{"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":"nuotolinio-stebejimo-augmenijos-indeksai-keicia-bulviu-derliaus-prognozavima","status":"publish","type":"post","link":"https:\/\/geopard.tech\/lt\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Biometriniai augal\u0173 indeksai padeda prognozuoti bulvi\u0173 derli\u0173"},"content":{"rendered":"<p>Bulv\u0117s yra vienas svarbiausi\u0173 maistini\u0173 augal\u0173 pasaulyje, tarnaujantis milijonams \u017emoni\u0173. Pirma, \u017einojimas, kaip auga bulv\u0117s, ir geb\u0117jimas numatyti j\u0173 derli\u0173 padeda \u016bkininkams veiksmingiau valdyti dr\u0117kinim\u0105, tr\u0119\u0161im\u0105 ir kenk\u0117j\u0173 kontrol\u0119.<\/p>\n<p>Antra, maisto perdirb\u0117jai ir sand\u0117liavimo \u012fmon\u0117s gali geriau planuoti logistik\u0105 ir darbo j\u0117g\u0105, kai turi patikimus derliaus \u012fvertinimus. Ta\u010diau tradiciniai metodai, pavyzd\u017eiui, fizi\u0161kai vaik\u0161\u010diojimas po laukus ir augal\u0173 matavimas rankomis, u\u017eima daug laiko ir yra link\u0119 \u012f \u017emogi\u0161k\u0105sias klaidas.<\/p>\n<p>Tod\u0117l mokslininkai kreip\u0117si \u012f nuotolin\u012f steb\u0117jim\u0105, kai naudojamos palydovuose, dronuose ar ne\u0161iojamuose \u012frenginiuose esan\u010dios kameros ir jutikliai, siekiant grei\u010diau ir tiksliau steb\u0117ti bulvi\u0173 augim\u0105 ir prognozuoti derli\u0173.<\/p>\n<h2>Bulvi\u0173 derliaus prognozi\u0173 supratimas<\/h2>\n<p>Per pastaruosius du de\u0161imtme\u010dius susidom\u0117jimas nuotolinio steb\u0117jimo taikymu bulvi\u0173 tyrimams labai i\u0161augo. I\u0161 ties\u0173, sistemin\u0117je ap\u017evalgoje i\u0161 482 i\u0161 prad\u017ei\u0173 atrinkt\u0173 straipsni\u0173 buvo nustatyti 79 tyrimai \u0161ia tema, paskelbti 2000\u20132022 m.<\/p>\n<p>Siekdami u\u017etikrinti skaidrum\u0105 ir atkuriamum\u0105, autoriai vadovavosi nustatytomis gair\u0117mis (\u201cKitchenham &amp; Charters\u201d, 2007 m.; \u201cPRISMA\u201d sistema), ie\u0161kodami a\u0161tuoniose pagrindin\u0117se duomen\u0173 baz\u0117se \u2013 \u201eGoogle Scholar\u201c, \u201eScienceDirect\u201c, \u201eScopus\u201c, \u201eWeb of Science\u201c, \u201eIEEE Xplore\u201c, MDPI, \u201eTaylor &amp; Francis\u201c ir \u201eSpringerLink\u201c \u2013 vartodami tokius terminus kaip \u201ebulvi\u0173 derliaus prognozavimas\u201c IR \u201enuotolinis steb\u0117jimas\u201c.\u201d<\/p>\n<p>Tod\u0117l buvo \u012ftraukti tik original\u016bs tyrimai angl\u0173 kalba, kuriuose augimo steb\u0117senai ar derliaus \u012fvertinimui buvo naudojami nuotolinio steb\u0117jimo duomenys. Be to, kiekvieno pasirinkto straipsnio duomenys buvo atrinkti pagal keturis pagrindinius klausimus:<\/p>\n<ul>\n<li>Kokia steb\u0117jimo platforma buvo naudojama (palydovin\u0117, bepilot\u0117 orlaivi\u0173 ar ant\u017eemin\u0117)?<\/li>\n<li>Kokie augmenijos indeksai ar spektriniai po\u017eymiai buvo \u012fvertinti?<\/li>\n<li>Kurie pas\u0117li\u0173 po\u017eymiai buvo stebimi (biomas\u0117, lap\u0173 plotas, chlorofilas, azotas)?<\/li>\n<li>Kaip tiksliai galima b\u016bt\u0173 numatyti galutin\u012f gumb\u0173 derli\u0173 (determinacijos koeficientas, R\u00b2)?<\/li>\n<\/ul>\n<p>\u0160ie klausimai pad\u0117jo recenzentams nustatyti esam\u0105 pad\u0117t\u012f ir spragas, \u012f kurias gal\u0117t\u0173 b\u016bti sutelkti b\u016bsimi tyrimai.