{"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":"indexy-vegetacie-dialkoveho-prieskumu-zeme-transformuju-predpovede-urody-zemiakov","status":"publish","type":"post","link":"https:\/\/geopard.tech\/sk\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Dia\u013ekov\u00e9 sn\u00edmanie Zeme vegeta\u010dn\u00e9 indexy transformuj\u00fa predpovede \u00farody zemiakov"},"content":{"rendered":"<p>Zemiaky patria medzi najd\u00f4le\u017eitej\u0161ie potravin\u00e1rske plodiny na svete a sl\u00fa\u017eia ako z\u00e1kladn\u00e1 potravina pre mili\u00f3ny \u013eud\u00ed. Po prv\u00e9, znalos\u0165 toho, ako rastliny zemiakov rast\u00fa, a schopnos\u0165 predpoveda\u0165 ich \u00farodu pom\u00e1ha po\u013enohospod\u00e1rom efekt\u00edvnej\u0161ie riadi\u0165 zavla\u017eovanie, hnojenie a ochranu proti \u0161kodcom.<\/p>\n<p>Po druh\u00e9, spracovatelia potrav\u00edn a skladovacie zariadenia m\u00f4\u017eu lep\u0161ie pl\u00e1nova\u0165 logistiku a pracovn\u00fa silu, ke\u010f maj\u00fa spo\u013eahliv\u00e9 odhady v\u00fdnosov. Tradi\u010dn\u00e9 met\u00f3dy \u2013 ako napr\u00edklad fyzick\u00e1 prech\u00e1dzka po poliach a ru\u010dn\u00e9 meranie rastl\u00edn \u2013 s\u00fa v\u0161ak \u010dasovo n\u00e1ro\u010dn\u00e9 a n\u00e1chyln\u00e9 na \u013eudsk\u00e9 chyby.<\/p>\n<p>Vedci sa preto obr\u00e1tili na dia\u013ekov\u00fd prieskum Zemiakov, ktor\u00fd vyu\u017e\u00edva kamery a senzory na satelitoch, dronoch alebo vreckov\u00fdch zariadeniach na monitorovanie rastu zemiakov a r\u00fdchlej\u0161ie a presnej\u0161ie predpovedanie \u00farody.<\/p>\n<h2>Pochopenie predpoved\u00ed \u00farody zemiakov<\/h2>\n<p>Za posledn\u00e9 dve desa\u0165ro\u010dia v\u00fdrazne vzr\u00e1stol z\u00e1ujem o aplik\u00e1ciu dia\u013ekov\u00e9ho prieskumu Zeme vo v\u00fdskume zemiakov. Systematick\u00fd preh\u013ead v skuto\u010dnosti identifikoval 79 \u0161t\u00fadi\u00ed publikovan\u00fdch v rokoch 2000 a\u017e 2022 na t\u00fato t\u00e9mu zo 482 p\u00f4vodne presk\u00faman\u00fdch \u010dl\u00e1nkov.<\/p>\n<p>Aby sa zabezpe\u010dila transparentnos\u0165 a reprodukovate\u013enos\u0165, autori sa riadili stanoven\u00fdmi pokynmi (Kitchenham &amp; Charters 2007; PRISMA framework) a preh\u013ead\u00e1vali osem hlavn\u00fdch datab\u00e1z \u2013 Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis a SpringerLink \u2013 s pou\u017eit\u00edm v\u00fdrazov ako \u201cpredikcia v\u00fdnosu zemiakov\u201d A \u201cdia\u013ekov\u00fd prieskum Zemiakov\u201d.\u201d<\/p>\n<p>V d\u00f4sledku toho bol zahrnut\u00fd iba origin\u00e1lny v\u00fdskum v angli\u010dtine, ktor\u00fd vyu\u017e\u00edval \u00fadaje dia\u013ekov\u00e9ho prieskumu Zeme na monitorovanie rastu alebo odhad v\u00fdnosu. Okrem toho boli \u00fadaje z ka\u017edej vybranej pr\u00e1ce extrahovan\u00e9 pod\u013ea \u0161tyroch k\u013e\u00fa\u010dov\u00fdch ot\u00e1zok:<\/p>\n<ul>\n<li>Ktor\u00e1 sn\u00edmacia platforma bola pou\u017eit\u00e1 (satelit, UAV alebo pozemn\u00e1)?<\/li>\n<li>Ktor\u00e9 vegeta\u010dn\u00e9 indexy alebo spektr\u00e1lne znaky boli hodnoten\u00e9?