.
Delfosse, P.
Bock, C. H.
Mahlein, A. K.
Fettweis, X.
Mercatoris, B.
Adams, F.
Lenné, J. M.
Hamdioui, S.
Leveraging edge artificial intelligence for sustainable agriculture
title Leveraging edge artificial intelligence for sustainable agriculture
title_full Leveraging edge artificial intelligence for sustainable agriculture
title_fullStr Leveraging edge artificial intelligence for sustainable agriculture
title_full_unstemmed Leveraging edge artificial intelligence for sustainable agriculture
title_short Leveraging edge artificial intelligence for sustainable agriculture
title_sort leveraging edge artificial intelligence for sustainable agriculture
topic Artificial intelligence
sustainable agriculture
url https://hdl.handle.net/10568/162970
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