<\/p>\n<h2>Nuotolinio steb\u0117jimo platformos ir augmenijos indeksai<\/h2>\n<p>Tyr\u0117jai naudojo tris pagrindinius nuotolinio steb\u0117jimo platform\u0173 tipus, kuri\u0173 kiekvienas turi sav\u0173 privalum\u0173 ir tr\u016bkum\u0173. Pirma, optiniai palydovai, tokie kaip \u201eSentinel-2\u201c (10 m erdvin\u0117 skiriamoji geba, 5 dien\u0173 pakartotinis apsilankymas) ir \u201eLandsat 5\u20138\u201c (30 m, 16 dien\u0173 pakartotinis apsilankymas), si\u016blo pla\u010di\u0105 apr\u0117pt\u012f ir da\u017enai nemokam\u0105 prieig\u0105 prie duomen\u0173.<\/p>\n<p>Antra, tokie palydovai kaip MODIS\/TERRA\/Aqua (250\u20131000 m, kasdien arba kas 2 dienas) ir komercin\u0117s sistemos, tokios kaip \u201ePlanetScope\u201c (3 m, kasdien, kainuoja apie $218 \/ 100 km\u00b2), leid\u017eia atlikti da\u017enesn\u012f arba didesn\u0117s skiriamosios gebos steb\u0117jim\u0105, nors i\u0161laidos gali b\u016bti svarbus veiksnys.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/lt\/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=\"Nuotolinio steb\u0117jimo platformos ir augmenijos indeksai\" 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\u010dia, bepilo\u010diai orlaiviai (UAV), turintys multispektrines arba hiperspektrines kameras, pasi\u017eymi labai didele skiriam\u0105ja geba (iki keli\u0173 centimetr\u0173 pikseliui) ir gali b\u016bti naudojami pagal poreik\u012f, ta\u010diau jie apima ma\u017eesnes teritorijas ir reikalauja daugiau logistikos.<\/p>\n<p>Galiausiai, ant\u017eeminiai jutikliai, tokie kaip ne\u0161iojamieji NDVI matuokliai ir SPAD chlorofilo matuokliai, atlieka labai tikslius ta\u0161kinius matavimus, nors, naudojant dideliuose laukuose, jie u\u017eima daug laiko.<\/p>\n<p>Vegetacijos indeksai (VI) neapdorotas atspind\u017eio vertes paver\u010dia reik\u0161mingais augalo savybi\u0173 \u012fver\u010diais. Da\u017eniausiai bulvi\u0173 tyrimuose naudojami indeksai yra \u0161ie:<\/p>\n<ul>\n<li>NDVI (normalizuotas augmenijos skirtumo indeksas): (NIR \u2013 raudonas) \/ (NIR + raudonas)<\/li>\n<li>GNDVI (\u017ealias NDVI): (NIR \u2013 \u017ealias) \/ (NIR + \u017ealias)<\/li>\n<li>NDRE (normalizuotas raudonojo kra\u0161to skirtumas): (NIR \u2013 raudonas kra\u0161tas) \/ (NIR + raudonas kra\u0161tas)<\/li>\n<li>OSAVI (Optimizuotas dirvo\u017eemio pakoreguotas augmenijos indeksas): 1,16 \u00d7 (NIR \u2013 raudonas) \/ (NIR + raudonas + 0,16)<\/li>\n<li>EVI (patobulintas augmenijos indeksas), \u201eCIred-edge\u201c, \u201eCIgreen\u201c ir kt. .<\/li>\n<\/ul>\n<p>\u0160ie indeksai parenkami atsi\u017evelgiant \u012f j\u0173 jautrum\u0105 med\u017ei\u0173 lajos dangai, chlorofilo kiekiui ir dirvo\u017eemio fonui. Tod\u0117l jie yra pagrindas augal\u0173 sveikatai \u012fvertinti ir derliui prognozuoti.<\/p>\n<h2>Bulvi\u0173 augimo steb\u0117jimas ir derliaus prognozavimas<\/h2>\n<p>Naudodami nuotolin\u012f steb\u0117jim\u0105, tyr\u0117jai stebi pagrindines bulvi\u0173 derliaus savybes \u2013 ant\u017eemin\u0119 biomas\u0119 (AGB), lap\u0173 ploto indeks\u0105 (LAI), lajos chlorofilo kiek\u012f (CCC) ir lap\u0173 azoto b\u016bsen\u0105 \u2013 ir susieja jas su galutiniu gumb\u0173 derliumi.<\/p>\n<p>Pirma, AGB \u012fvertinimas naudojant vien tik VI gali b\u016bti sud\u0117tingas, kai laj\u0173 danga yra tanki, nes daugelis indeks\u0173 yra persotinti; tod\u0117l VI derinimas su augal\u0173 auk\u0161\u010diu ar tekst\u016bros ypatyb\u0117mis ma\u0161ininio mokymosi modeliuose da\u017enai pagerina tikslum\u0105.