<\/li>\n<li>Ktor\u00e9 vlastnosti plod\u00edn boli monitorovan\u00e9 (biomasa, listov\u00e1 plocha, chlorofyl, dus\u00edk)?<\/li>\n<li>Ako presne by sa dal predpoveda\u0165 kone\u010dn\u00fd v\u00fdnos h\u013e\u00faz (koeficient determin\u00e1cie, R\u00b2)?<\/li>\n<\/ul>\n<p>Tieto ot\u00e1zky pomohli recenzentom zmapova\u0165 s\u00fa\u010dasn\u00fd stav techniky a identifikova\u0165 medzery, na ktor\u00e9 by sa mohol zamera\u0165 bud\u00faci v\u00fdskum.<\/p>\n<h2>Platformy dia\u013ekov\u00e9ho prieskumu Zeme a vegeta\u010dn\u00e9 indexy<\/h2>\n<p>V\u00fdskumn\u00edci pou\u017eili tri hlavn\u00e9 typy platforiem dia\u013ekov\u00e9ho prieskumu Zeme, pri\u010dom ka\u017ed\u00e1 m\u00e1 svoje vlastn\u00e9 v\u00fdhody a obmedzenia. Po prv\u00e9, optick\u00e9 satelity ako Sentinel-2 (priestorov\u00e9 rozl\u00ed\u0161enie 10 m, 5-d\u0148ov\u00e1 opakovan\u00e1 n\u00e1v\u0161teva) a Landsat 5\u20138 (30 m, 16-d\u0148ov\u00e1 opakovan\u00e1 n\u00e1v\u0161teva) pon\u00fakaj\u00fa \u0161irok\u00e9 pokrytie a \u010dasto bezplatn\u00fd pr\u00edstup k \u00fadajom.<\/p>\n<p>Po druh\u00e9, satelity ako MODIS\/TERRA\/Aqua (250 \u2013 1 000 m, denn\u00e9 a\u017e dvojd\u0148ov\u00e9 op\u00e4tovn\u00e9 n\u00e1v\u0161tevy) a komer\u010dn\u00e9 syst\u00e9my ako PlanetScope (3 m, denne, n\u00e1klady pribli\u017ene 1 TP4T218 na 100 km\u00b2) umo\u017e\u0148uj\u00fa \u010dastej\u0161ie monitorovanie alebo monitorovanie s vy\u0161\u0161\u00edm rozl\u00ed\u0161en\u00edm, hoci n\u00e1klady m\u00f4\u017eu by\u0165 faktorom.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/sk\/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=\"Platformy dia\u013ekov\u00e9ho prieskumu Zeme a vegeta\u010dn\u00e9 indexy\" 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>Po tretie, bezpilotn\u00e9 lietadl\u00e1 (UAV) s multispektr\u00e1lnymi alebo hyperspektr\u00e1lnymi kamerami poskytuj\u00fa ve\u013emi vysok\u00e9 rozl\u00ed\u0161enie (a\u017e nieko\u013eko centimetrov na pixel) a mo\u017eno ich prev\u00e1dzkova\u0165 na po\u017eiadanie, ale pokr\u00fdvaj\u00fa men\u0161ie oblasti a vy\u017eaduj\u00fa si viac logistiky.<\/p>\n<p>Nakoniec, pozemn\u00e9 senzory \u2013 ako s\u00fa ru\u010dn\u00e9 NDVI metre a SPAD chlorofyl metre \u2013 poskytuj\u00fa bodov\u00e9 merania, ktor\u00e9 s\u00fa vysoko presn\u00e9, hoci s\u00fa \u010dasovo n\u00e1ro\u010dn\u00e9 pri pou\u017eit\u00ed na ve\u013ek\u00fdch poliach.<\/p>\n<p>Vegeta\u010dn\u00e9 indexy (VI) premie\u0148aj\u00fa hodnoty odrazivosti na zmyslupln\u00e9 odhady vlastnost\u00ed rastl\u00edn. Medzi najbe\u017enej\u0161ie indexy v \u0161t\u00fadi\u00e1ch zemiakov patria:<\/p>\n<ul>\n<li>NDVI (Normalizovan\u00fd rozdielov\u00fd index veget\u00e1cie): (NIR \u2013 \u010derven\u00e1) \/ (NIR + \u010derven\u00e1)<\/li>\n<li>GNDVI (zelen\u00e1 NDVI): (NIR \u2013 zelen\u00e1) \/ (NIR + zelen\u00e1)<\/li>\n<li>NDRE (Normalizovan\u00fd rozdiel \u010derven\u00e9ho okraja): (NIR \u2013 \u010derven\u00fd okraj) \/ (NIR + \u010derven\u00fd okraj)<\/li>\n<li>OSAVI (Optimalizovan\u00fd index veget\u00e1cie upraven\u00fd o p\u00f4du): 1,16 \u00d7 (NIR \u2013 \u010derven\u00e1) \/ (NIR + \u010derven\u00e1 + 0,16)<\/li>\n<li>EVI (Enhanced Vegetation Index), CIred\u2011edge, CIgreen a \u010fal\u0161ie. .