{"id":11785,"date":"2025-07-06T21:42:54","date_gmt":"2025-07-06T19:42:54","guid":{"rendered":"https:\/\/geopard.tech\/?p=11785"},"modified":"2025-07-06T21:48:35","modified_gmt":"2025-07-06T19:48:35","slug":"fjarranalys-av-vegetationsindex-omvandlar-potatisavkastningsprognoser","status":"publish","type":"post","link":"https:\/\/geopard.tech\/swe\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Fj\u00e4rranalys av vegetationsindex transformerar prognoser f\u00f6r potatisavkastning"},"content":{"rendered":"<p>Potatis \u00e4r en av v\u00e4rldens viktigaste livsmedelsgr\u00f6dor och en basf\u00f6da f\u00f6r miljontals m\u00e4nniskor. F\u00f6r det f\u00f6rsta hj\u00e4lper kunskapen om hur potatisplantor v\u00e4xer och m\u00f6jligheten att f\u00f6ruts\u00e4ga deras avkastning b\u00f6nder att hantera bevattning, g\u00f6dsling och skadedjursbek\u00e4mpning mer effektivt.<\/p>\n<p>F\u00f6r det andra kan livsmedelsproducenter och lageranl\u00e4ggningar b\u00e4ttre planera logistik och arbetskraft n\u00e4r de har tillf\u00f6rlitliga avkastningsuppskattningar. Traditionella metoder \u2013 som att fysiskt g\u00e5 igenom f\u00e4lt och m\u00e4ta plantor f\u00f6r hand \u2013 \u00e4r dock tidskr\u00e4vande och ben\u00e4gna att orsaka m\u00e4nskliga fel.<\/p>\n<p>D\u00e4rf\u00f6r har forskare v\u00e4nt sig till fj\u00e4rranalys, som anv\u00e4nder kameror och sensorer p\u00e5 satelliter, dr\u00f6nare eller handh\u00e5llna enheter, f\u00f6r att \u00f6vervaka potatistillv\u00e4xt och prognostisera avkastning snabbare och mer exakt.<\/p>\n<h2>F\u00f6rst\u00e5 prognoser f\u00f6r potatisavkastning<\/h2>\n<p>Under de senaste tv\u00e5 decennierna har intresset f\u00f6r att till\u00e4mpa fj\u00e4rranalys inom potatisforskning \u00f6kat avsev\u00e4rt. Faktum \u00e4r att en systematisk granskning identifierade 79 studier publicerade mellan 2000 och 2022 om detta \u00e4mne, av 482 initialt granskade artiklar.<\/p>\n<p>F\u00f6r att s\u00e4kerst\u00e4lla transparens och reproducerbarhet f\u00f6ljde f\u00f6rfattarna etablerade riktlinjer (Kitchenham &amp; Charters 2007; PRISMA-ramverket) och s\u00f6kte i \u00e5tta stora databaser \u2013 Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis och SpringerLink \u2013 med termer som \u201cpotatisavkastningsprediktion\u201d OCH \u201cfj\u00e4rranalys\u201d.\u201d<\/p>\n<p>F\u00f6ljaktligen inkluderades endast originalforskning p\u00e5 engelska som anv\u00e4nde fj\u00e4rranalysdata f\u00f6r tillv\u00e4xt\u00f6vervakning eller avkastningsuppskattning. Dessutom extraherades data fr\u00e5n varje utvald artikel enligt fyra nyckelfr\u00e5gor:<\/p>\n<ul>\n<li>Vilken sensorplattform anv\u00e4ndes (satellit, dr\u00f6nare eller markbaserad)?<\/li>\n<li>Vilka vegetationsindex eller spektrala egenskaper utv\u00e4rderades?<\/li>\n<li>Vilka gr\u00f6degenskaper \u00f6vervakades (biomassa, bladyta, klorofyll, kv\u00e4ve)?<\/li>\n<li>Hur noggrant kan man f\u00f6ruts\u00e4ga den slutliga kn\u00f6lavkastningen (best\u00e4mningskoefficient, R\u00b2)?<\/li>\n<\/ul>\n<p>Dessa fr\u00e5gor hj\u00e4lpte granskarna att kartl\u00e4gga l\u00e4get och identifiera luckor som framtida forskning skulle kunna fokusera p\u00e5.