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/lt\/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=\"Bulvi\u0173 augimo steb\u0117jimas ir derliaus prognozavimas\" 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>Antra, vertinant LAI \u2013 bendr\u0105 vienpusi\u0173 lap\u0173 plot\u0105, tenkant\u012f \u017eem\u0117s plotui, \u2013 naudojant laiko eilu\u010di\u0173 duomenis i\u0161 bepilo\u010di\u0173 orlaivi\u0173 hiperspektrini\u0173 ir palydovini\u0173 multispektrini\u0173 jutikli\u0173, buvo pasiektos R\u00b2 vert\u0117s iki 0,84.<\/p>\n<p>Tre\u010dia, CCC \u012fver\u010diai, gauti i\u0161 toki\u0173 indeks\u0173 kaip \u201eCIred-edge\u201c, \u201eCIgreen\u201c, TCARI\/OSAVI ir TCARI + OSAVI, vegetacin\u0117s stadijos metu pasiek\u0117 R\u00b2 \u2248 0,85, o tai rodo stipri\u0105 koreliacij\u0105 su laboratorijoje i\u0161matuotu chlorofilo kiekiu.<\/p>\n<p>Galiausiai, naudojant ant\u017eeminius jutiklius ir regresinius arba atsitiktini\u0173 mi\u0161k\u0173 modelius, buvo prognozuojamas lap\u0173 azoto kiekis, kuris yra gyvybi\u0161kai svarbus sveikam augimui, o R\u00b2 svyravo nuo 0,52 iki 0,95.<\/p>\n<p>Kalbant apie gumb\u0173 derliaus prognozavim\u0105, i\u0161siskiria du pagrindiniai modeliavimo metodai:<\/p>\n<p>Empiriniai regresiniai modeliai: \u010dia vienas VI \u2013 da\u017eniausiai NDVI, GNDVI arba NDRE \u2013 yra pritaikomas prie i\u0161eigos duomen\u0173, kurie atitinka tik\u0117tin\u0105 i\u0161eig\u0105. Pateiktos NDVI ir i\u0161eigos R\u00b2 vert\u0117s svyruoja nuo 0,23 iki 0,84 (mediana \u2248 0,67), o NDRE ir i\u0161eigos koreliacijos svyruoja nuo 0,12 iki 0,85 (mediana \u2248 0,61).<\/p>\n<p>Ma\u0161ininio mokymosi modeliai: tai atsitiktini\u0173 mi\u0161k\u0173, atramini\u0173 vektori\u0173 ma\u0161inos ir neuroniniai tinklai, kurie sujungia kelis VI, spektrinius diapazonus ir nespektrinius veiksnius, tokius kaip orai, dirvo\u017eemis ir valdymas. Kai kuriuose tyrimuose tokie modeliai padidino R\u00b2 iki 0,93.<\/p>\n<p>Be to, duomen\u0173 rinkimo laikas labai veikia prognozavimo tikslum\u0105. Atliekant kelis tyrimus, VI matavimai, atlikti pra\u0117jus 36\u201355 dienoms po pasodinimo (DAP), parod\u0117 did\u017eiausi\u0105 koreliacij\u0105 su galutiniu gumb\u0173 derliumi.<\/p>\n<p>\u0160is etapas sutampa su maksimaliu \u017eem\u0117s padengimu ir gumb\u0173 augimo prad\u017eia, tod\u0117l augalo strukt\u016bra labiausiai rodo galutin\u012f derli\u0173. Kai kurie pagrindiniai nustatyti statistiniai duomenys:<\/p>\n<ul>\n<li>79 tyrimai (2000\u20132022 m.) i\u0161 482 nustatyt\u0173 atitiko per\u017ei\u016bros kriterijus.<\/li>\n<li>Fokusin\u0117s sritys: derliaus prognozavimas (37 %), lap\u0173 azoto b\u016bsena (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>Da\u017eniausiai naudojamos palydov\u0173 platformos: \u201eSentinel-2\u201c, \u201eLandsat\u201c, MODIS; komercin\u0117s: \u201ePlanetScope\u201c.<\/li>\n<li>R\u00b2 diapazonai: NDVI \u2013 i\u0161eiga (0,23\u20130,84), NDRE \u2013 i\u0161eiga (0,12\u20130,85), GNDVI \u2013 i\u0161eiga (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Bulvi\u0173 derliaus prognozavimo rekomendacijos<\/h2>\n<p>Remdamiesi \u0161iais rezultatais, praktikai pirmiausia tur\u0117t\u0173 pasirinkti tinkam\u0105 platform\u0105 savo tikslams pasiekti. Regionin\u0117ms derliaus prognoz\u0117ms nemokami \u201eSentinel-2\u201c duomenys u\u017etikrina patikim\u0105 apr\u0117pt\u012f su 10 m skiriam\u0105ja geba ir 5 dien\u0173 pakartotinio apsilankymo grafiku.