{"id":21897,"date":"2024-12-12T15:57:10","date_gmt":"2024-12-12T14:57:10","guid":{"rendered":"https:\/\/www.odenserobotics.dk\/pioneering-ai-architectures-for-detection-of-gas-phase-ammonia\/"},"modified":"2025-03-29T15:11:03","modified_gmt":"2025-03-29T14:11:03","slug":"pioneering-ai-architectures-for-detection-of-gas-phase-ammonia","status":"publish","type":"post","link":"https:\/\/www.odenserobotics.dk\/da\/pioneering-ai-architectures-for-detection-of-gas-phase-ammonia\/","title":{"rendered":"Pioneering AI architectures for detection of gas-phase ammonia using hyperspectral thermographic cameras"},"content":{"rendered":"<div id=\"pl-19632\"  class=\"panel-layout\" >\n<div id=\"pg-19632-0\"  class=\"panel-grid panel-no-style\" >\n<div id=\"pgc-19632-0-0\"  class=\"panel-grid-cell\"  data-weight=\"1\" >\n<div id=\"panel-19632-0-0-0\" class=\"so-panel widget widget_sow-editor panel-first-child\" data-index=\"0\" >\n<div\n\t\t\t\n\t\t\tclass=\"so-widget-sow-editor so-widget-sow-editor-base\"\n\t\t\t\n\t\t><\/p>\n<div class=\"siteorigin-widget-tinymce textwidget\">\n<h3>From simulated environments towards field-use<\/h3>\n<p><em><strong>English summary<\/strong>: <\/em><em>How do you develop neural networks for spectral reconstruction of data from thermographic hyperspectral cameras and apply them to detect ammonia? Read the results of this project &#8211; a collaboration between Newtec Engineering, AgroAlliancen, and the University of Southern Denmark, with contributions from Aarhus University and Foulum.<\/em><\/p>\n<h3>Read about the results of this project<\/h3>\n<p>I projektet er m\u00e5let at udvikle neurale netv\u00e6rk til spektral rekonstruktion af data fra termografisk hyperspektrale kameraer, og anvende dem til at detektere ammoniak. Projektet er et samarbejde mellem Newtec Engineering, AgroAlliancen og Syddansk Universitet hvor ogs\u00e5 Aarhus Universitet og Foulum har bidraget.\u00a0 Projektet skal blandt andet drive udviklingen af nye metoder til detektion af ammoniak emissioner fra landbrug.<\/p>\n<p>Vi har i l\u00f8bet af projektet udviklet og afpr\u00f8vet forskellige typer af neurale netv\u00e6rks baserede algoritmer til rekonstruktion af hyperspektrale billeder \u2013 b\u00e5de standard foldningsnetv\u00e6rk, autoencoder netv\u00e6rk og hybride fysik informerede netv\u00e6rk. Disse netv\u00e6rk er i projektet f\u00f8rst blevet testet til rekonstruktion af snapshot hyperspektrale billeder i det n\u00e6r-synlige omr\u00e5de optaget med et Computed Tomography Imaging Spectrometer udviklet af QTechnology og Newtec Engineering og rapporteret i preprint [1].<\/p>\n<p>Vi har optaget de f\u00f8rste billeder af ammoniak p\u00e5 gasform med et prototype termografisk hyperspektral kamera fra Newtec (Fig. 1, panel 1) og vist at vi kan detektere gassen med stor signifikans i laboratoriet (Fig.1, panel 2) hvorefter vi har v\u00e6ret i marken for at m\u00e5le p\u00e5 Metan og Ammoniak afgasning (Fig. 1, panel 3). Vi er nu ved at analysere disse resultater.<\/p>\n<p><b>Fig. 1: M\u00e5linger af ammoniak i laboratoriet p\u00e5 SDU (venstre og midt) og i marken (h\u00f8jre).<\/b><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-19640\" src=\"https:\/\/www.odenserobotics.dk\/wp-content\/uploads\/2024\/12\/Skaermbillede-2024-12-12-kl.-16.07.38-300x76.png\" alt=\"test\" width=\"554\" height=\"140\" \/><\/p>\n<p>Ved at udvikle nye metoder til detektion og kvantificering af ammoniak emissioner vil projektet hj\u00e6lpe med at belyse emissionskilder- og m\u00f8nstre, hvilket vil tillade mere m\u00e5lrettede indgreb til at nedbringe ammonia emissionerne blandt andet fra landbrug.<\/p>\n<p>[1] Investigating the Applicability of a Snapshot Computed Tomography Imaging Spectrometer for the Prediction of Brix and pH of Grapes.