{"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":"talizpetes-vegetacijas-indeksi-parveido-kartupelu-razas-prognozesanu","status":"publish","type":"post","link":"https:\/\/geopard.tech\/lv\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"T\u0101lizp\u0113tes ve\u0123et\u0101cijas indeksi p\u0101rveido kartupe\u013cu ra\u017eas prognoz\u0113\u0161anu"},"content":{"rendered":"<p>Kartupe\u013ci ir viena no pasaul\u0113 svar\u012bg\u0101kaj\u0101m p\u0101rtikas kult\u016br\u0101m, kas kalpo par pamatproduktu miljoniem cilv\u0113ku. Pirmk\u0101rt, zin\u0101\u0161anas par to, k\u0101 aug kartupe\u013cu augi, un sp\u0113ja prognoz\u0113t to ra\u017eu pal\u012bdz lauksaimniekiem efekt\u012bv\u0101k p\u0101rvald\u012bt ap\u016bde\u0146o\u0161anu, m\u0113slo\u0161anu un kait\u0113k\u013cu apkaro\u0161anu.<\/p>\n<p>Otrk\u0101rt, p\u0101rtikas p\u0101rstr\u0101d\u0101t\u0101ji un uzglab\u0101\u0161anas uz\u0146\u0113mumi var lab\u0101k pl\u0101not lo\u0123istiku un darbasp\u0113ku, ja tiem ir uzticamas ra\u017eas apl\u0113ses. Tom\u0113r tradicion\u0101l\u0101s metodes, piem\u0113ram, fiziska pastaiga pa laukiem un augu m\u0113r\u012b\u0161ana ar rok\u0101m, ir laikietilp\u012bgas un pak\u013cautas cilv\u0113cisk\u0101m k\u013c\u016bd\u0101m.<\/p>\n<p>T\u0101p\u0113c zin\u0101tnieki ir piev\u0113rsu\u0161ies t\u0101lizp\u0113tei, kur\u0101 tiek izmantotas kameras un sensori uz satel\u012btiem, droniem vai rokas ier\u012bc\u0113m, lai \u0101tr\u0101k un prec\u012bz\u0101k uzraudz\u012btu kartupe\u013cu aug\u0161anu un prognoz\u0113tu ra\u017eu.<\/p>\n<h2>Kartupe\u013cu ra\u017eas progno\u017eu izpratne<\/h2>\n<p>P\u0113d\u0113jo divu desmitga\u017eu laik\u0101 interese par t\u0101lizp\u0113tes pielieto\u0161anu kartupe\u013cu p\u0113tniec\u012bb\u0101 ir iev\u0113rojami pieaugusi. Faktiski sistem\u0101tisk\u0101 p\u0101rskat\u0101 no 482 s\u0101kotn\u0113ji p\u0101rbaud\u012btiem rakstiem tika identific\u0113ti 79 p\u0113t\u012bjumi, kas public\u0113ti laik\u0101 no 2000. l\u012bdz 2022. gadam par \u0161o t\u0113mu.<\/p>\n<p>Lai nodro\u0161in\u0101tu p\u0101rredzam\u012bbu un atk\u0101rtojam\u012bbu, autori iev\u0113roja noteikt\u0101s vadl\u012bnijas (Kitchenham &amp; Charters 2007; PRISMA sist\u0113ma), mekl\u0113jot asto\u0146\u0101s liel\u0101kaj\u0101s datub\u0101z\u0113s \u2014 Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis un SpringerLink \u2014, lietojot t\u0101dus terminus k\u0101 \u201ckartupe\u013cu ra\u017eas prognoz\u0113\u0161ana\u201d UN \u201ct\u0101lizp\u0113te\u201d.\u201d<\/p>\n<p>L\u012bdz ar to tika iek\u013cauti tikai ori\u0123in\u0101li p\u0113t\u012bjumi ang\u013cu valod\u0101, kuros aug\u0161anas monitoringam vai ra\u017eas nov\u0113rt\u0113\u0161anai tika izmantoti t\u0101lizp\u0113tes dati. Turkl\u0101t dati no katra atlas\u012bt\u0101 raksta tika ieg\u016bti atbilsto\u0161i \u010detriem galvenajiem jaut\u0101jumiem:<\/p>\n<ul>\n<li>Kura sensoru platforma tika izmantota (satel\u012bts, bezpilota lidapar\u0101ts vai uz zemes b\u0101z\u0113ta)?<\/li>\n<li>Kuri ve\u0123et\u0101cijas indeksi vai spektr\u0101l\u0101s paz\u012bmes tika nov\u0113rt\u0113tas?<\/li>\n<li>Kuras kult\u016braugu \u012bpa\u0161\u012bbas tika uzraudz\u012btas (biomasa, lapu plat\u012bba, hlorofils, sl\u0101peklis)?<\/li>\n<li>Cik prec\u012bzi var\u0113tu prognoz\u0113t gal\u012bgo bumbu\u013cu ra\u017eu (noteik\u0161anas koeficients, R\u00b2)?