{"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":"daljinsko-istrazivanje-indeksa-vegetacije-transformira-prognoziranje-prinosa-krumpira","status":"publish","type":"post","link":"https:\/\/geopard.tech\/hr\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Daljinska istra\u017eivanja indeksa vegetacije mijenjaju prognoziranje prinosa krumpira"},"content":{"rendered":"<p>Krumpir je jedna od najva\u017enijih svjetskih prehrambenih kultura, slu\u017ee\u0107i kao osnovna namirnica za milijune ljudi. Prvo, poznavanje na\u010dina na koji krumpir raste i mogu\u0107nost predvi\u0111anja njegovog prinosa poma\u017ee poljoprivrednicima u\u010dinkovitije upravljati navodnjavanjem, gnojidbom i suzbijanjem \u0161tetnika.<\/p>\n<p>Drugo, prera\u0111iva\u010di hrane i skladi\u0161ta mogu bolje planirati logistiku i radnu snagu kada imaju pouzdane procjene prinosa. Me\u0111utim, tradicionalne metode \u2013 poput fizi\u010dkog obilaska polja i ru\u010dnog mjerenja biljaka \u2013 dugotrajne su i sklone ljudskim pogre\u0161kama.<\/p>\n<p>Stoga su se znanstvenici okrenuli daljinskom istra\u017eivanju (remote sensing), koje koristi kamere i senzore na satelitima, bespilotnim letjelicama ili ru\u010dnim ure\u0111ajima, kako bi br\u017ee i to\u010dnije pratili rast krumpira i predvi\u0111ali prinose.<\/p>\n<h2>Predvi\u0111anje prinosa krumpira<\/h2>\n<p>Tijekom protekla dva desetlje\u0107a zna\u010dajno je porastao interes za primjenu daljinskih istra\u017eivanja u istra\u017eivanju krumpira. Zapravo, sustavni pregled identificirao je 79 studija objavljenih izme\u0111u 2000. i 2022. na ovu temu, od 482 izvorno pregledana \u010dlanka.<\/p>\n<p>Kako bi se osigurala transparentnost i ponovljivost, autori su se pridr\u017eavali uspostavljenih smjernica (Kitchenham &amp; Charters 2007; PRISMA okvir), pretra\u017euju\u0107i osam glavnih baza podataka\u2014Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis i SpringerLink\u2014koriste\u0107i pojmove poput \u201cpotato yield prediction\u201d I \u201cremote sensing\u201d.\u201d<\/p>\n<p>Posljedi\u010dno, uklju\u010dena su samo izvorna istra\u017eivanja na engleskom jeziku koja su koristila podatke daljinskih istra\u017eivanja za pra\u0107enje rasta ili procjenu prinosa. Nadalje, podaci iz svakog odabranog rada prikupljeni su prema \u010detiri klju\u010dna pitanja:<\/p>\n<ul>\n<li>Koja je kori\u0161tena platforma senzora (satelit, bespilotna letjelica ili zemaljska)?<\/li>\n<li>Koji su vegetacijski indeksi ili spektralne zna\u010dajke evaluirani?<\/li>\n<li>Koje su se biljne osobine pratile (biomasa, lisna povr\u0161ina, klorofil, du\u0161ik)?<\/li>\n<li>Koliko to\u010dno predvi\u0111eni kona\u010dni prinos krumpira (koeficijent determinacije, R\u00b2)?<\/li>\n<\/ul>\n<p>Ova pitanja pomogla su recenzentima da mapiraju trenutno stanje i identificiraju praznine gdje bi se budu\u0107a istra\u017eivanja mogla fokusirati.<\/p>\n<h2>Platforme za daljinska istra\u017eivanja i Indeksi vegetacije<\/h2>\n<p>Istra\u017eiva\u010di su koristili tri glavne vrste platformi za daljinsko istra\u017eivanje, od kojih svaka ima svoje prednosti i nedostatke. Prvo, opti\u010dki sateliti kao \u0161to su Sentinel-2 (prostorna rezolucija 10 m, ponovni posjet svakih 5 dana) i Landsat 5\u20138 (30 m, ponovni posjet svakih 16 dana) nude \u0161iroku pokrivenost i \u010desto besplatan pristup podacima.