<\/li>\n<\/ul>\n<p>Tieto indexy sa vyberaj\u00fa na z\u00e1klade ich citlivosti na pokryvnos\u0165 koruny, obsah chlorofylu a p\u00f4dne pozadie. V d\u00f4sledku toho sl\u00fa\u017eia ako z\u00e1klad pre odhad zdravia rastl\u00edn a predpovedanie v\u00fdnosu.<\/p>\n<h2>Monitorovanie rastu zemiakov a predpovedanie v\u00fdnosu<\/h2>\n<p>Prostredn\u00edctvom dia\u013ekov\u00e9ho prieskumu Zeme v\u00fdskumn\u00edci monitoruj\u00fa k\u013e\u00fa\u010dov\u00e9 vlastnosti \u00farody zemiakov \u2013 nadzemn\u00fa biomasu (AGB), index listovej plochy (LAI), obsah chlorofylu v korune (CCC) a stav dus\u00edka v listoch \u2013 a potom ich porovn\u00e1vaj\u00fa s kone\u010dn\u00fdm v\u00fdnosom h\u013e\u00faz.<\/p>\n<p>Po prv\u00e9, odhad AGB pomocou samotn\u00fdch VI m\u00f4\u017ee by\u0165 n\u00e1ro\u010dn\u00fd, ke\u010f je porast koruny hust\u00fd, preto\u017ee mnoh\u00e9 indexy sa nas\u00fdtia; preto kombin\u00e1cia VI s v\u00fd\u0161kou rastl\u00edn alebo text\u00farnymi znakmi v modeloch strojov\u00e9ho u\u010denia \u010dasto zvy\u0161uje presnos\u0165.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/sk\/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=\"Monitorovanie rastu zemiakov a predpovedanie v\u00fdnosu\" 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>Po druh\u00e9, hodnotenie LAI \u2013 celkovej plochy listov na jednu stranu zeme \u2013 dosiahlo hodnoty R\u00b2 a\u017e 0,84 s pou\u017eit\u00edm \u010dasov\u00fdch radov \u00fadajov z hyperspektr\u00e1lnych senzorov UAV aj satelitn\u00fdch multispektr\u00e1lnych senzorov.<\/p>\n<p>Po tretie, odhady CCC, odvoden\u00e9 z indexov ako CIred\u2011edge, CIgreen, TCARI\/OSAVI a TCARI + OSAVI, dosiahli po\u010das vegetat\u00edvneho \u0161t\u00e1dia hodnotu R\u00b2 \u2248 0,85, \u010do nazna\u010duje siln\u00fa korel\u00e1ciu s laborat\u00f3rne nameran\u00fdm chlorofylom.<\/p>\n<p>Nakoniec, stav dus\u00edka v listoch, ktor\u00fd je nevyhnutn\u00fd pre zdrav\u00fd rast, bol predpovedan\u00fd s R\u00b2 v rozmedz\u00ed od 0,52 do 0,95 pri pou\u017eit\u00ed pozemn\u00fdch senzorov plus regresn\u00fdch alebo n\u00e1hodn\u00fdch lesn\u00fdch modelov.<\/p>\n<p>Pokia\u013e ide o predikciu v\u00fdnosu h\u013e\u00faz, vynikaj\u00fa dva hlavn\u00e9 modelovacie pr\u00edstupy:<\/p>\n<p>Empirick\u00e9 regresn\u00e9 modely: V tomto pr\u00edpade sa jeden VI \u2013 naj\u010dastej\u0161ie NDVI, GNDVI alebo NDRE \u2013 prisp\u00f4sobuje skuto\u010dn\u00fdm \u00fadajom o v\u00fdnose. Uv\u00e1dzan\u00e9 hodnoty R\u00b2 pre NDVI oproti v\u00fdnosu sa pohybuj\u00fa od 0,23 do 0,84 (medi\u00e1n \u2248 0,67), zatia\u013e \u010do korel\u00e1cie NDRE a v\u00fdnosu sa pohybuj\u00fa od 0,12 do 0,85 (medi\u00e1n \u2248 0,61).