{"id":2711,"date":"2021-10-23T09:57:40","date_gmt":"2021-10-23T09:57:40","guid":{"rendered":"https:\/\/www.encore-lab.com\/project\/project-afarcloud\/"},"modified":"2022-05-26T09:17:59","modified_gmt":"2022-05-26T09:17:59","slug":"project-afarcloud","status":"publish","type":"project","link":"https:\/\/www.encore-lab.com\/en\/project\/project-afarcloud\/","title":{"rendered":"Afarcloud"},"content":{"rendered":"

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Project Afarcloud<\/span><\/h1>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” background_image=”https:\/\/encore-lab.com\/wp-content\/uploads\/2021\/10\/afarcloud-banner.jpg” custom_margin=”||||false|false” custom_padding=”0px||1px||false|false” global_colors_info=”{}”][et_pb_row column_structure=”2_3,1_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”2_3″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ _module_preset=”default” background_color=”#f2f2f2″ custom_padding=”||25px||false|false” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” custom_margin=”||||false|false” custom_padding=”25px|||15px|false|false” global_colors_info=”{}”]<\/p>\n

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=”4.17.2″ _module_preset=”default” width=”90%” module_alignment=”center” custom_margin=”-15px||15px||false|false” custom_padding=”||||false|false” global_colors_info=”{}”]<\/p>\n

2017<\/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

+60 participants<\/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

SS system, humidity sensors and calculation algorithms.<\/p>\n

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Project description<\/h2>\n

AFarCloud will enable the integration and cooperation of cyber-physical agricultural systems in real time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs.<\/p>\n

The AFarCloud project also aims to make agricultural robots accessible to a wider range of users, allowing agricultural vehicles to work in a cooperative mesh, thus opening up new applications and ensuring reusability, as heterogeneous standard vehicles can combine their capabilities to increase farmers’ income and reduce labour costs.<\/p>\n

AFarCloud’s achievements will be demonstrated in 3 holistic demonstrators (Finland, Spain and Italy), including crop and livestock management scenarios and 8 local demonstrators (Latvia, Sweden, Spain and Czech Republic) in order to test specific functionalities and validate project results in relevant environments located in different European regions.<\/p>\n

 <\/p>\n

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

Encore-Lab participates as task leader in WP3 and has contributed with humidity sensors and irrigation needs calculation algorithms in the AS04 demonstrator, as well as the implementation of a decision support system or DSS with connectivity to the afarcloud MMT.<\/p>\n

 <\/p>\n

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Results<\/h2>\n

The results of AFarCloud will strengthen the partners’ position in the market, boosting their innovation capacity and addressing industrial needs both at EU and international level.<\/p>\n

The consortium represents the entire value chain of ICT-based agricultural solutions, including all key actors necessary for the development, demonstration and future market uptake of the project’s target precision farming framework.<\/p>\n

 <\/p>\n

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Project AfarcloudFile Year 2017Area AgricultureParticipants +60 participantsTechnology used SS system, humidity sensors and calculation algorithms.Project description AFarCloud will enable the integration and cooperation of cyber-physical agricultural systems in real time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. 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