{"id":606,"date":"2019-06-18T15:42:00","date_gmt":"2019-06-18T15:42:00","guid":{"rendered":"http:\/\/localhost:8888\/humain\/?p=606"},"modified":"2024-12-30T06:54:18","modified_gmt":"2024-12-30T06:54:18","slug":"personalized-optimal-grape-harvest-by-autonomous-robot","status":"publish","type":"post","link":"https:\/\/humainlab.cs.duth.gr\/?p=606&lang=en","title":{"rendered":"Personalized Optimal Grape Harvest by Autonomous Robot"},"content":{"rendered":"\n<p><strong>Project title:<\/strong>&nbsp;<a href=\"http:\/\/evtar.eu\/\" target=\"_blank\" rel=\"noreferrer noopener\">Personalized Optimal Grape Harvest by Autonomous Robot<\/a><br><strong>Budget:<\/strong>&nbsp;\u20ac997,292.70 (Public Expense: \u20ac931,167.70)<br><strong>Funded from:<\/strong>Action \u201cRESEARCH \u2013 DEVELOP \u2013 INNOVATE\u201d, cycle A, Intervention II, Operational Programme \u201cCompetitiveness, Entrepreneurship and Innovation\u201d, NSRF (National Strategic Reference Framework) 2014-2020 Project no. \u03a41\u0395\u0394\u039a-00300<br><strong>Coordinator &amp; Principal Investigator:<\/strong>&nbsp;Professor Vassilis Kaburlasos<br><strong>Start \u2013 end dates:<\/strong>&nbsp;28 June 2018 \u2013 27 June 2022<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a href=\"http:\/\/humain-lab.cs.ihu.gr\/wp-content\/uploads\/2012\/04\/e-bannersEUERDF730X90.jpg\"><img decoding=\"async\" src=\"http:\/\/humain-lab.cs.ihu.gr\/wp-content\/uploads\/2012\/04\/e-bannersEUERDF730X90.jpg\" alt=\"\" class=\"wp-image-5493\"\/><\/a><\/figure>\n<\/div>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>The current practices of grape harvest include extensive human involvement such as monitoring as well as specialized manual works. However, the human involvement in grape harvest seems to have reached its limits. For instance, the workers often are not willing to work during the night when harvest conditions are optimal, young people often abandon farming and especially viticulture because of inherent work difficulties, the increasing age of human grape harvesters prolongs harvest duration, therefore it reduces the quality of the harvested grapes and ultimately it reduces the quality of the produced wines. For the aforementioned reasons there is a need to reliably mechanize grape harvest. This project proposes the development of an \u201cintelligent\u201d wheeled Autonomous Robot for Grape harvest (ARG, for short) toward mechanizing grape harvest in a way that, instead of massively harvesting rows of vines, to harvest selected grapes from the entire vineyard as follows. At first, we will automate the skillful work of Defoliation which selectively removes leaves in order to modify the grape microclimate. Then, we will automate the skillful work of Green Harvest which selectively removes immature grapes in order to facilitate ripening of the remaining grapes. The objective of both Defoliation and Green Harvest is not only the optimization of grape quality \/quantity but also the preparation of grape harvest. Finally, we will automate the skillful work of Homogeneous Harvest of grapes of optimal maturity by ARG by harvesting exclusively grapes of similar degree of ripeness from the entire vineyard in order to maximize the production of wines of consistent high quality. From a technical point of view the ARG will be developed by the integration of a wheeled robot, at least one robot arm, sufficient end-effectors such as a cutter, sufficient electronic sensors such as cameras, sufficient instruments for \u201con the spot\u201d chemical analyses, and also software that will coordinate the operation of the mechanical as well as the electronic components of ARG. In addition, an aerial drone will contribute to the development of digital maps required for ARG\u2019s autonomous navigation. The practice of grape harvest by ARG differs from current practices in that the ARG will carry out, with the dexterity of a human harvester, personalized harvest \u201cgrape by grape\u201d from the entire vineyard instead of carrying out grape harvest \u201crow by row\u201d of vines as it is currently pursued by skillful human harvesters as well as by non-skillful harvest machines, the latter significantly deteriorate the quality of the grapes they harvest. It seems that ARG\u2019s technical features outmatch those of alternative viticulture robots currently in use. The ARG aims directly at improving the competitiveness of Greek viticulture products in two different manners: (a) the increase in quantity, and (b) the consistent high quality of the produced grapes and wines, with a simultaneous decrease of production cost. In the aforementioned context, the existing human personnel could be engaged ever more in new roles, for example as supervisors (e.g. for controlling the effectiveness of ARG) as well as managers for the enterprise (e.g. toward promoting the product in the markets). In addition, new jobs will be generated regarding the development as well as the continuous improvement of the ARG since this project will promote its results toward investment aiming at an industrial development of the ARG. The \u201cknow how\u201d to be developed in the project can be extended in alternative viticulture works, e.g. spraying, as well as beyond viticulture, e.g. in olive culture, and beyond.