<\/p>\n<p>Drugo, sateliti poput MODIS\/TERRA\/Aqua (250\u20131000\u202fm, s ponovnim posjetom od dnevnog do 2 dana) i komercijalni sustavi poput PlanetScope (3\u202fm, dnevni, stoji oko $218 po 100\u202fkm\u00b2) omogu\u0107uju \u010de\u0161\u0107e pra\u0107enje ili pra\u0107enje vi\u0161e rezolucije, iako tro\u0161kovi mogu biti faktor.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/hr\/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-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" 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=\"Platforme za daljinska istra\u017eivanja i Indeksi vegetacije\" 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>Tre\u0107e, bespilotne letjelice (UAV) opremljene multispektralnim ili hiperspektralnim kamerama pru\u017eaju vrlo visoku rezoluciju (do nekoliko centimetara po pikselu) i mogu se letjeti na zahtjev, ali pokrivaju manje povr\u0161ine i zahtijevaju vi\u0161e logistike.<\/p>\n<p>Naposljetku, senzori temeljeni na zemlji \u2013 poput ru\u010dnih NDVI metara i SPAD metara za klorofil \u2013 daju precizna mjerenja na licu mjesta, iako su dugotrajni kada se koriste na velikim poljima.<\/p>\n<p>Vegetacijski indeksi (VI-evi) pretvaraju sirove vrijednosti refleksije u smislene procjene biljnih svojstava. Naj\u010de\u0161\u0107i indeksi u istra\u017eivanjima krumpira uklju\u010duju:<\/p>\n<ul>\n<li>NDVI (Indeks normalizirane razlike vegetacije): (BLN \u2013 Crvena) \/ (BLN + Crvena)<\/li>\n<li>GNDVI (Zeleni NDVI): (NIR \u2013 Zeleni) \/ (NIR + Zeleni)<\/li>\n<li>NDRE (Normalizirani razmak crvenog ruba): (bliske infracrvene - crveni rub) \/ (bliske infracrvene + crveni rub)<\/li>\n<li>OSAVI (Optimizirani indeks vegetacije prilago\u0111en tlu): 1.16 \u00d7 (NIR \u2013 Crveno) \/ (NIR + Crveno + 0.16)<\/li>\n<li>EVI (Pobolj\u0161ani vegetacijski indeks), CIred\u2011edge, CIgreen i vi\u0161e. .<\/li>\n<\/ul>\n<p>Ovi indeksi odabrani su na temelju njihove osjetljivosti na pokrovnost kro\u0161njama, sadr\u017eaj klorofila i pozadinu tla. Posljedi\u010dno, oni slu\u017ee kao temelj za procjenu zdravlja biljaka i predvi\u0111anje prinosa.<\/p>\n<h2>Pra\u0107enje rasta krumpira i predvi\u0111anje prinosa<\/h2>\n<p>Kroz daljinska istra\u017eivanja, istra\u017eiva\u010di prate klju\u010dne osobine usjeva krumpira \u2013 nadzemnu biomasu (AGB), indeks lisne povr\u0161ine (LAI), sadr\u017eaj klorofila u kro\u0161nji (CCC) i status du\u0161ika u li\u0161\u0107u \u2013 te ih zatim povezuju s kona\u010dnim prinosom gomolja.<\/p>\n<p>Prvo, procjena ukupne suhe biomase (AGB) samo pomo\u0107u vizualnih indeksa (VIs) mo\u017ee biti izazovna kada je pokrovnost kro\u0161anja gusta jer se mnogi indeksi zasi\u0107uju; stoga kombiniranje VI-ova s visinom biljke ili teksturalnim zna\u010dajkama u modelima strojnog u\u010denja \u010desto pobolj\u0161ava to\u010dnost.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/hr\/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-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" 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=\"Pra\u0107enje rasta krumpira i predvi\u0111anje prinosa\" 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>Drugo, procjena LAI-a \u2014 ukupne jednostrane povr\u0161ine lista po povr\u0161ini tla \u2014 postigla je R\u00b2 vrijednosti do 0,84 kori\u0161tenjem podataka vremenskih serija iz hiperspektralnih senzora besposadnih zrakoplova i multispektralnih satelitskih senzora.