{"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":"kaugseire-taimestikuindeksid-muudavad-kartulisaagi-prognoosimist","status":"publish","type":"post","link":"https:\/\/geopard.tech\/est\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Kaugseire taimestikuindeksid muudavad kartulisaagi prognoosimist"},"content":{"rendered":"<p>Kartul on \u00fcks maailma olulisemaid toidukultuure, mis on miljonitele inimestele p\u00f5hitoiduks. Esiteks aitab kartulitaimede kasvup\u00f5him\u00f5tted ja saagikuse ennustamine p\u00f5llumeestel t\u00f5husamalt hallata niisutust, v\u00e4etamist ja kahjurit\u00f5rjet.<\/p>\n<p>Teiseks saavad toiduainete t\u00f6\u00f6tlejad ja ladustamisasutused logistikat ja t\u00f6\u00f6j\u00f5udu paremini planeerida, kui neil on usaldusv\u00e4\u00e4rsed saagikuse hinnangud. Traditsioonilised meetodid \u2013 n\u00e4iteks p\u00f5ldude l\u00e4bimine ja taimede k\u00e4sitsi m\u00f5\u00f5tmine \u2013 on aga aegan\u00f5udvad ja altid inimlikele vigadele.<\/p>\n<p>Seet\u00f5ttu on teadlased p\u00f6\u00f6rdunud kaugseire poole, mis kasutab satelliitidel, droonidel v\u00f5i pihuarvutitel olevaid kaameraid ja andureid, et j\u00e4lgida kartuli kasvu ja prognoosida saagikust kiiremini ja t\u00e4psemalt.<\/p>\n<h2>Kartulisaagi prognooside m\u00f5istmine<\/h2>\n<p>Viimase kahe aastak\u00fcmne jooksul on huvi kaugseire rakendamise vastu kartuliuuringutes m\u00e4rkimisv\u00e4\u00e4rselt kasvanud. Tegelikult tuvastas s\u00fcstemaatiline \u00fclevaade 482 esialgselt l\u00e4bivaadatud artiklist 79 uuringut, mis avaldati sellel teemal aastatel 2000\u20132022.<\/p>\n<p>L\u00e4bipaistvuse ja reprodutseeritavuse tagamiseks j\u00e4rgisid autorid kehtestatud suuniseid (Kitchenham &amp; Charters 2007; PRISMA raamistik), otsides kaheksast suuremast andmebaasist \u2013 Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis ja SpringerLink \u2013, kasutades termineid nagu \u201ckartuli saagikuse ennustus\u201d JA \u201ckaugseire\u201d.\u201d<\/p>\n<p>Seega kaasati ainult ingliskeelsed originaaluuringud, mis kasutasid kaugseireandmeid kasvu j\u00e4lgimiseks v\u00f5i saagikuse hindamiseks. Lisaks eraldati iga valitud artikli andmed nelja p\u00f5hik\u00fcsimuse alusel:<\/p>\n<ul>\n<li>Millist sensorplatvormi kasutati (satelliit, mehitamata \u00f5hus\u00f5iduk v\u00f5i maapealne)?<\/li>\n<li>Milliseid taimestiku indekseid v\u00f5i spektraalseid tunnuseid hinnati?<\/li>\n<li>Milliseid p\u00f5llukultuuride omadusi j\u00e4lgiti (biomass, lehtede pindala, klorof\u00fcll, l\u00e4mmastik)?<\/li>\n<li>Kui t\u00e4pselt saab ennustada mugulate l\u00f5plikku saagikust (m\u00e4\u00e4ramiskordaja, R\u00b2)?<\/li>\n<\/ul>\n<p>Need k\u00fcsimused aitasid retsensentidel kaardistada hetkeseisu ja tuvastada l\u00fcnki, millele edaspidised uuringud v\u00f5iksid keskenduda.<\/p>\n<h2>Kaugseireplatvormid ja taimestiku indeksid<\/h2>\n<p>Teadlased on kasutanud kolme peamist t\u00fc\u00fcpi kaugseireplatvorme, millel k\u00f5igil on oma eelised ja piirangud. Esiteks pakuvad optilised satelliidid, n\u00e4iteks Sentinel-2 (10 m ruumiline lahutusv\u00f5ime, 5-p\u00e4evane kordusvisiit) ja Landsat 5\u20138 (30 m, 16-p\u00e4evane kordusvisiit), laia leviala ja sageli tasuta juurdep\u00e4\u00e4su andmetele.