<\/li>\n<\/ul>\n<p>\u0160ie jaut\u0101jumi pal\u012bdz\u0113ja recenzentiem noteikt pa\u0161reiz\u0113jo situ\u0101ciju un noteikt nepiln\u012bbas, uz kur\u0101m var\u0113tu koncentr\u0113ties turpm\u0101kie p\u0113t\u012bjumi.<\/p>\n<h2>T\u0101lizp\u0113tes platformas un ve\u0123et\u0101cijas indeksi<\/h2>\n<p>P\u0113tnieki ir izmantoju\u0161i tr\u012bs galvenos t\u0101lizp\u0113tes platformu veidus, katram no tiem ir savas priek\u0161roc\u012bbas un ierobe\u017eojumi. Pirmk\u0101rt, optiskie satel\u012bti, piem\u0113ram, Sentinel-2 (10 m telpisk\u0101 iz\u0161\u0137irtsp\u0113ja, 5 dienu atk\u0101rtota viz\u012bte) un Landsat 5\u20138 (30 m, 16 dienu atk\u0101rtota viz\u012bte), pied\u0101v\u0101 pla\u0161u p\u0101rkl\u0101jumu un bie\u017ei vien bezmaksas piek\u013cuvi datiem.<\/p>\n<p>Otrk\u0101rt, t\u0101di satel\u012bti k\u0101 MODIS\/TERRA\/Aqua (250\u20131000 m, dien\u0101 l\u012bdz divu dienu apmekl\u0113jumam) un komerci\u0101las sist\u0113mas, piem\u0113ram, PlanetScope (3 m, dien\u0101, izmaksas aptuveni $218 uz 100 km\u00b2), \u013cauj veikt bie\u017e\u0101ku vai augst\u0101kas iz\u0161\u0137irtsp\u0113jas uzraudz\u012bbu, lai gan izmaksas var b\u016bt faktors.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/lv\/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=\"T\u0101lizp\u0113tes platformas un ve\u0123et\u0101cijas indeksi\" 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\u0161k\u0101rt, bezpilota lidapar\u0101ti (UAV), kas p\u0101rvad\u0101 multispektr\u0101las vai hiperspektr\u0101las kameras, nodro\u0161ina \u013coti augstu iz\u0161\u0137irtsp\u0113ju (l\u012bdz da\u017eiem centimetriem uz pikseli) un tos var lidot p\u0113c piepras\u012bjuma, ta\u010du tie aptver maz\u0101kas teritorijas un prasa liel\u0101ku lo\u0123istiku.<\/p>\n<p>Visbeidzot, uz zemes b\u0101z\u0113ti sensori, piem\u0113ram, rokas NDVI m\u0113r\u012bt\u0101ji un SPAD hlorofila m\u0113r\u012bt\u0101ji, sniedz \u013coti prec\u012bzus punktveida m\u0113r\u012bjumus, lai gan tie ir laikietilp\u012bgi, ja tos izmanto lielos laukos.<\/p>\n<p>Ve\u0123et\u0101cijas indeksi (VI) p\u0101rv\u0113r\u0161 neapstr\u0101d\u0101tas atstaro\u0161anas v\u0113rt\u012bbas j\u0113gpilnos augu \u012bpa\u0161\u012bbu nov\u0113rt\u0113jumos. Kartupe\u013cu p\u0113t\u012bjumos visbie\u017e\u0101k izmantotie indeksi ir:<\/p>\n<ul>\n<li>NDVI (normaliz\u0113ts ve\u0123et\u0101cijas diferenci\u0101lais indekss): (NIR \u2013 sarkanais) \/ (NIR + sarkanais)<\/li>\n<li>GNDVI (za\u013c\u0161 NDVI): (NIR \u2013 za\u013c\u0161) \/ (NIR + za\u013c\u0161)<\/li>\n<li>NDRE (normaliz\u0113ta sarkan\u0101s malas at\u0161\u0137ir\u012bba): (NIR \u2013 sarkan\u0101 mala) \/ (NIR + sarkan\u0101 mala)<\/li>\n<li>OSAVI (Optimiz\u0113ts augsnes kori\u0123\u0113ts ve\u0123et\u0101cijas indekss): 1,16 \u00d7 (NIR \u2013 sarkanais) \/ (NIR + sarkanais + 0,16)<\/li>\n<li>EVI (Uzlabotais ve\u0123et\u0101cijas indekss), CIred-edge, CIgreen un citi. .<\/li>\n<\/ul>\n<p>\u0160ie indeksi tiek izv\u0113l\u0113ti, pamatojoties uz to jut\u012bbu pret vainaga segumu, hlorofila saturu un augsnes fonu. L\u012bdz ar to tie kalpo par pamatu augu vesel\u012bbas nov\u0113rt\u0113\u0161anai un ra\u017eas prognoz\u0113\u0161anai.<\/p>\n<h2>Kartupe\u013cu aug\u0161anas uzraudz\u012bba un ra\u017eas prognoz\u0113\u0161ana<\/h2>\n<p>Ar t\u0101lizp\u0113tes pal\u012bdz\u012bbu p\u0113tnieki uzrauga galven\u0101s kartupe\u013cu kult\u016braugu \u012bpa\u0161\u012bbas \u2014 virszemes biomasu (AGB), lapu laukuma indeksu (LAI), lapotnes hlorofila saturu (CCC) un lapu sl\u0101pek\u013ca statusu \u2014 un p\u0113c tam saista t\u0101s ar gal\u012bgo bumbu\u013cu ra\u017eu.