<\/p>\n<p>Siekiant patikslinti vietinius \u012fvertinimus, bepilo\u010di\u0173 orlaivi\u0173 skryd\u017eiai, suplanuoti ma\u017edaug 36\u201355 dienas po pasodinimo, fiksuoja svarbi\u0105 debesies dinamik\u0105 ir pagerina palydovini\u0173 modeli\u0173 kalibravim\u0105. Ant\u017eeminiai jutikliai geriausiai naudojami atsitiktiniams patikrinimams ir nuotolini\u0173 steb\u0117jim\u0173 kalibravimui, ypa\u010d derinant spektrinius duomenis su lauko matavimais.<\/p>\n<p>Kalbant apie augmenijos indeksus, specialistai, prognozuodami galutin\u012f derli\u0173, tur\u0117t\u0173 teikti pirmenyb\u0119 NDVI, NDRE ir CI <sub>raudonojo kra\u0161to rodikliams<\/sub> , nes \u0161ie rodikliai nuolat rodo stipri\u0105 koreliacij\u0105.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/lt\/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=\"Bulvi\u0173 derliaus prognozavimo rekomendacijos\" 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>Vertinant chlorofilo ir azoto kiek\u012f, tiksliausius rezultatus galima gauti derinant raudonojo kra\u0161to indeksus su dirvo\u017eemio at\u017evilgiu pakoreguotais VI, pvz., TCARI\/OSAVI. Biomas\u0117s vertinimo tikslum\u0105 dar labiau padidina VI integravimas su augalo auk\u0161\u010diu ar tekst\u016bros ypatyb\u0117mis ma\u0161ininio mokymosi sistemose.<\/p>\n<p>Kalbant apie modeliavim\u0105, paprastos tiesin\u0117s arba netiesin\u0117s regresijos, naudojant vien\u0105 indeks\u0105, yra veiksmingos, kai yra riboti faktiniai duomenys. Ta\u010diau, kai yra keli indeksai ir pagalbiniai duomenys (orai, dirvo\u017eemis, valdymas), geresnius rezultatus u\u017etikrina ma\u0161ininio mokymosi metodai, tokie kaip atsitiktini\u0173 mi\u0161k\u0173 arba neuroniniai tinklai. Svarbu tai, kad labai svarbu laiku matuoti vaizdus ma\u017edaug 36\u201355 dienas po pasodinimo, nes \u0161is laikotarpis nuolat u\u017etikrina did\u017eiausi\u0105 prognozavimo tikslum\u0105.<\/p>\n<h2>I\u0161vada<\/h2>\n<p>Apibendrinant galima teigti, kad nuotolinis steb\u0117jimas si\u016blo greit\u0105, lanks\u010di\u0105 ir tiksli\u0105 bulvi\u0173 augimo steb\u0117jimo ir gumb\u0173 derliaus prognozavimo priemon\u0119. Pasirinkdami tinkam\u0105 platform\u0105, informatyviausius vegetacijos rodiklius, duomen\u0173 rinkimo laik\u0105 ma\u017edaug 36\u201355 DAP ir taikydami tinkamus modeliavimo metodus, tyr\u0117jai ir praktikai gali gerokai pagerinti derliaus prognozes.<\/p>\n<p>Toks po\u017ei\u016bris ne tik taupo laik\u0105, bet ir padeda priimti i\u0161manesnius valdymo sprendimus, o tai galiausiai naudinga \u016bkininkams, agronomams ir visai bulvi\u0173 tiekimo grandinei.<\/p>\n<p><strong>Nuoroda<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>ir kt.<\/i>\u00a0Sistemin\u0117 vegetacijos indeks\u0173, skirt\u0173 bulvi\u0173 augimo steb\u0117senai ir gumb\u0173 derliaus prognozavimui nuotoliniu b\u016bdu, ap\u017evalga.\u00a0<i>Bulvi\u0173 rez.<\/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>Bulv\u0117s yra vienas svarbiausi\u0173 maistini\u0173 augal\u0173 pasaulyje, tarnaujantis kaip pagrindinis produktas milijonams \u017emoni\u0173. Pirma, \u017einant, kaip auga bulv\u0117s\u2026<\/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\/lt\/tinklarastis\/nuotolinio-stebejimo-augmenijos-indeksai-keicia-bulviu-derliaus-prognozavima\/\" \/>\n<meta property=\"og:locale\" content=\"lt_LT\" \/>\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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