<\/p>\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2411.03114\" target=\"_blank\" rel=\"noopener\">Read the research paper here<\/a> written by:<\/p>\n<ul>\n<li>Mads Svanborg Peters<\/li>\n<li>Mads Juul Ahleb\u00e6k<\/li>\n<li>Mads Toudal Frandsen<\/li>\n<li>Bjarke J\u00f8rgensen<\/li>\n<li>Christian Hald Jessen<\/li>\n<li>Andreas Krogh Carlsen<\/li>\n<li>Wei-Chih Huang<\/li>\n<li>Ren\u00e9 Lynge Eriksen<\/li>\n<\/ul>\n<h3>The participants were:<\/h3>\n<ul>\n<li>Newtec Engineering<\/li>\n<li>AgroAlliancen<\/li>\n<\/ul>\n<p>For further information please contact: Mads Toudal Frandsen, SDU Galaxy, Professor CP3-Origins at University of Southern Denmark on <a href=\"mailto:frandsen@cp3.sdu.dk\">frandsen@cp3.sdu.dk<\/a>.<\/p>\n<p><em>The project has been funded by the Danish Agency for Higher Education and Science through Innovationskraftbevillingen 2021-2024.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"panel-19632-0-0-1\" class=\"so-panel widget widget_mtt-contact-archive panel-last-child\" data-index=\"1\" data-style=\"{&quot;background_image_attachment&quot;:false,&quot;background_display&quot;:&quot;tile&quot;,&quot;background_image_size&quot;:&quot;full&quot;,&quot;background_image_opacity&quot;:&quot;100&quot;,&quot;border_thickness&quot;:&quot;1px&quot;}\" >[siteorigin_widget class=&#8221;MTTSiteoriginWidgets\\\\Widgets\\\\MTTContactArchive\\\\MTTContactArchive&#8221;]<input type=\"hidden\" value=\"{&quot;instance&quot;:{&quot;tagline&quot;:&quot;&quot;,&quot;title&quot;:&quot;For more information&quot;,&quot;text&quot;:&quot;Please contact:&quot;,&quot;employees&quot;:&quot;1201&quot;,&quot;_sow_form_id&quot;:&quot;1213541125676026b84a763546283505&quot;,&quot;_sow_form_timestamp&quot;:&quot;1734354640516&quot;,&quot;settings&quot;:&quot;&quot;,&quot;so_sidebar_emulator_id&quot;:&quot;mtt-contact-archive-1963210001&quot;,&quot;option_name&quot;:&quot;widget_mtt-contact-archive&quot;},&quot;args&quot;:{&quot;before_widget&quot;:&quot;&lt;div id=\\&quot;panel-19632-0-0-1\\&quot; class=\\&quot;so-panel widget widget_mtt-contact-archive panel-last-child\\&quot; data-index=\\&quot;1\\&quot; data-style=\\&quot;{&amp;quot;background_image_attachment&amp;quot;:false,&amp;quot;background_display&amp;quot;:&amp;quot;tile&amp;quot;,&amp;quot;background_image_size&amp;quot;:&amp;quot;full&amp;quot;,&amp;quot;background_image_opacity&amp;quot;:&amp;quot;100&amp;quot;,&amp;quot;border_thickness&amp;quot;:&amp;quot;1px&amp;quot;}\\&quot; &gt;&quot;,&quot;after_widget&quot;:&quot;&lt;\\\/div&gt;&quot;,&quot;before_title&quot;:&quot;&lt;h3 class=\\&quot;widget-title\\&quot;&gt;&quot;,&quot;after_title&quot;:&quot;&lt;\\\/h3&gt;&quot;,&quot;widget_id&quot;:&quot;widget-0-0-1&quot;}}\" \/>[\/siteorigin_widget]<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>How do you develop neural networks for spectral reconstruction of data from thermographic hyperspectral cameras and apply them to detect ammonia? Read the results of this project &#8211; a collaboration between Newtec Engineering, AgroAlliancen and SDU, with contributions from Aarhus University and Foulum.<\/p>\n","protected":false},"author":1,"featured_media":19634,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[769],"tags":[],"published_by":[],"class_list":["post-21897","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-da"],"acf":{"thumbnail_image":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Pioneering AI architectures for detection of gas-phase ammonia using hyperspectral thermographic cameras - Odense Robotics<\/title>\n<meta name=\"description\" content=\"How do you develop neural networks for spectral reconstruction of data from thermographic hyperspectral cameras and apply them to detect ammonia?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.odenserobotics.dk\/da\/pioneering-ai-architectures-for-detection-of-gas-phase-ammonia\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pioneering AI architectures for detection of gas-phase ammonia using hyperspectral thermographic cameras - 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