<\/p>\n<p>Teiseks v\u00f5imaldavad satelliidid nagu MODIS\/TERRA\/Aqua (250\u20131000 m, iga p\u00e4ev kuni kahep\u00e4evase kordusk\u00fclastuseni) ja kommertss\u00fcsteemid nagu PlanetScope (3 m, iga p\u00e4ev, maksumus umbes $218 100 km\u00b2 kohta) sagedasemat v\u00f5i suurema eraldusv\u00f5imega seiret, kuigi kulud v\u00f5ivad olla teguriks.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/est\/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=\"Kaugseireplatvormid ja taimestiku indeksid\" 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>Kolmandaks, mehitamata \u00f5hus\u00f5idukid (UAV-d), mis kannavad multispektraalseid v\u00f5i h\u00fcperspektraalseid kaameraid, pakuvad v\u00e4ga k\u00f5rget eraldusv\u00f5imet (kuni paar sentimeetrit piksli kohta) ja neid saab n\u00f5udmisel lennutada, kuid need katavad v\u00e4iksemaid alasid ja vajavad rohkem logistikat.<\/p>\n<p>L\u00f5puks annavad maapealsed andurid \u2013 n\u00e4iteks pihuarvutid NDVI-meetrid ja SPAD-klorof\u00fclli meetrid \u2013 v\u00e4ga t\u00e4pseid punktm\u00f5\u00f5tmisi, kuigi need on suurtel p\u00f5ldudel kasutamisel aegan\u00f5udvad.<\/p>\n<p>Taimestiku indeksid (VI-d) teisendavad toorpeegeldusv\u00e4\u00e4rtused taimeomaduste sisukateks hinnanguteks. Kartuliuuringutes k\u00f5ige levinumad indeksid on j\u00e4rgmised:<\/p>\n<ul>\n<li>NDVI (normaliseeritud taimestiku erinevusindeks): (NIR \u2013 punane) \/ (NIR + punane)<\/li>\n<li>GNDVI (roheline NDVI): (NIR \u2013 roheline) \/ (NIR + roheline)<\/li>\n<li>NDRE (normaliseeritud erinevus punase serva suhtes): (NIR \u2013 punane serv) \/ (NIR + punane serv)<\/li>\n<li>OSAVI (optimeeritud mulla j\u00e4rgi kohandatud taimestikuindeks): 1,16 \u00d7 (NIR \u2013 punane) \/ (NIR + punane + 0,16)<\/li>\n<li>EVI (t\u00e4iustatud taimestiku indeks), CIred-edge, CIgreen ja palju muud. .<\/li>\n<\/ul>\n<p>Need indeksid valitakse v\u00f5rastiku katte, klorof\u00fclli sisalduse ja mulla tausta suhtes tundlikkuse p\u00f5hjal. Seega on need aluseks taimede tervise hindamisele ja saagikuse ennustamisele.<\/p>\n<h2>Kartuli kasvu j\u00e4lgimine ja saagikuse ennustamine<\/h2>\n<p>Kaugseire abil j\u00e4lgivad teadlased kartuli saagi peamisi omadusi \u2013 maapealset biomassi (AGB), lehtede pindala indeksit (LAI), v\u00f5ra klorof\u00fclli sisaldust (CCC) ja lehtede l\u00e4mmastikusisaldust \u2013 ning seostavad need seej\u00e4rel mugulate l\u00f5pliku saagikusega.<\/p>\n<p>Esiteks v\u00f5ib AGB hindamine ainult VI-de abil olla keeruline, kui v\u00f5rade katvus on tihe, kuna paljud indeksid on k\u00fcllastunud; seet\u00f5ttu parandab VI-de kombineerimine masin\u00f5ppe mudelites sageli t\u00e4psust.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/est\/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=\"Kartuli kasvu j\u00e4lgimine ja saagikuse ennustamine\" 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>Teiseks, LAI \u2013 \u00fchepoolsete lehtede kogupindala maapinna kohta \u2013 hindamisel on saavutatud R\u00b2 v\u00e4\u00e4rtused kuni 0,84, kasutades nii mehitamata \u00f5hus\u00f5idukite h\u00fcperspektraalsete kui ka satelliitidelt saadud multispektraalsete andurite aegridade andmeid.