<\/p>\n<p>Modely strojov\u00e9ho u\u010denia: Patria sem modely n\u00e1hodn\u00e9ho lesa, podporn\u00e9 vektorov\u00e9 stroje a neur\u00f3nov\u00e9 siete, ktor\u00e9 kombinuj\u00fa viacero VI, spektr\u00e1lne p\u00e1sma a nespektr\u00e1lne faktory, ako je po\u010dasie, p\u00f4da a mana\u017ement. Tak\u00e9to modely v niektor\u00fdch \u0161t\u00fadi\u00e1ch zv\u00fd\u0161ili R\u00b2 a\u017e na 0,93.<\/p>\n<p>Okrem toho na\u010dasovanie zberu \u00fadajov v\u00fdrazne ovplyv\u0148uje presnos\u0165 predikcie. Vo viacer\u00fdch \u0161t\u00fadi\u00e1ch merania VI vykonan\u00e9 36 \u2013 55 dn\u00ed po v\u00fdsadbe (DAP) priniesli najvy\u0161\u0161ie korel\u00e1cie s kone\u010dn\u00fdm v\u00fdnosom h\u013e\u00faz.<\/p>\n<p>T\u00e1to f\u00e1za sa zhoduje s maxim\u00e1lnym pokryt\u00edm p\u00f4dy a za\u010diatkom tvorby h\u013e\u00faz, v\u010faka \u010domu je \u0161trukt\u00fara rastliny najv\u00fdznamnej\u0161\u00edm ukazovate\u013eom kone\u010dnej \u00farody. Niektor\u00e9 z k\u013e\u00fa\u010dov\u00fdch zisten\u00fdch \u0161tatist\u00edk:<\/p>\n<ul>\n<li>Krit\u00e9ri\u00e1 hodnotenia splnilo 79 \u0161t\u00fadi\u00ed (2000 \u2013 2022) zo 482 identifikovan\u00fdch.<\/li>\n<li>Oblasti zamerania: predikcia v\u00fdnosu (37 %), stav dus\u00edka v listoch (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>Najpou\u017e\u00edvanej\u0161ie satelitn\u00e9 platformy: Sentinel\u20112, Landsat, MODIS; komer\u010dn\u00e9: PlanetScope.<\/li>\n<li>Rozsahy R\u00b2: NDVI \u2013 v\u00fd\u0165a\u017eok (0,23 \u2013 0,84), NDRE \u2013 v\u00fd\u0165a\u017eok (0,12 \u2013 0,85), GNDVI \u2013 v\u00fd\u0165a\u017eok (0,26 \u2013 0,75).<\/li>\n<\/ul>\n<h2>Odpor\u00fa\u010dania pre predikciu \u00farody zemiakov<\/h2>\n<p>Na z\u00e1klade t\u00fdchto zisten\u00ed by si odborn\u00edci mali najprv vybra\u0165 vhodn\u00fa platformu pre svoje ciele. Pre region\u00e1lne predpovede v\u00fdnosov poskytuj\u00fa bezplatn\u00e9 d\u00e1ta Sentinel-2 spo\u013eahliv\u00e9 pokrytie s rozl\u00ed\u0161en\u00edm 10 m a 5-d\u0148ov\u00fdm harmonogramom opakovan\u00fdch n\u00e1v\u0161tev.<\/p>\n<p>Na spresnenie lok\u00e1lnych odhadov s\u00fa lety bezpilotn\u00fdch lietadiel (UAV) napl\u00e1novan\u00e9 pribli\u017ene 36 \u2013 55 dn\u00ed po v\u00fdsadbe zachyt\u00e1vaj\u00face kritick\u00fa dynamiku porastu a zlep\u0161uj\u00fa kalibr\u00e1ciu satelitn\u00fdch modelov. Pozemn\u00e9 senzory sa najlep\u0161ie pou\u017e\u00edvaj\u00fa na nam\u00e1tkov\u00e9 kontroly a kalibr\u00e1ciu dia\u013ekov\u00fdch pozorovan\u00ed, najm\u00e4 pri kombin\u00e1cii spektr\u00e1lnych \u00fadajov s ter\u00e9nnymi meraniami.<\/p>\n<p>Pokia\u013e ide o vegeta\u010dn\u00e9 indexy, odborn\u00edci by mali pri predpovedan\u00ed kone\u010dn\u00e9ho v\u00fdnosu uprednostni\u0165 NDVI, NDRE a CI <sub>red-edge<\/sub> , preto\u017ee tieto indexy konzistentne vykazuj\u00fa siln\u00e9 korel\u00e1cie.