{"id":57,"date":"2020-10-20T11:59:40","date_gmt":"2020-10-20T11:59:40","guid":{"rendered":"https:\/\/iot-ngin.eu\/?page_id=57"},"modified":"2026-09-08T10:16:56","modified_gmt":"2026-09-08T10:16:56","slug":"usecases","status":"publish","type":"page","link":"https:\/\/agridatavalue.eu\/index.php\/usecases\/","title":{"rendered":"Use Cases"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text el_class=”text”]The AgriDataValue technological tools, mechanisms, and LMAA will be fully tested and validated during the project’s lifetime through 37 Use Cases (UCs). These Use Cases have been grouped into clusters, addressing similar domains across different regions and varying cultural, societal, and farming contexts.\u00a0<\/span><\/p>\n

To ensure robust validation across diverse agricultural settings, the initiative is implemented through distinct use cases across 23 pilots in 9 countries, spanning over 181,000 hectares. These real-world demonstrators encompass a wide variety of crop types\u2014covering both outdoor and greenhouse environments, as well as organic and conventional production\u2014along with more than 2,000 animals across 5 types.<\/p>\n

Structured around strategic thematic clusters, the project’s pilot activities address critical agricultural sectors, including arable crops, vegetables, permanent crops and vineyards, livestock, cross-sector circular economy applications, CAP realization, and climate monitoring. This multidimensional approach optimizes resource efficiency\u2014such as precision irrigation and targeted nutrient management\u2014while directly supporting European Common Agricultural Policy (CAP) compliance and long-term agricultural sustainability.[\/vc_column_text][vc_single_image image=”3309″ img_size=”large” alignment=”center”][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3417″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #1 – Arable Crops” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Optimize the quality and quantity of the crop production and increase the environmental sustainability. Reduce the wasted irrigation water, fertilizers, pesticides, energy. Involve different technologies and data platforms such as IoT sensors, edge cloud, drones\/satellite visual\/multispectral images and AI models.[\/vc_column_text]Read More<\/a>[\/vc_column][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3272″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #2 – Vegetables” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Precision irrigation\/ fertilization, harvest \/diseases prediction for vegetables\/arable crop increased production. Involve IoT sensors, edge cloud, radiation\/clorofile\/pH metering, multiple data platforms with geotagged photos alone with drones\/satellite multispectral imagery.
\n[\/vc_column_text]Read More<\/a>[\/vc_column][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3276″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #3 – Trees & Vineyard” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Protect the health and quality of fruit trees and vineyards crop. Increase quality and quantity, avoid diseases with less pesticides, foresee and mitigate frost. Involve IoT weather\/soil sensors, edge cloud, diverse geotagged photos\u2019 datasets, drones\/satellite multispectral imagery.
\n[\/vc_column_text]Read More<\/a>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][vc_empty_space][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3278″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #4 – Livestock” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Use edge cloud and real-time IoT sensor data (e.g. neck collar, feeders, emission sensors) together with GPS location data to monitor the cattle\/pig health, activity, feeding and calving, proactively control milk and meat quality, reduce the greenhouse gas emissions and nitrogen deposition.[\/vc_column_text]Read More<\/a>[\/vc_column][vc_column width=”1\/3″][\/vc_column][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3270″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #5 – Cross Sector” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Validate cross domain use cases (fruit, vineyards, livestock, milk, oil, biogas, manure, energy) and address both supply and demand sides of the supply chain, including interoperability and traceability of platforms, electricity production and waste management.
\n[\/vc_column_text]Read More<\/a>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][vc_empty_space][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3440″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #6 – CAP Realization” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Focuses on CAP realization tools\/applications and aims to assess and manage risk through modern ML with the aim of reducing the use of pesticides, fertilisers and antibiotics. Promote modern crop monitoring technologies. Benchmarking eco-scheme monitoring tools to support the new CAP towards fair income, land use and environmental protection.[\/vc_column_text]Read More<\/a>[\/vc_column][vc_column width=”1\/3″][\/vc_column][vc_column width=”1\/3″ el_class=”el-center”][vc_single_image image=”3441″ img_size=”400×400″ alignment=”center”][vc_custom_heading text=”UC CLUSTER #7 – Climate Monitoring” font_container=”tag:h2|font_size:22|text_align:center|color:%23005327″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_column_text el_class=”text”]Climate monitoring plays a vital role in agriculture by providing valuable information about weather patterns, climate variability, and climate change impacts on crop growth, pests, diseases, and water availability. It involves collecting data from weather stations, satellites, remote sensing technologies, and climate models.[\/vc_column_text]Read More<\/a>[\/vc_column][\/vc_row][vc_row][vc_column width=”1\/3″][\/vc_column][vc_column width=”1\/3″][\/vc_column][vc_column width=”1\/3″][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][vc_column_text el_class=”text”]The AgriDataValue technological tools, mechanisms, and LMAA will be fully tested and validated during the project’s lifetime through 37 Use Cases (UCs). These Use Cases have been grouped into clusters, addressing similar domains across different regions and varying cultural, …<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"_mi_skip_tracking":false,"footnotes":""},"_links":{"self":[{"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/pages\/57"}],"collection":[{"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/comments?post=57"}],"version-history":[{"count":139,"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/pages\/57\/revisions"}],"predecessor-version":[{"id":5119,"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/pages\/57\/revisions\/5119"}],"wp:attachment":[{"href":"https:\/\/agridatavalue.eu\/index.php\/wp-json\/wp\/v2\/media?parent=57"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}