<\/p>\n<h2>Fj\u00e4rranalysplattformar och vegetationsindex<\/h2>\n<p>Forskare har anv\u00e4nt tre huvudtyper av fj\u00e4rranalysplattformar, var och en med sina egna f\u00f6rdelar och begr\u00e4nsningar. F\u00f6r det f\u00f6rsta erbjuder optiska satelliter som Sentinel-2 (10 m rumslig uppl\u00f6sning, 5 dagars \u00e5terbes\u00f6k) och Landsat 5\u20138 (30 m, 16 dagars \u00e5terbes\u00f6k) bred t\u00e4ckning och ofta gratis data\u00e5tkomst.<\/p>\n<p>F\u00f6r det andra m\u00f6jligg\u00f6r satelliter som MODIS\/TERRA\/Aqua (250\u20131000 m, dagligen till \u00e5terbes\u00f6k varannan dag) och kommersiella system som PlanetScope (3 m, dagligen, kostar cirka $218 per 100 km\u00b2) mer frekvent eller h\u00f6gre uppl\u00f6snings\u00f6vervakning, \u00e4ven om kostnaderna kan vara en faktor.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/swe\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/remote-sensing-platforms-and-vegetation-indices\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?fit=1024%2C1024&amp;ssl=1\" data-orig-size=\"1024,1024\" data-comments-opened=\"1\" data-image-title=\"Remote Sensing Platforms and Vegetation Indices\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?fit=1024%2C1024&amp;ssl=1\" class=\"alignnone size-full wp-image-11792\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=810%2C810&#038;ssl=1\" alt=\"Fj\u00e4rranalysplattformar och vegetationsindex\" width=\"810\" height=\"810\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=150%2C150&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=768%2C768&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Remote-Sensing-Platforms-and-Vegetation-Indices.webp?resize=120%2C120&amp;ssl=1 120w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>F\u00f6r det tredje ger obemannade flygfarkoster (UAV) med multispektrala eller hyperspektrala kameror mycket h\u00f6g uppl\u00f6sning (ner till n\u00e5gra centimeter per pixel) och kan flygas p\u00e5 beg\u00e4ran, men de t\u00e4cker mindre omr\u00e5den och kr\u00e4ver mer logistik.<\/p>\n<p>Slutligen ger markbaserade sensorer \u2013 s\u00e5som handh\u00e5llna NDVI-m\u00e4tare och SPAD-klorofyllm\u00e4tare \u2013 punktm\u00e4tningar som \u00e4r mycket exakta, \u00e4ven om de \u00e4r tidskr\u00e4vande n\u00e4r de anv\u00e4nds \u00f6ver stora f\u00e4lt.<\/p>\n<p>Vegetationsindex (VI) \u00f6vers\u00e4tter r\u00e5a reflektansv\u00e4rden till meningsfulla uppskattningar av v\u00e4xtegenskaper. De vanligaste indexen i potatisstudier inkluderar:<\/p>\n<ul>\n<li>NDVI (Normaliserat skillnadsvegetationsindex): (NIR \u2013 R\u00f6d) \/ (NIR + R\u00f6d)<\/li>\n<li>GNDVI (Gr\u00f6n NDVI): (NIR \u2013 Gr\u00f6n) \/ (NIR + Gr\u00f6n)<\/li>\n<li>NDRE (Normaliserad skillnad Red-Edge): (NIR \u2013 RedEdge) \/ (NIR + RedEdge)<\/li>\n<li>OSAVI (Optimerat jordjusterat vegetationsindex): 1,16 \u00d7 (NIR \u2013 R\u00f6d) \/ (NIR + R\u00f6d + 0,16)<\/li>\n<li>EVI (Enhanced Vegetation Index), CIred-edge, CIgreen och mer. .<\/li>\n<\/ul>\n<p>Dessa index v\u00e4ljs utifr\u00e5n deras k\u00e4nslighet f\u00f6r tr\u00e4dkronor, klorofyllhalt och jordbakgrund. F\u00f6ljaktligen fungerar de som grund f\u00f6r att uppskatta v\u00e4xth\u00e4lsa och f\u00f6ruts\u00e4ga avkastning.