{"id":48798,"date":"2021-12-08T09:47:27","date_gmt":"2021-12-08T07:47:27","guid":{"rendered":"https:\/\/www.iemn.fr\/?p=48798"},"modified":"2022-01-06T11:10:14","modified_gmt":"2022-01-06T09:10:14","slug":"vers-une-insemination-artificielle-bovine-de-precision-grace-a-lintelligence-artificielle","status":"publish","type":"post","link":"https:\/\/www.iemn.fr\/en\/newsletter\/vers-une-insemination-artificielle-bovine-de-precision-grace-a-lintelligence-artificielle.html","title":{"rendered":"Vers une ins\u00e9mination artificielle bovine de pr\u00e9cision gr\u00e2ce \u00e0 l\u2019intelligence artificielle"},"content":{"rendered":"<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kwx8kxa8-23df9d85e6dc2b5c0c5ee2113e271713\">\n#top .av_textblock_section.av-kwx8kxa8-23df9d85e6dc2b5c0c5ee2113e271713 .avia_textblock{\ncolor:#387cb5;\n}\n<\/style>\n<section  class='av_textblock_section av-kwx8kxa8-23df9d85e6dc2b5c0c5ee2113e271713'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock av_inherit_color'  itemprop=\"text\" ><h2 style=\"text-align: center;\">Vers une ins\u00e9mination artificielle bovine de pr\u00e9cision gr\u00e2ce \u00e0 l\u2019intelligence artificielle<\/h2>\n<hr \/>\n<h3><\/h3>\n<\/div><\/section>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1ctii9q-7f2b6a9e38103998b7b5bb84251474c9\">\n.flex_column.av-1ctii9q-7f2b6a9e38103998b7b5bb84251474c9{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-1ctii9q-7f2b6a9e38103998b7b5bb84251474c9 av_one_half  avia-builder-el-1  el_after_av_textblock  el_before_av_one_half  first flex_column_div av-zero-column-padding  column-top-margin'     ><section  class='av_textblock_section av-kwx8ccqb-25ebf63822eaa6de66dfdcc3319d8471'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><blockquote>\n<p><strong><a href=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/insemination.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-48846 alignleft\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/insemination.jpg\" alt=\"\" width=\"200\" height=\"122\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/insemination.jpg 200w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/insemination-18x12.jpg 18w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/><\/a>Dans le secteur de l\u2019\u00e9levage bovin, d\u00e9tecter la p\u00e9riode des chaleurs chez une vache est une condition indispensable pour r\u00e9ussir son ins\u00e9mination artificielle et concr\u00e9tiser ainsi une conception. Jusqu\u2019\u00e0 pr\u00e9sent, la d\u00e9tection s\u2019effectuait au moyen d\u2019observations visuelles de signes comportementaux par un op\u00e9rateur humain pouvant \u00eatre assist\u00e9 par des syst\u00e8mes d\u2019analyse automatis\u00e9s. Une \u00e9tude originale combinant intelligence artificielle et \u00e9quipement innovant d\u2019ins\u00e9mination a montr\u00e9 qu\u2019il est possible de d\u00e9tecter rapidement et plus efficacement les chaleurs par analyse endoscopique de l\u2019appareil g\u00e9nital de la vache.<\/strong><\/p>\n<\/blockquote>\n<p>L\u2019ins\u00e9mination artificielle bovine est une biotechnologie qui contribue pleinement au d\u00e9veloppement d\u2019une agriculture durable gr\u00e2ce aux avantages qu\u2019elle offre sur le plan de la s\u00e9curit\u00e9 sanitaire, du gain g\u00e9n\u00e9tique ainsi que des co\u00fbts \u00e9conomiques. La condition principale pour la r\u00e9ussite d\u2019une ins\u00e9mination d\u2019une vache (concr\u00e9tisation de la conception) est la d\u00e9tection de sa p\u00e9riode des chaleurs.