<\/p>\n<p>Kolmandaks, CCC hinnangud, mis on tuletatud sellistest indeksitest nagu CIred-edge, CIgreen, TCARI\/OSAVI ja TCARI + OSAVI, j\u00f5udsid vegetatiivses staadiumis R\u00b2 \u2248 0,85-ni, mis n\u00e4itab tugevat korrelatsiooni laboris m\u00f5\u00f5detud klorof\u00fclliga.<\/p>\n<p>L\u00f5puks ennustati lehtede l\u00e4mmastikusisaldust, mis on terve kasvu jaoks \u00fclioluline, R\u00b2-ga vahemikus 0,52 kuni 0,95, kasutades maapealseid andureid koos regressiooni- v\u00f5i juhusliku metsa mudelitega.<\/p>\n<p>Mugulate saagikuse ennustamisel paistavad silma kaks peamist modelleerimismeetodit:<\/p>\n<p>Empiirilised regressioonimudelid: siin sobitatakse \u00fcks VI \u2013 enamasti NDVI, GNDVI v\u00f5i NDRE \u2013 t\u00f5ep\u00f5hi all oleva saagikuse andmetega. NDVI ja saagikuse seose R\u00b2 v\u00e4\u00e4rtused on vahemikus 0,23 kuni 0,84 (mediaan \u2248 0,67), samas kui NDRE ja saagikuse korrelatsioonid on vahemikus 0,12 kuni 0,85 (mediaan \u2248 0,61).<\/p>\n<p>Masin\u00f5ppe mudelid: nende hulka kuuluvad juhuslik mets, tugivektormasinad ja n\u00e4rviv\u00f5rgud, mis kombineerivad mitut VI-d, spektraalribasid ja mittespektraalseid tegureid, nagu ilm, pinnas ja majandamine. M\u00f5nedes uuringutes on sellised mudelid t\u00f5stnud R\u00b2 v\u00e4\u00e4rtuse 0,93-ni.<\/p>\n<p>Lisaks m\u00f5jutab andmete kogumise ajastus oluliselt ennustuse t\u00e4psust. Mitmetes uuringutes andsid 36\u201355 p\u00e4eva p\u00e4rast istutamist tehtud VI m\u00f5\u00f5tmised k\u00f5rgeima korrelatsiooni l\u00f5pliku mugulasaagiga.<\/p>\n<p>See etapp langeb kokku maksimaalse maakattega ja mugulate arengu algusega, mist\u00f5ttu taime struktuur on l\u00f5pliku saagikuse k\u00f5ige n\u00e4itajam. M\u00f5ned peamised leitud statistilised andmed:<\/p>\n<ul>\n<li>482 tuvastatud uuringu hulgast vastas l\u00e4bivaatamise kriteeriumidele 79 uuringut (2000\u20132022).<\/li>\n<li>Fookusvaldkonnad: saagikuse ennustamine (37 %), lehtede l\u00e4mmastiku staatus (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>K\u00f5ige enamkasutatavad satelliidiplatvormid: Sentinel-2, Landsat, MODIS; kommertskasutuseks: PlanetScope.<\/li>\n<li>R\u00b2 vahemikud: NDVI \u2013 saagis (0,23\u20130,84), NDRE \u2013 saagis (0,12\u20130,85), GNDVI \u2013 saagis (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Kartulisaagi ennustamise soovitused<\/h2>\n<p>Nende leidude p\u00f5hjal peaksid praktikud k\u00f5igepealt valima oma eesm\u00e4rkide saavutamiseks sobiva platvormi. Piirkondlike saagikuse prognooside jaoks pakuvad tasuta Sentinel-2 andmed usaldusv\u00e4\u00e4rset katvust 10-meetrise resolutsiooniga ja 5-p\u00e4evase kordusgraafikuga.