{"id":11416,"date":"2025-03-30T21:39:19","date_gmt":"2025-03-30T19:39:19","guid":{"rendered":"https:\/\/geopard.tech\/?p=11416"},"modified":"2025-03-30T21:45:02","modified_gmt":"2025-03-30T19:45:02","slug":"hur-uas-baserad-fenotypning-med-hog-genomstromning-forandrar-modern-vaxtforadling","status":"publish","type":"post","link":"https:\/\/geopard.tech\/swe\/blog\/how-uas-based-high-throughput-phenotyping-is-transforming-modern-plant-breeding\/","title":{"rendered":"Hur UAS-baserad fenotypning med h\u00f6g genomstr\u00f6mning f\u00f6r\u00e4ndrar modern v\u00e4xtf\u00f6r\u00e4dling"},"content":{"rendered":"<p>\u00c5r 2050 f\u00f6rv\u00e4ntas v\u00e4rldens befolkning n\u00e5 9,8 miljarder m\u00e4nniskor, vilket f\u00f6rdubblar efterfr\u00e5gan p\u00e5 mat. Att ut\u00f6ka jordbruksmarken f\u00f6r att m\u00f6ta detta behov \u00e4r dock oh\u00e5llbart. \u00d6ver 501 ton ny odlingsmark som skapats sedan \u00e5r 2000 har ersatt skogar och naturliga ekosystem, vilket f\u00f6rv\u00e4rrar klimatf\u00f6r\u00e4ndringarna och f\u00f6rlusten av biologisk m\u00e5ngfald.<\/p>\n<p>F\u00f6r att undvika denna kris v\u00e4nder sig forskare till v\u00e4xtf\u00f6r\u00e4dling \u2013 vetenskapen om att utveckla gr\u00f6dor med h\u00f6gre avkastning, sjukdomsresistens och klimatmotst\u00e5ndskraft. Traditionella f\u00f6r\u00e4dlingsmetoder \u00e4r dock f\u00f6r l\u00e5ngsamma f\u00f6r att h\u00e5lla j\u00e4mna steg med problemets br\u00e5dska.<\/p>\n<p>Det \u00e4r h\u00e4r dr\u00f6nare och artificiell intelligens (AI) kliver in som banbrytande och erbjuder ett snabbare och smartare s\u00e4tt att odla b\u00e4ttre gr\u00f6dor.<\/p>\n<h2>Varf\u00f6r traditionell v\u00e4xtf\u00f6r\u00e4dling halkar efter<\/h2>\n<p>V\u00e4xtf\u00f6r\u00e4dling bygger p\u00e5 att v\u00e4lja v\u00e4xter med \u00f6nskv\u00e4rda egenskaper, s\u00e5som torkt\u00e5lighet eller skadedjursresistens, och korsavel dem \u00f6ver flera generationer. Den st\u00f6rsta flaskhalsen i denna process \u00e4r fenotypning \u2013 den manuella m\u00e4tningen av v\u00e4xtegenskaper som h\u00f6jd, bladh\u00e4lsa eller avkastning.<\/p>\n<p>Till exempel kan det ta veckor att m\u00e4ta planth\u00f6jden \u00f6ver ett f\u00e4lt med 3 000 rutor, d\u00e4r m\u00e4nskliga fel orsakar inkonsekvenser p\u00e5 upp till 20%. Dessutom f\u00f6rb\u00e4ttras sk\u00f6rdarna med bara 0,5\u20131% \u00e5rligen, vilket \u00e4r l\u00e5ngt under den tillv\u00e4xttakt p\u00e5 2,9% som beh\u00f6vs f\u00f6r att m\u00f6ta efterfr\u00e5gan \u00e5r 2050.<\/p>\n<p>Majs, en viktig gr\u00f6da f\u00f6r miljarder, illustrerar denna avmattning: dess \u00e5rliga avkastningstillv\u00e4xt har minskat fr\u00e5n 2,21 TP3T p\u00e5 1960-talet till 1,331 TP3T idag. F\u00f6r att \u00f6verbrygga denna klyfta beh\u00f6ver forskare verktyg som automatiserar datainsamling, minskar fel och snabbar upp beslutsfattandet.