{"id":2717,"date":"2022-04-04T08:44:46","date_gmt":"2022-04-04T08:44:46","guid":{"rendered":"https:\/\/www.encore-lab.com\/project\/weather-control\/"},"modified":"2022-05-26T09:17:14","modified_gmt":"2022-05-26T09:17:14","slug":"weather-control","status":"publish","type":"project","link":"https:\/\/www.encore-lab.com\/en\/project\/weather-control\/","title":{"rendered":"Weather Control"},"content":{"rendered":"

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Project Weather Control<\/span><\/h1>\n

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File<\/h1>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” max_width=”90%” module_alignment=”center” custom_margin=”||0px||false|false” global_colors_info=”{}”]Year<\/strong>
\n[\/et_pb_text][et_pb_divider color=”#c4c4c4″ divider_weight=”1px” _builder_version=”4.16″ _module_preset=”default” background_color=”RGBA(0,0,0,0)” width=”90%” module_alignment=”center” custom_margin=”||-20px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.16.0″ _module_preset=”default” width=”90%” module_alignment=”center” custom_margin=”-15px||15px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”]<\/p>\n

2021<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” max_width=”90%” module_alignment=”center” custom_margin=”||0px||false|false” global_colors_info=”{}”]Area<\/strong>
\n[\/et_pb_text][et_pb_divider color=”#c4c4c4″ divider_weight=”1px” _builder_version=”4.16″ _module_preset=”default” background_color=”RGBA(0,0,0,0)” width=”90%” module_alignment=”center” custom_margin=”||-20px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.16.0″ _module_preset=”default” width=”90%” module_alignment=”center” custom_margin=”-15px||15px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”]<\/p>\n

Agriculture<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” max_width=”90%” module_alignment=”center” custom_margin=”||0px||false|false” global_colors_info=”{}”]Participants<\/strong>
\n[\/et_pb_text][et_pb_divider color=”#c4c4c4″ divider_weight=”1px” _builder_version=”4.16″ _module_preset=”default” background_color=”RGBA(0,0,0,0)” width=”90%” module_alignment=”center” custom_margin=”||-20px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.16.0″ _module_preset=”default” width=”90%” module_alignment=”center” custom_margin=”-15px||15px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”]<\/p>\n

Aertic, Encore-Lab, Bodegas Vintae.<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” max_width=”90%” module_alignment=”center” custom_margin=”||0px||false|false” global_colors_info=”{}”]Technology used<\/strong>
\n[\/et_pb_text][et_pb_divider color=”#c4c4c4″ divider_weight=”1px” _builder_version=”4.16″ _module_preset=”default” background_color=”RGBA(0,0,0,0)” width=”90%” module_alignment=”center” custom_margin=”||-20px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”][\/et_pb_divider][et_pb_text _builder_version=”4.16.0″ _module_preset=”default” width=”90%” module_alignment=”center” custom_margin=”-15px||15px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”]<\/p>\n

Neural networks, natural language processing (NLP), field sensors, DSS, computational algorithms.<\/p>\n

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Project description<\/h2>\n

“Weather Control predictive tool for climatic parameters at plot level”, financed under the 2021 Call for Proposals of the Grants Established to Support Innovative Business Groupings with a view to Improving the Competitiveness of Small and Medium-sized Enterprises.<\/p>\n

 <\/p>\n

 <\/p>\n

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Reply from Encore-Lab<\/h2>\n

Development of the mathematical prediction model based on machine learning techniques.<\/p>\n

 <\/p>\n

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Results<\/h2>\n

As a result of the developments carried out in the project, a mathematical model based on automatic learning techniques has been obtained that substantially improves the predictions of the regional models offered by the state meteorological agencies at regional level, so that this information can help to make better decisions regarding irrigation, treatments and the organisation of agricultural work.<\/p>\n

The use of various data sources to predict climate variables in a short time frame has been key. to improve the accuracy of water balance models and disease risk models…<\/p>\n

On the other hand, the project has strengthened innovation and technology transfer between two key sectors of the Riojan ecosystem, such as ICT and the wine sector. <\/p>\n

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