<\/p>\n<p>Kohalike hinnangute t\u00e4psustamiseks j\u00e4\u00e4dvustatakse umbes 36\u201355 p\u00e4eva p\u00e4rast maandumist mehitamata \u00f5hus\u00f5idukite lende, mis j\u00e4\u00e4dvustatakse v\u00f5ra kriitilise d\u00fcnaamika j\u00e4\u00e4dvustamiseks ja satelliidimudelite kalibreerimise parandamiseks. Maapealseid andureid on k\u00f5ige parem kasutada pistelisteks kontrollideks ja kaugvaatluste kalibreerimiseks, eriti spektraalandmete kombineerimisel v\u00e4lim\u00f5\u00f5tmistega.<\/p>\n<p>Taimestiku indeksite osas peaksid praktikud l\u00f5pliku saagikuse ennustamisel prioriteediks seadma NDVI, NDRE ja CI <sub>punaserva<\/sub> , kuna need n\u00e4itavad j\u00e4rjepidevalt tugevat korrelatsiooni.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/est\/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=\"Kartulisaagi ennustamise soovitused\" 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>Klorof\u00fclli ja l\u00e4mmastikusisalduse hindamisel annab k\u00f5ige t\u00e4psemad tulemused punase serva indeksite kombineerimine mulla suhtes korrigeeritud viskoossusindeksitega (nt TCARI\/OSAVI). Biomassi hindamisel suurendab t\u00e4psust veelgi viskoossusindeksite integreerimine taime k\u00f5rguse v\u00f5i tekstuuri tunnustega masin\u00f5ppe raamistikes.<\/p>\n<p>Mis puutub modelleerimisse, siis lihtsad lineaarsed v\u00f5i mittelineaarsed regressioonid, mis kasutavad \u00fchte indeksit, on t\u00f5husad, kui t\u00f5ep\u00e4raseid andmeid on piiratud. Kui aga on saadaval mitu indeksit ja abiandmeid (ilm, muld, majandamine), pakuvad masin\u00f5ppe meetodid, n\u00e4iteks juhuslik mets v\u00f5i n\u00e4rviv\u00f5rgud, paremat j\u00f5udlust. Oluline on see, et ajastuskujutised umbes 36\u201355 p\u00e4eva p\u00e4rast istutamist on \u00fcliolulised, kuna see aken annab j\u00e4rjepidevalt suurima ennustust\u00e4psuse.<\/p>\n<h2>Kokkuv\u00f5te<\/h2>\n<p>Kokkuv\u00f5tteks pakub kaugseire kiiret, paindlikku ja t\u00e4pset t\u00f6\u00f6riistakomplekti kartuli kasvu j\u00e4lgimiseks ja mugulate saagikuse prognoosimiseks. Sobiva platvormi valimise, k\u00f5ige informatiivsemate vegetatsiooniindeksite valimise, andmete kogumise ajastamise umbes 36\u201355 p\u00e4eva vanuseks saagiks ja sobivate modelleerimistehnikate rakendamise abil saavad teadlased ja praktikud saagikuse prognoose oluliselt parandada.<\/p>\n<p>See l\u00e4henemisviis mitte ainult ei s\u00e4\u00e4sta aega, vaid toetab ka targemaid juhtimisotsuseid, millest saavad l\u00f5ppkokkuv\u00f5ttes kasu p\u00f5llumehed, agronoomid ja kogu kartuli tarneahela.<\/p>\n<p><strong>Viide<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>jt.<\/i>\u00a0Kartuli kasvu seire ja mugulate saagikuse ennustamise kaugseire abil taimestikuindeksite s\u00fcstemaatiline \u00fclevaade.\u00a0<i>Kartuliuuringud.<\/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>Kartul on \u00fcks maailma olulisemaid toidukultuure, mis on miljonitele inimestele p\u00f5hitoiduks. Esiteks, teadmine, kuidas kartulitaimed kasvavad...<\/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\/est\/blogi\/kaugseire-taimestikuindeksid-muudavad-kartulisaagi-prognoosimist\/\" \/>\n<meta property=\"og:locale\" content=\"et_EE\" \/>\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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