<\/p>\n<h2>Potatis\u00f6vervakning av tillv\u00e4xt och f\u00f6ruts\u00e4gelse av avkastning<\/h2>\n<p>Genom fj\u00e4rranalys \u00f6vervakar forskare viktiga egenskaper hos potatisgr\u00f6dor \u2013 ovanjordisk biomassa (AGB), bladareaindex (LAI), klorofyllinneh\u00e5ll i tr\u00e4dkronan (CCC) och kv\u00e4vestatus i bladen \u2013 och relaterar sedan dessa till slutlig kn\u00f6lsk\u00f6rd.<\/p>\n<p>F\u00f6r det f\u00f6rsta kan det vara utmanande att uppskatta AGB med hj\u00e4lp av enbart VI n\u00e4r tr\u00e4dkronorna \u00e4r t\u00e4ta eftersom m\u00e5nga index m\u00e4ttar; d\u00e4rf\u00f6r f\u00f6rb\u00e4ttrar kombinationen av VI med v\u00e4xth\u00f6jd eller texturegenskaper i maskininl\u00e4rningsmodeller ofta noggrannheten.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/swe\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/potato-monitoring-growth-and-predicting-yield\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?fit=1024%2C1024&amp;ssl=1\" data-orig-size=\"1024,1024\" data-comments-opened=\"1\" data-image-title=\"Potato Monitoring Growth and Predicting Yield\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?fit=1024%2C1024&amp;ssl=1\" class=\"alignnone size-full wp-image-11793\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=810%2C810&#038;ssl=1\" alt=\"Potatis\u00f6vervakning av tillv\u00e4xt och f\u00f6ruts\u00e4gelse av avkastning\" width=\"810\" height=\"810\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=150%2C150&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=768%2C768&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Monitoring-Growth-and-Predicting-Yield.webp?resize=120%2C120&amp;ssl=1 120w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>F\u00f6r det andra har bed\u00f6mningen av LAI \u2013 den totala ensidiga l\u00f6vytan per markyta \u2013 uppn\u00e5tt R\u00b2-v\u00e4rden upp till 0,84 genom att anv\u00e4nda tidsseriedata fr\u00e5n b\u00e5de hyperspektrala UAV- och multispektrala satellitsensorer.<\/p>\n<p>F\u00f6r det tredje n\u00e5dde CCC-uppskattningar, h\u00e4rledda fr\u00e5n index som CIred-edge, CIgreen, TCARI\/OSAVI och TCARI + OSAVI, R\u00b2 \u2248 0,85 under det vegetativa stadiet, vilket indikerar stark korrelation med laboratoriem\u00e4tt klorofyll.<\/p>\n<p>Slutligen f\u00f6rutsp\u00e5ddes bladens kv\u00e4vestatus, avg\u00f6rande f\u00f6r h\u00e4lsosam tillv\u00e4xt, med R\u00b2 fr\u00e5n 0,52 till 0,95 vid anv\u00e4ndning av markbaserade sensorer plus regressions- eller slumpm\u00e4ssiga skogsmodeller.<\/p>\n<p>N\u00e4r det g\u00e4ller att f\u00f6ruts\u00e4ga kn\u00f6lavkastning sticker tv\u00e5 huvudsakliga modelleringsmetoder ut:<\/p>\n<p>Empiriska regressionsmodeller: H\u00e4r anpassas ett enda VI \u2013 oftast NDVI, GNDVI eller NDRE \u2013 till markbaserad avkastningsdata. Rapporterade R\u00b2-v\u00e4rden f\u00f6r NDVI kontra avkastning varierar fr\u00e5n 0,23 till 0,84 (median \u2248 0,67), medan NDRE-avkastningskorrelationer varierar fr\u00e5n 0,12 till 0,85 (median \u2248 0,61).<\/p>\n<p>Maskininl\u00e4rningsmodeller: Dessa inkluderar slumpm\u00e4ssiga skogar, st\u00f6dvektormaskiner och neurala n\u00e4tverk som kombinerar flera VI:er, spektralband och icke-spektrala faktorer som v\u00e4der, jordm\u00e5n och sk\u00f6tsel. S\u00e5dana modeller har drivit upp R\u00b2 till 0,93 i vissa studier.