<\/p>\n<p>Tre\u0107e, CCC procjene, izvedene iz indeksa kao \u0161to su CIred\u2011edge, CIgreen, TCARI\/OSAVI i TCARI\u202f+\u202fOSAVI, dosegle su R\u00b2 \u2248 0,85 tijekom vegetativne faze, \u0161to ukazuje na jaku korelaciju s klorofilom izmjerenim u laboratoriju.<\/p>\n<p>Naposljetku, status du\u0161ika u li\u0161\u0107u, klju\u010dan za zdrav rast, predvi\u0111en je s R\u00b2 u rasponu od 0,52 do 0,95 kada su kori\u0161teni senzori temeljeni na tlu uz regresijske ili modele slu\u010dajnih \u0161uma.<\/p>\n<p>Kada je rije\u010d o predvi\u0111anju prinosa gomolja, izdvajaju se dva glavna modelarska pristupa:<\/p>\n<p>Empirijski regresijski modeli: Ovdje se pojedina\u010dni VI \u2014 naj\u010de\u0161\u0107e NDVI, GNDVI ili NDRE \u2014 povezuje s podacima o prinosu dobivenim na terenu. Prijavljene R\u00b2 vrijednosti za NDVI u odnosu na prinos kre\u0107u se od 0,23 do 0,84 (medijan \u2248 0,67), dok se korelacije NDRE\u2013prinos kre\u0107u od 0,12 do 0,85 (medijan \u2248 0,61).<\/p>\n<p>Modeli strojnog u\u010denja: To uklju\u010duje slu\u010dajne \u0161ume, potporne vektorske strojeve i neuronske mre\u017ee koje kombiniraju vi\u0161e VI-ova, spektralne pojaseve i nespektralne \u010dimbenike poput vremena, tla i upravljanja. Takvi su modeli u nekim studijama pove\u0107ali R\u00b2 do 0,93.<\/p>\n<p>Nadalje, vremenski razmak prikupljanja podataka zna\u010dajno utje\u010de na to\u010dnost predvi\u0111anja. Prema vi\u0161estrukim studijama, mjerenja VI provedena 36\u201355 dana nakon sadnje (DAP) dala su najve\u0107e korelacije s kona\u010dnim prinosom gomolja.<\/p>\n<p>Ova faza odgovara maksimalnom pokrivanju tla i po\u010detku formiranja gomolja, \u010dime struktura biljke najvi\u0161e ukazuje na kona\u010dni prinos. Neke od klju\u010dnih prona\u0111enih statistika:<\/p>\n<ul>\n<li>79 studija (2000. \u2013 2022.) ispunilo je kriterije pregleda, od identificiranih 482.<\/li>\n<li>Podru\u010dja fokusa: predvi\u0111anje prinosa (37%), status N li\u0161\u0107a (21%), AGB (15%), LAI (15%), CCC (12%).<\/li>\n<li>Najvi\u0161e kori\u0161tene satelitske platforme: Sentinel-2, Landsat, MODIS; komercijalne: PlanetScope.<\/li>\n<li>R\u00b2 rasponi: NDVI\u2013prinos (0,23\u20130,84), NDRE\u2013prinos (0,12\u20130,85), GNDVI\u2013prinos (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Preporuke za predvi\u0111anje prinosa krumpira<\/h2>\n<p>Na temelju ovih nalaza, prakti\u010dari bi prvo trebali odabrati odgovaraju\u0107u platformu za svoje ciljeve. Za prognoze regionalnih prinosa, besplatni Sentinel-2 podaci pru\u017eaju pouzdanu pokrivenost s rezolucijom od 10 m i u\u010destalo\u0161\u0107u ponovnog snimanja od 5 dana.<\/p>\n<p>Za preciznije lokalne procene, letovi bespilotnih letelica zakazani oko 36\u201355 dana nakon sadnje hvataju klju\u010dne dinamike kro\u0161nje i pobolj\u0161avaju kalibraciju satelitskih modela. Zemaljski senzori se najbolje koriste za povremene provere i kalibraciju daljinskih posmatranja, posebno prilikom kombinovanja spektralnih podataka sa merenjima na terenu.