<\/p>\n<h2>Hur dr\u00f6nartekniken f\u00f6r\u00e4ndrar v\u00e4xtf\u00f6r\u00e4dling<\/h2>\n<p>Dr\u00f6nare, eller obemannade flygsystem (UAS), utrustade med avancerade sensorer och AI, revolutionerar jordbruket. Dessa enheter kan flyga \u00f6ver f\u00e4lt och samla in exakta data om tusentals v\u00e4xter p\u00e5 n\u00e5gra minuter, en process som kallas h\u00f6gkapacitetsfenotypning (HTP).<\/p>\n<p>Till skillnad fr\u00e5n traditionella metoder samlar dr\u00f6nare in data \u00f6ver hela f\u00e4lt, vilket eliminerar snedvridning i provtagningen. De anv\u00e4nder specialiserade sensorer f\u00f6r att m\u00e4ta allt fr\u00e5n planth\u00f6jd till vattenstressniv\u00e5er.<\/p>\n<p>Till exempel detekterar multispektrala sensorer n\u00e4ra-infrar\u00f6tt ljus som reflekteras av friska l\u00f6v, medan v\u00e4rmekameror identifierar torkstress genom att m\u00e4ta temperaturen i tr\u00e4dkronorna.<\/p>\n<p>Genom att automatisera datainsamling minskar dr\u00f6nare arbetskostnaderna och accelererar f\u00f6r\u00e4dlingscyklerna, vilket g\u00f6r det m\u00f6jligt att utveckla f\u00f6rb\u00e4ttrade gr\u00f6dsorter p\u00e5 \u00e5r ist\u00e4llet f\u00f6r \u00e5rtionden.<\/p>\n<h2>Vetenskapen bakom dr\u00f6narsensorer och datainsamling<\/h2>\n<p>Dr\u00f6nare anv\u00e4nder en m\u00e4ngd olika sensorer f\u00f6r att samla in viktig v\u00e4xtdata. RGB-kameror, det mest prisv\u00e4rda alternativet, f\u00e5ngar synligt ljus f\u00f6r att m\u00e4ta tr\u00e4dkronans t\u00e4ckning och v\u00e4xth\u00f6jd. I sockerr\u00f6rsf\u00e4lt har dessa kameror uppn\u00e5tt en noggrannhet p\u00e5 64\u201369% vid stj\u00e4lkr\u00e4kning, vilket ers\u00e4tter felben\u00e4gna manuella r\u00e4kningar.<\/p>\n<p>Multispektrala sensorer g\u00e5r l\u00e4ngre genom att detektera icke-synliga v\u00e5gl\u00e4ngder som n\u00e4ra-infrar\u00f6tt, vilket korrelerar med klorofyllniv\u00e5er och v\u00e4xth\u00e4lsa. Till exempel har de f\u00f6rutsp\u00e5tt torktolerans i sockerr\u00f6r med \u00f6ver 80%-noggrannhet.<\/p>\n<ul>\n<li><strong>RGB-kameror<\/strong>: F\u00e5nga r\u00f6tt, gr\u00f6nt och bl\u00e5tt ljus f\u00f6r att skapa f\u00e4rgbilder.<\/li>\n<li><strong>Multispektrala sensorer<\/strong>: Detektera ljus bortom det synliga spektrumet (t.ex. n\u00e4ra infrar\u00f6tt).<\/li>\n<li><strong>Termiska sensorer<\/strong>M\u00e4t v\u00e4rme som avges av v\u00e4xter.<\/li>\n<li><strong>LiDAR<\/strong>Anv\u00e4nder laserpulser f\u00f6r att skapa 3D-kartor \u00f6ver v\u00e4xter.<\/li>\n<li><strong>Hyperspektrala sensorer<\/strong>F\u00e5nga 200+ ljusv\u00e5gl\u00e4ngder f\u00f6r ultradetaljerad analys.<\/li>\n<\/ul>\n<p>Termiska sensorer detekterar v\u00e4rmesignaturer och identifierar vattenstressade v\u00e4xter som ser varmare ut \u00e4n friska. I bomullsf\u00e4lt har termiska dr\u00f6nare matchat markbaserade temperaturm\u00e4tningar med mindre \u00e4n 5%-fel.<\/p>\n<p>LiDAR-sensorer anv\u00e4nder laserpulser f\u00f6r att skapa 3D-kartor \u00f6ver gr\u00f6dor, m\u00e4ta biomassa och h\u00f6jd med 95%-precision i energir\u00f6rsf\u00f6rs\u00f6k. De mest avancerade verktygen, hyperspektrala sensorer, analyserar hundratals ljusv\u00e5gl\u00e4ngder f\u00f6r att uppt\u00e4cka n\u00e4ringsbrister eller sjukdomar som \u00e4r osynliga f\u00f6r blotta \u00f6gat.