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Forecasting the 2022–2023 Indoor Greenhouse Temperature Using artificial Intelligence

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dc.contributor.author Soufi, Mohammed
dc.contributor.author Zahi, Abdelhadi
dc.date.accessioned 2024-12-09T09:20:03Z
dc.date.available 2024-12-09T09:20:03Z
dc.date.issued 2024
dc.identifier.uri https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/8927
dc.description.abstract This research uses various named agricultural methods to modify the environment for plant growth. Ideally, crops will be produced in areas that do not require particularly favorable climatic and environmental conditions, but the temperature and relative humidity conditions are adjusted to be optimal. This research work aims to design and operate two tunnel greenhouses, which have been prepared to be capable of studying their thermal behavior with and without cooling systems. The first one, without cooling systems, will serve as a control greenhouse, while the second will be modified to test the effect of cooling systems and any reported modifications. EN_en
dc.publisher université Ghardaia EN_en
dc.subject tunnel greenhouse; natural cooling; natural and forced ventilation; solar energy. EN_en
dc.subject serre tunnel ; refroidissement naturel ; ventilation naturelle et forcée ; énergie solaire. EN_en
dc.title Forecasting the 2022–2023 Indoor Greenhouse Temperature Using artificial Intelligence EN_en
dc.type Thesis EN_en


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