<\/p>\n<p>\u0160to se ti\u010de vegetacijskih indeksa, prakti\u010dari bi trebali dati prednost NDVI, NDRE i CI<sub>red-edge<\/sub> za predvi\u0111anje kona\u010dnog prinosa, jer oni dosljedno pokazuju sna\u017ene korelacije.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/hr\/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-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" 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=\"Preporuke za predvi\u0111anje prinosa krumpira\" 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>Pri procjeni sadr\u017eaja klorofila i du\u0161ika, kombiniranje indeksa crvenog ruba s VIs prilago\u0111enim tlu \u2013 poput TCARI\/OSAVI \u2013 daje najto\u010dnije rezultate. Za procjenu biomase, integracija VIs-a s visinom biljke ili teksturnim zna\u010dajkama unutar okvira strojnog u\u010denja dodatno pove\u0107ava to\u010dnost.<\/p>\n<p>Kad je rije\u010d o modeliranju, jednostavne linearne ili nelinearne regresije koje koriste jedan indeks u\u010dinkovite su kada su podaci o stvarnom stanju ograni\u010deni. Me\u0111utim, kada su dostupni vi\u0161estruki indeksi i dodatni podaci (vrijeme, tlo, upravljanje), metode strojnog u\u010denja kao \u0161to su nasumi\u010dna \u0161uma ili neuronske mre\u017ee nude superiorne performanse. Va\u017eno je napomenuti da je snimanje slika oko 36\u201355 dana nakon sadnje klju\u010dno, jer taj vremenski period dosljedno pru\u017ea najvi\u0161u to\u010dnost predvi\u0111anja.<\/p>\n<h2>Zaklju\u010dak<\/h2>\n<p>Zaklju\u010dno, daljinska istra\u017eivanja nude brzi, fleksibilni i to\u010dni skup alata za pra\u0107enje rasta krumpira i predvi\u0111anje prinosa gomolja. Odabirom odgovaraju\u0107e platforme, odabirom najinformativnijih indeksa vegetacije, tempiranjem prikupljanja podataka oko 36\u201355 DAP (dana nakon nicanja) i primjenom prikladnih tehnika modeliranja, istra\u017eiva\u010di i prakti\u010dari mogu zna\u010dajno pobolj\u0161ati prognoze prinosa.<\/p>\n<p>Ovaj pristup ne samo da \u0161tedi vrijeme, ve\u0107 podr\u017eava i dono\u0161enje pametnijih upravlja\u010dkih odluka, \u0161to u kona\u010dnici koristi poljoprivrednicima, agronomima i cijelom lancu opskrbe krumpirom.<\/p>\n<p><strong>Referenca<\/strong>Mukiibi, A., Machakaire, A.T.B., Franke, A.C.\u00a0<i>i sur.<\/i>\u00a0Sustavni pregled vegetacijskih indeksa za pra\u0107enje rasta krumpira i predvi\u0111anje prinosa gomolja iz daljinskog istra\u017eivanja.\u00a0<i>Krumpir.<\/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>Krumpir je jedna od najva\u017enijih svjetskih prehrambenih kultura i osnovna je namirnica za milijune ljudi. Prvo, poznavanje na\u010dina rasta krumpira\u2026<\/p>","protected":false},"author":210249433,"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":"","_crdt_document":"","content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","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 Premium plugin v21.6 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture<\/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\/hr\/blog\/daljinsko-istrazivanje-indeksa-vegetacije-transformira-prognoziranje-prinosa-krumpira\/\" \/>\n<meta property=\"og:locale\" content=\"hr_HR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting\" \/>\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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