<\/p>\n<p>Dessutom p\u00e5verkar tidpunkten f\u00f6r datainsamlingen i h\u00f6g grad prediktionsnoggrannheten. I flera studier gav VI-m\u00e4tningar gjorda 36\u201355 dagar efter plantering (DAP) de h\u00f6gsta korrelationerna med slutlig kn\u00f6lavkastning.<\/p>\n<p>Detta skede infaller i linje med maximal markt\u00e4ckning och b\u00f6rjan av kn\u00f6lbildning, vilket g\u00f6r plantstrukturen till den mest indikativa faktorn f\u00f6r slutlig avkastning. N\u00e5gra av de viktigaste statistikuppgifterna som hittats:<\/p>\n<ul>\n<li>79 studier (2000\u20132022) uppfyllde granskningskriterierna, av 482 identifierade.<\/li>\n<li>Fokusomr\u00e5den: avkastningsprognos (37 %), kv\u00e4vestatus i blad (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>Mest anv\u00e4nda satellitplattformar: Sentinel-2, Landsat, MODIS; kommersiella: PlanetScope.<\/li>\n<li>R\u00b2-intervall: NDVI\u2013utbyte (0,23\u20130,84), NDRE\u2013utbyte (0,12\u20130,85), GNDVI\u2013utbyte (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Rekommendationer f\u00f6r potatisavkastningsprognos<\/h2>\n<p>Baserat p\u00e5 dessa resultat b\u00f6r yrkesverksamma f\u00f6rst v\u00e4lja l\u00e4mplig plattform f\u00f6r sina m\u00e5l. F\u00f6r regionala avkastningsprognoser ger gratis Sentinel-2-data tillf\u00f6rlitlig t\u00e4ckning med 10 m uppl\u00f6sning och ett 5-dagars \u00e5terbes\u00f6ksschema.<\/p>\n<p>F\u00f6r att f\u00f6rfina lokala uppskattningar f\u00e5ngar dr\u00f6nare som \u00e4r planerade runt 36\u201355 dagar efter plantering kritisk dynamik i tr\u00e4dkronorna och f\u00f6rb\u00e4ttrar kalibreringen av satellitmodeller. Marksensorer anv\u00e4nds b\u00e4st f\u00f6r stickprovskontroller och f\u00f6r att kalibrera fj\u00e4rrobservationer, s\u00e4rskilt n\u00e4r man kombinerar spektraldata med f\u00e4ltm\u00e4tningar.<\/p>\n<p>N\u00e4r det g\u00e4ller vegetationsindex b\u00f6r ut\u00f6vare prioritera NDVI, NDRE och CI <sub>red-edge<\/sub> f\u00f6r att f\u00f6ruts\u00e4ga slutsk\u00f6rd, eftersom dessa konsekvent visar starka korrelationer.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/swe\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/potato-yield-prediction-recommendations\/\" data-orig-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?fit=1024%2C1024&amp;ssl=1\" data-orig-size=\"1024,1024\" data-comments-opened=\"1\" data-image-title=\"Potato Yield Prediction Recommendations\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?fit=1024%2C1024&amp;ssl=1\" class=\"alignnone size-full wp-image-11794\" src=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=810%2C810&#038;ssl=1\" alt=\"Rekommendationer f\u00f6r potatisavkastningsprognos\" width=\"810\" height=\"810\" srcset=\"https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?w=1024&amp;ssl=1 1024w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=150%2C150&amp;ssl=1 150w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=768%2C768&amp;ssl=1 768w, https:\/\/i0.wp.com\/geopard.tech\/wp-content\/uploads\/2025\/07\/Potato-Yield-Prediction-Recommendations.webp?resize=120%2C120&amp;ssl=1 120w\" sizes=\"(max-width: 810px) 100vw, 810px\" \/><\/p>\n<p>Vid uppskattning av klorofyll- och kv\u00e4vehalt ger kombinationen av r\u00f6dkantsindex med jordjusterade VI:er \u2013 s\u00e5som TCARI\/OSAVI \u2013 de mest exakta resultaten. F\u00f6r uppskattning av biomassa \u00f6kar integreringen av VI:er med v\u00e4xth\u00f6jd eller texturegenskaper inom ramverk f\u00f6r maskininl\u00e4rning ytterligare noggrannheten.