{"id":2516,"date":"2022-11-03T09:30:00","date_gmt":"2022-11-03T09:30:00","guid":{"rendered":"https:\/\/metricsproject.eu\/?page_id=2516"},"modified":"2023-06-07T23:07:42","modified_gmt":"2023-06-07T23:07:42","slug":"2023-cascade-campaign","status":"publish","type":"page","link":"https:\/\/metricsproject.eu\/agri-food\/2023-cascade-campaign\/","title":{"rendered":"2023 Cascade Campaign"},"content":{"rendered":"
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2023 ACRE Cascade Campaign<\/h2>\n

In the\u00a02023 ACRE 2nd Cascade Campaign<\/strong>, participants are asked to perform object detection to recognize crop plants and weeds on RGB images. Data was collected in real crop fields during the 1st Field Campaign in June 2022. The dataset comprises images captured by an agricultural robot of different field plots with an RGB camera. Images represent two kinds of crops (maize and bean) and multiple species of weeds. Participants are provided with labeled images to train their models, and they are asked to submit their hypothesis (detections in COCO format) of a test dataset.<\/p>\n

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Competition was taking place on Codalab: https:\/\/codalab.lisn.upsaclay.fr\/competitions\/11237<\/a> from March 7 to May 9, 2023<\/p>\n

Results:<\/h3>\n\n\n\n\n\n\n\n
Place<\/strong><\/td>\nTeam<\/strong><\/td>\nScore<\/strong><\/td>\n<\/tr>\n
1st winner<\/td>\nGT team<\/strong> composed by Min Gao, Mengjia Wang, Jingwen Zhang<\/td>\n0.4913<\/strong><\/td>\n<\/tr>\n
2nd winner<\/td>\nSmartfarmrobotix team<\/strong> composed by Rossen Dimov<\/td>\n0.4599<\/strong><\/td>\n<\/tr>\n
3rd winner<\/td>\nXLIM-ReMIX team<\/strong> composed by Franco Jesus, Tariq Zioud, Daniel Soto Guerrero, Jorge Aviles<\/td>\n0.4593<\/strong><\/td>\n<\/tr>\n
4th winner<\/td>\nEli Spizzichino<\/td>\n0.4472<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n<\/div>\n<\/div><\/div><\/div><\/div>

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More information:<\/h3>\n

acre(a)metricsproject.eu<\/a><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"

2023 ACRE Cascade Campaign In the\u00a02023 ACRE 2nd Cascade Campaign, participants are asked to perform object detection to recognize crop plants and weeds on RGB images. Data was collected in real crop fields during the 1st Field Campaign in June 2022. The dataset comprises images captured by an agricultural robot of different field plots with […]<\/p>\n","protected":false},"author":4,"featured_media":1662,"parent":98,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/agri-page.php","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/2516"}],"collection":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/comments?post=2516"}],"version-history":[{"count":19,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/2516\/revisions"}],"predecessor-version":[{"id":3144,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/2516\/revisions\/3144"}],"up":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/pages\/98"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/media\/1662"}],"wp:attachment":[{"href":"https:\/\/metricsproject.eu\/wp-json\/wp\/v2\/media?parent=2516"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}