<\/p>\n<\/div><\/section><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ukzti6-71d2fedcb1e6e99768925744c853c3e7\">\n.flex_column.av-ukzti6-71d2fedcb1e6e99768925744c853c3e7{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ukzti6-71d2fedcb1e6e99768925744c853c3e7 av_one_half  avia-builder-el-3  el_after_av_one_half  el_before_av_one_full  flex_column_div av-zero-column-padding  column-top-margin'     ><section  class='av_textblock_section av-kwx8ch5j-8e3ee48bba6b09be8cc7535e1b648162'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>G\u00e9n\u00e9ralement, cette p\u00e9riode est identifi\u00e9e par le fermier gr\u00e2ce \u00e0 l\u2019observation visuelle d\u2019un ensemble de signes comportementaux manifest\u00e9s par la vache. Cependant, l\u2019exploitation de cette m\u00e9thode de d\u00e9tection dans des troupeaux de grande taille est un processus fastidieux. De plus, les signes manifest\u00e9s par la vache peuvent \u00eatre impact\u00e9s par diff\u00e9rents facteurs li\u00e9s \u00e0 sa sant\u00e9 et \u00e0 son environnement ce qui r\u00e9duit l\u2019efficacit\u00e9 de la d\u00e9tection.<\/p>\n<p><strong>Pour faire face \u00e0 ces limites, un syst\u00e8me original de d\u00e9tection des chaleurs par analyse endoscopique a \u00e9t\u00e9 d\u00e9velopp\u00e9 en exploitant une technologie d\u2019ins\u00e9mination innovante nomm\u00e9e Eye Breed et un mod\u00e8le d\u2019intelligence artificielle con\u00e7u et optimis\u00e9 pour un d\u00e9ploiement sur smartphone.<\/strong><br \/>\nComme illustr\u00e9 dans la Figure 1, le dispositif Eye Breed, \u00e9quip\u00e9 d\u2019une cam\u00e9ra embarqu\u00e9e et connect\u00e9 \u00e0 un smartphone, est introduit dans l\u2019appareil g\u00e9nital de la vache par un op\u00e9rateur humain qui supervise en temps r\u00e9el une simulation d\u2019une op\u00e9ration d\u2019ins\u00e9mination. La vid\u00e9o enregistr\u00e9e est ensuite automatiquement analys\u00e9e par le mod\u00e8le d\u2019intelligence artificielle embarqu\u00e9 sur le smartphone pour statuer en moins de 20 secondes sur l\u2019\u00e9tat des chaleurs de la vache. \u00a0Le mod\u00e8le d\u2019intelligence artificielle d\u00e9velopp\u00e9 emploie une technique d\u2019apprentissage machine profond (Deep Learning) bas\u00e9e sur les r\u00e9seaux de neurones convolutionnels (Convolutional Neural Networks) dont la structure est inspir\u00e9e par les structures de champ r\u00e9cepteur qui se trouvent dans le cortex visuel primaire humain.<\/p>\n<\/div><\/section><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-kwx8noc3-a5a6185a2962058b48296458802eee7f\">\n@keyframes av_boxShadowEffect_av-kwx8noc3-a5a6185a2962058b48296458802eee7f-column {\n0%   { box-shadow:  0 0 0 0 #a3a3a3; opacity: 1; }\n100% { box-shadow:  0 0 10px 0 #a3a3a3; opacity: 1; }\n}\n.flex_column.av-kwx8noc3-a5a6185a2962058b48296458802eee7f{\nbox-shadow: 0 0 10px 0 #a3a3a3;\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-kwx8noc3-a5a6185a2962058b48296458802eee7f av_one_full  avia-builder-el-5  el_after_av_one_half  el_before_av_one_full  first flex_column_div shadow-not-animated av-zero-column-padding  column-top-margin'     ><section  class='av_textblock_section av-kwx8ndx0-5ccb7751c46bfafd9926a187a1eab00f'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p><a href=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-48799 size-full\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow.jpg\" alt=\"\" width=\"800\" height=\"471\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow.jpg 800w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow-300x177.jpg 300w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow-768x452.jpg 768w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow-18x12.jpg 18w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2021\/12\/cow-705x415.jpg 705w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><em>Figure 1 :\u00a0Vue d\u2019ensemble de notre syst\u00e8me de d\u00e9tection des chaleurs chez la vache [1]<\/em><\/p>\n<\/div><\/section><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-ujonz2-4cce9d79387fd552f58a12c8ccb11975\">\n.flex_column.av-ujonz2-4cce9d79387fd552f58a12c8ccb11975{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-ujonz2-4cce9d79387fd552f58a12c8ccb11975 av_one_full  avia-builder-el-7  el_after_av_one_full  avia-builder-el-last  first flex_column_div av-zero-column-padding  column-top-margin'     ><section  class='av_textblock_section