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"720\" style=\"aspect-ratio: 1280 \/ 720;\" width=\"1280\" controls src=\"http:\/\/localhost:8888\/humain\/wp-content\/uploads\/2019\/06\/\u0395\u0392\u03a4\u0391\u03a1-video.mp4\"><\/video><\/figure>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Project title:&nbsp;Personalized Optimal Grape Harvest by Autonomous RobotBudget:&nbsp;\u20ac997,292.70 (Public Expense: \u20ac931,167.70)Funded from:Action \u201cRESEARCH \u2013 DEVELOP \u2013 INNOVATE\u201d, cycle A, Intervention II, Operational Programme \u201cCompetitiveness, Entrepreneurship and Innovation\u201d, NSRF (National Strategic Reference Framework) 2014-2020 Project no. \u03a41\u0395\u0394\u039a-00300Coordinator &amp; Principal Investigator:&nbsp;Professor Vassilis KaburlasosStart \u2013 end dates:&nbsp;28 June 2018 \u2013 27 June 2022 The current practices of grape harvest include extensive human involvement such as monitoring as well as specialized manual works. However, the human involvement in grape harvest seems to have reached its limits. For instance, the workers often are not willing to work during the night when harvest conditions are optimal, young people often abandon farming and especially viticulture because of inherent work difficulties, the increasing age of human grape harvesters prolongs harvest duration, therefore it reduces the quality of the harvested grapes and ultimately it reduces the quality of the produced wines. For the aforementioned reasons there is a need to reliably mechanize grape harvest. This project proposes the development of an \u201cintelligent\u201d wheeled Autonomous Robot for Grape harvest (ARG, for short) toward mechanizing grape harvest in a way that, instead of massively harvesting rows of vines, to harvest selected grapes from the entire vineyard as follows. At first, we will automate the skillful work of Defoliation which selectively removes leaves in order to modify the grape microclimate. Then, we will automate the skillful work of Green Harvest which selectively removes immature grapes in order to facilitate ripening of the remaining grapes. The objective of both Defoliation and Green Harvest is not only the optimization of grape quality \/quantity but also the preparation of grape harvest. Finally, we will automate the skillful work of Homogeneous Harvest of grapes of optimal maturity by ARG by harvesting exclusively grapes of similar degree of ripeness from the entire vineyard in order to maximize the production of wines of consistent high quality. From a technical point of view the ARG will be developed by the integration of a wheeled robot, at least one robot arm, sufficient end-effectors such as a cutter, sufficient electronic sensors such as cameras, sufficient instruments for \u201con the spot\u201d chemical analyses, and also software that will coordinate the operation of the mechanical as well as the electronic components of ARG. In addition, an aerial drone will contribute to the development of digital maps required for ARG\u2019s autonomous navigation. The practice of grape harvest by ARG differs from current practices in that the ARG will carry out, with the dexterity of a human harvester, personalized harvest \u201cgrape by grape\u201d from the entire vineyard instead of carrying out grape harvest \u201crow by row\u201d of vines as it is currently pursued by skillful human harvesters as well as by non-skillful harvest machines, the latter significantly deteriorate the quality of the grapes they harvest. It seems that ARG\u2019s technical features outmatch those of alternative viticulture robots currently in use. The ARG aims directly at improving the competitiveness of Greek viticulture products in two different manners: (a) the increase in quantity, and (b) the consistent high quality of the produced grapes and wines, with a simultaneous decrease of production cost. In the aforementioned context, the existing human personnel could be engaged ever more in new roles, for example as supervisors (e.g. for controlling the effectiveness of ARG) as well as managers for the enterprise (e.g. toward promoting the product in the markets). In addition, new jobs will be generated regarding the development as well as the continuous improvement of the ARG since this project will promote its results toward investment aiming at an industrial development of the ARG. The \u201cknow how\u201d to be developed in the project can be extended in alternative viticulture works, e.g. spraying, as well as beyond viticulture, e.g. in olive culture, and beyond.<\/p>\n","protected":false},"author":1,"featured_media":859,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[69],"tags":[],"class_list":["post-606","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-projects"],"_links":{"self":[{"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/posts\/606","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=606"}],"version-history":[{"count":3,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/posts\/606\/revisions"}],"predecessor-version":[{"id":2488,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/posts\/606\/revisions\/2488"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=\/wp\/v2\/media\/859"}],"wp:attachment":[{"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=606"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=606"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/humainlab.cs.duth.gr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}