<\/p>\n<p>N\u00e4r det g\u00e4ller modellering \u00e4r enkla linj\u00e4ra eller icke-linj\u00e4ra regressioner med ett enda index effektiva n\u00e4r markdata \u00e4r begr\u00e4nsade. Men n\u00e4r flera index och kompletterande data (v\u00e4der, jordm\u00e5n, sk\u00f6tsel) \u00e4r tillg\u00e4ngliga, erbjuder maskininl\u00e4rningsmetoder som slumpm\u00e4ssig skog eller neurala n\u00e4tverk \u00f6verl\u00e4gsen prestanda. Viktigt \u00e4r att tidpunkten f\u00f6r bilder runt 36\u201355 dagar efter plantering \u00e4r avg\u00f6rande, eftersom detta f\u00f6nster konsekvent ger den h\u00f6gsta prediktionsnoggrannheten.<\/p>\n<h2>Slutsats<\/h2>\n<p>Sammanfattningsvis erbjuder fj\u00e4rranalys en snabb, flexibel och noggrann verktygsl\u00e5da f\u00f6r att \u00f6vervaka potatistillv\u00e4xt och f\u00f6ruts\u00e4ga kn\u00f6lavkastning. Genom att v\u00e4lja l\u00e4mplig plattform, v\u00e4lja de mest informativa vegetationsindexen, tidsinsamling av data runt 36\u201355 DAP och till\u00e4mpa l\u00e4mpliga modelleringstekniker kan forskare och yrkesverksamma avsev\u00e4rt f\u00f6rb\u00e4ttra avkastningsprognoserna.<\/p>\n<p>Denna metod sparar inte bara tid utan st\u00f6der ocks\u00e5 smartare f\u00f6rvaltningsbeslut, vilket i slut\u00e4ndan gynnar jordbrukare, agronomer och hela potatisf\u00f6rs\u00f6rjningskedjan.<\/p>\n<p><strong>H\u00e4nvisning<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>m.fl.<\/i>\u00a0En systematisk granskning av vegetationsindex f\u00f6r \u00f6vervakning av potatis tillv\u00e4xt och f\u00f6ruts\u00e4gelse av kn\u00f6lavkastning med hj\u00e4lp av fj\u00e4rranalys.\u00a0<i>Potatis Res.<\/i>\u00a0<b>68<\/b>, 409\u2013448 (2025). <a href=\"https:\/\/doi.org\/10.1007\/s11540-024-09748-7\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s11540-024-09748-7<\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Potatis \u00e4r en av v\u00e4rldens viktigaste livsmedelsgr\u00f6dor och \u00e4r en basf\u00f6da f\u00f6r miljontals m\u00e4nniskor. F\u00f6r det f\u00f6rsta, att veta hur potatisplantor v\u00e4xer ...<\/p>","protected":false},"author":210157960,"featured_media":11791,"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":[1378],"tags":[],"class_list":["post-11785","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","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>Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - 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\/fjarranalys-av-vegetationsindex-omvandlar-potatisavkastningsprognoser\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard\" \/>\n<meta property=\"og:description\" content=\"Potato stands as one of the world\u2019s most important food crops, serving as a staple for millions of people. 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