<\/p>\n<p>Dessa sensorer hj\u00e4lpte forskare att koppla 28 nya gener till f\u00f6rdr\u00f6jd \u00e5ldring hos vete, en egenskap som \u00f6kar avkastningen.<\/p>\n<h2>Fr\u00e5n flygning till insikt: Hur dr\u00f6nare analyserar gr\u00f6ddata<\/h2>\n<p>Dr\u00f6narfenotypningsprocessen b\u00f6rjar med noggrann flygplanering. Dr\u00f6nare flyger p\u00e5 30\u2013100 meters h\u00f6jd och tar \u00f6verlappande bilder f\u00f6r att s\u00e4kerst\u00e4lla full t\u00e4ckning. Ett f\u00e4lt p\u00e5 10 hektar kan till exempel skannas p\u00e5 15\u201330 minuter.<\/p>\n<p>Efter flygningen sammanfogar programvara som Agisoft Metashape tusentals bilder till detaljerade kartor med hj\u00e4lp av Structure-from-Motion (SfM) \u2013 en teknik som omvandlar 2D-foton till 3D-modeller. Dessa modeller g\u00f6r det m\u00f6jligt f\u00f6r forskare att m\u00e4ta egenskaper som v\u00e4xth\u00f6jd eller tr\u00e4dkrona med ett enda knapptryck.<\/p>\n<p>AI-algoritmer analyserar sedan data, f\u00f6ruts\u00e4ger avkastning eller identifierar sjukdomsutbrott. Dr\u00f6nare skannade till exempel 3 132 sockerr\u00f6rsodlingar p\u00e5 bara 7 timmar \u2013 en uppgift som skulle ta tre veckor manuellt. Denna hastighet och precision g\u00f6r det m\u00f6jligt f\u00f6r f\u00f6r\u00e4dlare att fatta snabbare beslut, som att kassera l\u00e5gpresterande plantor tidigt p\u00e5 s\u00e4songen.<\/p>\n<h2>Viktiga till\u00e4mpningar av dr\u00f6nare inom modernt jordbruk<\/h2>\n<p>Dr\u00f6nare anv\u00e4nds f\u00f6r att hantera n\u00e5gra av jordbrukets st\u00f6rsta utmaningar. En viktig till\u00e4mpning \u00e4r direkt egenskapsm\u00e4tning, d\u00e4r dr\u00f6nare ers\u00e4tter manuellt arbete. I majsf\u00e4lt m\u00e4ter dr\u00f6nare planth\u00f6jden med 90%-noggrannhet, med sk\u00e4rfel fr\u00e5n 0,5 meter till 0,21 meter.<\/p>\n<p>De sp\u00e5rar ocks\u00e5 tr\u00e4dkronans t\u00e4ckning, ett m\u00e5tt som visar hur v\u00e4l v\u00e4xter skuggar marken f\u00f6r att undertrycka ogr\u00e4s. Energiodlare anv\u00e4nde dessa data f\u00f6r att identifiera sorter som minskar ogr\u00e4stillv\u00e4xten med 40%.<\/p>\n<p>Ett annat genombrott \u00e4r prediktiv avel, d\u00e4r AI-modeller anv\u00e4nder dr\u00f6nardata f\u00f6r att prognostisera gr\u00f6dors prestanda. Till exempel har multispektrala bilder f\u00f6rutsp\u00e5tt majsavkastning med 80%-noggrannhet, vilket \u00f6vertr\u00e4ffar traditionell genomisk testning.<\/p>\n<p>Dr\u00f6nare hj\u00e4lper ocks\u00e5 till med genuppt\u00e4ckt och hj\u00e4lper forskare att lokalisera DNA-segment som \u00e4r ansvariga f\u00f6r \u00f6nskv\u00e4rda egenskaper. Hos vete kopplade dr\u00f6nare tr\u00e4dkronans gr\u00f6nhet till 22 nya gener, vilket potentiellt kan \u00f6ka torktoleransen.<\/p>\n<p>Dessutom uppt\u00e4cker hyperspektrala sensorer sjukdomar som citrusfruktans gr\u00f6nning veckor innan symtomen uppst\u00e5r, vilket ger jordbrukarna tid att agera.