av-kwx8a69t-0e6b1c798632470e6b384943a0540f97'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p><strong>Le syst\u00e8me d\u00e9velopp\u00e9 a montr\u00e9 une haute performance sur les 32 vaches test\u00e9es \u00e0 ce jour avec un taux de bonne d\u00e9tection de 87.5%. A moyen terme, le syst\u00e8me sera d\u00e9ploy\u00e9 et exp\u00e9riment\u00e9 dans plusieurs fermes de la r\u00e9gion nord de la France. Aussi, de nouvelles pistes de recherche seront \u00e9tudi\u00e9es en lien avec le d\u00e9veloppement de syst\u00e8mes intelligents d\u2019aide au diagnostic des pathologies des vaches en exploitant le dispositif Eye Breed.<\/strong><\/p>\n<p>Ce travail collaboratif entre plusieurs \u00e9quipes de recherche de l\u2019IEMN (groupes BioMEMS et COMNUM), de JUNIA, de G\u00e8nes Diffusion, d\u2019ELEXINN et du LIMMS, s\u2019inscrit dans le cadre d\u2019un projet de recherche FEDER r\u00e9gion Hauts-de-France. L\u2019approche d\u00e9velopp\u00e9e ainsi que les r\u00e9sultats obtenus ont \u00e9t\u00e9 publi\u00e9s dans la r\u00e9f\u00e9rence [1].<\/p>\n<p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-13ewzjw-9a9f1cfb2a3570afb0f44a0c4adce97a\">\n.av_font_icon.av-13ewzjw-9a9f1cfb2a3570afb0f44a0c4adce97a{\ncolor:#800000;\nborder-color:#800000;\n}\n.av_font_icon.av-13ewzjw-9a9f1cfb2a3570afb0f44a0c4adce97a .av-icon-char{\nfont-size:30px;\nline-height:30px;\n}\n<\/style>\n<span  class='av_font_icon av-13ewzjw-9a9f1cfb2a3570afb0f44a0c4adce97a avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue871' data-av_iconfont='entypo-fontello' ><\/span><\/span>[1]\u00a0He, R., Benhabiles, H., Windal, F.\u00a0et al.\u00a0A CNN-based methodology for cow heat analysis from endoscopic images.\u00a0Applied Intelligence \u2013 The International Journal of Research on Intelligent Systems for Real Life Complex Problems \u2013\u00a0SPRINGER \u2013 published online 27 October 2021. <a href=\"https:\/\/link.springer.com\/article\/10.1007%2Fs10489-021-02910-5\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/s10489-021-02910-5<\/a><\/p>\n<p><div  class='avia-button-wrap av-rpqvoq-07dd8316b81a98f3c35b1b8a1587a243-wrap avia-button-left  avia-builder-el-10  el_after_av_font_icon  el_before_av_button'><a href='mailto:halim.benhabiles@iemn.fr'  class='avia-button av-rpqvoq-07dd8316b81a98f3c35b1b8a1587a243 av-link-btn avia-icon_select-yes-left-icon avia-size-small avia-position-left avia-color-silver'   aria-label=\"halim.benhabiles@iemn.fr\"><span class='avia_button_icon avia_button_icon_left' aria-hidden='true' data-av_icon='\ue805' data-av_iconfont='entypo-fontello'><\/span><span class='avia_iconbox_title' >halim.benhabiles@iemn.fr<\/span><\/a><\/div><br \/>\n<div  class='avia-button-wrap av-rpqvoq-41838aa1c2413d0a3d1525df96ecd9be-wrap avia-button-left  avia-builder-el-11  el_after_av_button  avia-builder-el-last'><a href='mailto:feryal.windal@iemn.fr'  class='avia-button av-rpqvoq-41838aa1c2413d0a3d1525df96ecd9be av-link-btn avia-icon_select-yes-left-icon avia-size-small avia-position-left avia-color-silver'   aria-label=\"feryal.windal@iemn.fr\"><span class='avia_button_icon avia_button_icon_left' aria-hidden='true' data-av_icon='\ue805' data-av_iconfont='entypo-fontello'><\/span><span class='avia_iconbox_title' >feryal.windal@iemn.fr<\/span><\/a><\/div><\/p>\n<\/div><\/section><\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[297],"tags":[],"class_list":["post-48798","post","type-post","status-publish","format-standard","hentry","category-newsletter"],"_links":{"self":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/48798","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/comments?post=48798"}],"version-history":[{"count":0,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/48798\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media?parent=48798"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/categories?post=48798"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/tags?post=48798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}