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/sk\/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=\"Odpor\u00fa\u010dania pre predikciu \u00farody zemiakov\" 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 odhadovan\u00ed obsahu chlorofylu a dus\u00edka prin\u00e1\u0161a najpresnej\u0161ie v\u00fdsledky kombin\u00e1cia indexov \u010derven\u00e9ho okraja s indexmi vidite\u013enosti (VI) upraven\u00fdmi o p\u00f4du \u2013 ako napr\u00edklad TCARI\/OSAVI. Pri odhade biomasy presnos\u0165 \u010falej zvy\u0161uje integr\u00e1cia VI s v\u00fd\u0161kou alebo text\u00farou rastl\u00edn v r\u00e1mci syst\u00e9mov strojov\u00e9ho u\u010denia.<\/p>\n<p>Pokia\u013e ide o modelovanie, jednoduch\u00e9 line\u00e1rne alebo neline\u00e1rne regresie s pou\u017eit\u00edm jedin\u00e9ho indexu s\u00fa \u00fa\u010dinn\u00e9, ke\u010f s\u00fa \u00fadaje o ter\u00e9ne obmedzen\u00e9. Ak je v\u0161ak k dispoz\u00edcii viacero indexov a pomocn\u00fdch \u00fadajov (po\u010dasie, p\u00f4da, hospod\u00e1renie), met\u00f3dy strojov\u00e9ho u\u010denia, ako napr\u00edklad n\u00e1hodn\u00fd les alebo neur\u00f3nov\u00e9 siete, pon\u00fakaj\u00fa vynikaj\u00faci v\u00fdkon. D\u00f4le\u017eit\u00e9 je, \u017ee na\u010dasovanie sn\u00edmok okolo 36 \u2013 55 dn\u00ed po v\u00fdsadbe je k\u013e\u00fa\u010dov\u00e9, preto\u017ee toto okno konzistentne poskytuje najvy\u0161\u0161iu presnos\u0165 predikcie.<\/p>\n<h2>Z\u00e1ver<\/h2>\n<p>Z\u00e1verom mo\u017eno poveda\u0165, \u017ee dia\u013ekov\u00fd prieskum Zeme pon\u00faka r\u00fdchlu, flexibiln\u00fa a presn\u00fa sadu n\u00e1strojov na monitorovanie rastu zemiakov a predpovedanie \u00farody h\u013e\u00faz. V\u00fdberom vhodnej platformy, v\u00fdberom najinformat\u00edvnej\u0161\u00edch vegeta\u010dn\u00fdch indexov, na\u010dasovan\u00edm zberu \u00fadajov okolo 36 \u2013 55 DAP a pou\u017eit\u00edm vhodn\u00fdch modelovac\u00edch techn\u00edk m\u00f4\u017eu v\u00fdskumn\u00edci a odborn\u00edci z praxe v\u00fdrazne zlep\u0161i\u0165 progn\u00f3zy \u00farody.<\/p>\n<p>Tento pr\u00edstup nielen \u0161etr\u00ed \u010das, ale tie\u017e podporuje inteligentnej\u0161ie mana\u017e\u00e9rske rozhodnutia, \u010do v kone\u010dnom d\u00f4sledku prospieva po\u013enohospod\u00e1rom, agron\u00f3mom a cel\u00e9mu dod\u00e1vate\u013esk\u00e9mu re\u0165azcu zemiakov.<\/p>\n<p><strong>Referencia<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>a kol.<\/i>\u00a0Systematick\u00fd preh\u013ead vegeta\u010dn\u00fdch indexov pre monitorovanie rastu zemiakov a predikciu \u00farody h\u013e\u00faz z dia\u013ekov\u00e9ho prieskumu Zeme.\u00a0<i>Zemiakov\u00e9 v\u00fdskumy.<\/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>Zemiaky patria medzi najd\u00f4le\u017eitej\u0161ie potravin\u00e1rske plodiny na svete a sl\u00fa\u017eia ako z\u00e1kladn\u00e1 potravina pre mili\u00f3ny \u013eud\u00ed. Po prv\u00e9, vedie\u0165, ako rastliny zemiakov rast\u00fa\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 v27.4) - 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\/sk\/blog\/indexy-vegetacie-dialkoveho-prieskumu-zeme-transformuju-predpovede-urody-zemiakov\/\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\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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