<\/p>\n<h2>\u00d6ka genetiska vinster med precisionsteknik<\/h2>\n<p>Genetisk vinst \u2013 den \u00e5rliga f\u00f6rb\u00e4ttringen av gr\u00f6dans egenskaper p\u00e5 grund av avel \u2013 ber\u00e4knas med en enkel formel:<\/p>\n<p style=\"text-align: center;\"><strong>(Survalsintensitet \u00d7 \u00c4rftlighet \u00d7 Egenskapsvariabilitet) \u00f7 Avelscykeltid.<\/strong><\/p>\n<p style=\"text-align: center;\">Genetisk vinst (\u0394G) ber\u00e4knas som:<br \/>\n<strong>\u0394G = (i \u00d7 h\u00b2 \u00d7 \u03c3p) \/ L<\/strong><\/p>\n<p style=\"text-align: left;\">D\u00e4r:<\/p>\n<ul>\n<li><strong>i<\/strong>\u00a0= Urvalsintensitet (hur strikta uppf\u00f6dare \u00e4r).<\/li>\n<li><strong>h\u00b2<\/strong>\u00a0= \u00c4rftlighet (hur mycket av en egenskap som \u00e4rvs fr\u00e5n f\u00f6r\u00e4ldrar till avkomma).<\/li>\n<li><strong>\u03c3p<\/strong>\u00a0= Egenskapsvariabilitet i en population.<\/li>\n<li><strong>L<\/strong>\u00a0= Tid per avelscykel.<\/li>\n<\/ul>\n<p><strong>Varf\u00f6r det spelar roll<\/strong>Dr\u00f6nare f\u00f6rb\u00e4ttrar alla variabler:<\/p>\n<ol start=\"1\">\n<li><strong>i<\/strong>Skanna\u00a0<strong>10 g\u00e5nger fler v\u00e4xter<\/strong>, vilket m\u00f6jligg\u00f6r str\u00e4ngare urval.<\/li>\n<li><strong>h\u00b2<\/strong>Minska m\u00e4tfel, f\u00f6rb\u00e4ttra uppskattningarna av \u00e4rftlighet.<\/li>\n<li><strong>\u03c3p<\/strong>F\u00e5nga upp subtila variationer i egenskaper \u00f6ver hela f\u00e4lt.<\/li>\n<li><strong>L<\/strong>: Minska cykeltiden fr\u00e5n\u00a0<strong>5 \u00e5r till 2\u20133 \u00e5r<\/strong>\u00a0genom tidiga f\u00f6ruts\u00e4gelser.<\/li>\n<\/ol>\n<p>Dr\u00f6nare f\u00f6rb\u00e4ttrar varje del av denna ekvation. Genom att skanna hela f\u00e4lt l\u00e5ter de f\u00f6r\u00e4dlare v\u00e4lja ut de b\u00e4sta 1%-plantorna ist\u00e4llet f\u00f6r de b\u00e4sta 10%-plantorna, vilket \u00f6kar urvalsintensiteten. De f\u00f6rb\u00e4ttrar ocks\u00e5 \u00e4rftlighetsuppskattningarna genom att minska m\u00e4tfel.<\/p>\n<p>Till exempel introducerar manuell bed\u00f6mning av v\u00e4xth\u00f6jd 20%-variabilitet, medan dr\u00f6nare minskar detta till 5%. Dessutom f\u00e5ngar dr\u00f6nare subtila egenskapsvariationer \u00f6ver tusentals v\u00e4xter, vilket maximerar egenskapsvariabiliteten.<\/p>\n<p>Viktigast av allt f\u00f6rkortar de avelscyklerna genom att m\u00f6jligg\u00f6ra tidiga f\u00f6ruts\u00e4gelser. Sockerr\u00f6rsodlare som anv\u00e4nder dr\u00f6nare har tredubblat sina genetiska vinster j\u00e4mf\u00f6rt med traditionella metoder, vilket bevisar teknikens transformerande potential.<\/p>\n<h2>Att \u00f6vervinna utmaningar och omfamna framtiden<\/h2>\n<p>Trots deras l\u00f6fte st\u00e5r dr\u00f6narbaserad fenotypning fortfarande inf\u00f6r betydande utmaningar. Den h\u00f6ga kostnaden f\u00f6r avancerade sensorer \u00e4r fortfarande ett stort hinder \u2013 hyperspektrala kameror kan till exempel \u00f6verstiga $50 000, vilket g\u00f6r dem o\u00f6verkomliga f\u00f6r de flesta sm\u00e5skaliga jordbrukare.<\/p>\n<p>Att bearbeta de enorma m\u00e4ngder data som samlas in kr\u00e4ver ocks\u00e5 betydande molntj\u00e4nstresurser, vilket \u00f6kar kostnaden. AI-plattformar som AutoGIS automatiserar dataanalys, vilket eliminerar behovet av manuell inmatning.<\/p>\n<p>Forskare integrerar ocks\u00e5 dr\u00f6nare med jordsensorer och v\u00e4derstationer, vilket skapar ett realtids\u00f6vervakningssystem som varnar jordbrukare f\u00f6r skadedjur eller torka. Dessa innovationer banar v\u00e4g f\u00f6r en ny era av precisionsjordbruk, d\u00e4r datadrivna beslut ers\u00e4tter gissningar.<\/p>\n<h2>Slutsats<\/h2>\n<p>Dr\u00f6nare och AI f\u00f6r\u00e4ndrar inte bara v\u00e4xtf\u00f6r\u00e4dling \u2013 de omdefinierar h\u00e5llbart jordbruk. Genom att m\u00f6jligg\u00f6ra snabbare utveckling av torkt\u00e5liga, h\u00f6gavkastande gr\u00f6dor skulle dessa tekniker kunna f\u00f6rdubbla livsmedelsproduktionen till 2050 utan att ut\u00f6ka jordbruksmarken.<\/p>\n<p>Detta skulle spara \u00f6ver 100 miljoner hektar skog, motsvarande Egyptens storlek, och minska jordbrukets koldioxidavtryck. Jordbrukare som anv\u00e4nder dr\u00f6nardata har redan minskat vatten- och bek\u00e4mpningsmedelsanv\u00e4ndningen med upp till 30%, vilket skyddar ekosystem och s\u00e4nker kostnaderna.<\/p>\n<p>Som en forskare noterade: \u201cVi gissar inte l\u00e4ngre vilka v\u00e4xter som \u00e4r b\u00e4st. Dr\u00f6narna ber\u00e4ttar det f\u00f6r oss.\u201d Med fortsatt innovation skulle denna sammansm\u00e4ltning av biologi och teknik kunna s\u00e4kerst\u00e4lla livsmedelss\u00e4kerhet f\u00f6r miljarder samtidigt som v\u00e5r planet skyddas.<\/p>\n<p><strong>H\u00e4nvisning<\/strong>Khuimphukhieo, I., &amp; da Silva, JA (2025). F\u00e4ltbaserad h\u00f6gkapacitetsfenotypning (HTP) baserad p\u00e5 obemannade flygplan (UAS) som verktygsl\u00e5da f\u00f6r v\u00e4xtf\u00f6r\u00e4dlare: en omfattande \u00f6versikt. Smart Agricultural Technology, 100888.<\/p>","protected":false},"excerpt":{"rendered":"<p>\u00c5r 2050 f\u00f6rv\u00e4ntas v\u00e4rldens befolkning n\u00e5 9,8 miljarder m\u00e4nniskor, vilket f\u00f6rdubblar efterfr\u00e5gan p\u00e5 mat. Att ut\u00f6ka jordbruksmarken f\u00f6r att m\u00f6ta detta behov \u00e4r dock...<\/p>","protected":false},"author":210157960,"featured_media":11421,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[1377,1378],"tags":[],"class_list":["post-11416","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-crop-monitoring","category-remote-sensing"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How UAS-Based High-Throughput Phenotyping is Transforming Modern Plant Breeding - GeoPard<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/geopard.tech\/swe\/blogg\/hur-uas-baserad-fenotypning-med-hog-genomstromning-forandrar-modern-vaxtforadling\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How UAS-Based High-Throughput Phenotyping is Transforming Modern Plant Breeding - GeoPard\" \/>\n<meta property=\"og:description\" content=\"By 2050, the global population is projected to reach 9.8 billion people, doubling the demand for food. 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