<\/p>\n<p>Pirmk\u0101rt, AGB nov\u0113rt\u0113\u0161ana, izmantojot tikai VI, var b\u016bt sare\u017e\u0123\u012bta, ja vainagu segums ir bl\u012bvs, jo daudzi indeksi ir pies\u0101tin\u0101ti; t\u0101p\u0113c VI apvieno\u0161ana ar augu augstuma vai tekst\u016bras paz\u012bm\u0113m ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s mode\u013cos bie\u017ei vien uzlabo precizit\u0101ti.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/lv\/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=\"Kartupe\u013cu aug\u0161anas uzraudz\u012bba un ra\u017eas prognoz\u0113\u0161ana\" 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>Otrk\u0101rt, nov\u0113rt\u0113jot LAI \u2014 kop\u0113jo vienpus\u0113jo lapu laukumu uz zemes plat\u012bbu \u2014, izmantojot laikrindu datus gan no bezpilota lidapar\u0101tu hiperspektr\u0101lajiem, gan satel\u012btu multispektr\u0101lajiem sensoriem, ir sasniegtas R\u00b2 v\u0113rt\u012bbas l\u012bdz pat 0,84.<\/p>\n<p>Tre\u0161k\u0101rt, CCC apl\u0113ses, kas ieg\u016btas no t\u0101diem indeksiem k\u0101 CIred-edge, CIgreen, TCARI\/OSAVI un TCARI + OSAVI, ve\u0123etat\u012bv\u0101s stadijas laik\u0101 sasniedza R\u00b2 \u2248 0,85, kas nor\u0101da uz sp\u0113c\u012bgu korel\u0101ciju ar laboratorij\u0101 izm\u0113r\u012bto hlorofila l\u012bmeni.<\/p>\n<p>Visbeidzot, lapu sl\u0101pek\u013ca statuss, kas ir vit\u0101li svar\u012bgs vesel\u012bgai aug\u0161anai, tika prognoz\u0113ts ar R\u00b2 no 0,52 l\u012bdz 0,95, izmantojot uz zemes b\u0101z\u0113tus sensorus kop\u0101 ar regresijas vai nejau\u0161o me\u017eu mode\u013ciem.<\/p>\n<p>Run\u0101jot par bumbu\u013cu ra\u017eas prognoz\u0113\u0161anu, izce\u013cas divas galven\u0101s model\u0113\u0161anas pieejas:<\/p>\n<p>Emp\u012brisk\u0101s regresijas mode\u013ci: \u0160eit viens VI \u2014 visbie\u017e\u0101k NDVI, GNDVI vai NDRE \u2014 tiek piel\u0101gots pamatpaties\u012bbas ra\u017eas datiem. Zi\u0146ot\u0101s R\u00b2 v\u0113rt\u012bbas NDVI un ra\u017eas attiec\u012bbai sv\u0101rst\u0101s no 0,23 l\u012bdz 0,84 (medi\u0101na \u2248 0,67), savuk\u0101rt NDRE un ra\u017eas korel\u0101cijas sv\u0101rst\u0101s no 0,12 l\u012bdz 0,85 (medi\u0101na \u2248 0,61).<\/p>\n<p>Ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s mode\u013ci: tie ietver nejau\u0161o me\u017eu, atbalsta vektoru ma\u0161\u012bnas un neironu t\u012bklus, kas apvieno vair\u0101kus VI, spektr\u0101l\u0101s joslas un nespektr\u0101lus faktorus, piem\u0113ram, laikapst\u0101k\u013cus, augsni un apsaimnieko\u0161anu. \u0160\u0101di mode\u013ci da\u017eos p\u0113t\u012bjumos ir palielin\u0101ju\u0161i R\u00b2 l\u012bdz 0,93.<\/p>\n<p>Turkl\u0101t datu v\u0101k\u0161anas laiks b\u016btiski ietekm\u0113 prognoz\u0113\u0161anas precizit\u0101ti. Vair\u0101kos p\u0113t\u012bjumos VI m\u0113r\u012bjumi, kas veikti 36\u201355 dienas p\u0113c ies\u0113\u0161anas (DAP), uzr\u0101d\u012bja visaugst\u0101ko korel\u0101ciju ar gal\u012bgo bumbu\u013cu ra\u017eu.<\/p>\n<p>\u0160is posms sakr\u012bt ar maksim\u0101lo zemes seguma veido\u0161anos un bumbu\u013cu veido\u0161an\u0101s s\u0101kumu, padarot auga strukt\u016bru par visprec\u012bz\u0101ko r\u0101d\u012bt\u0101ju gal\u012bgajai ra\u017eai. Da\u017ei no galvenajiem konstat\u0113tajiem statistikas datiem:<\/p>\n<ul>\n<li>79 p\u0113t\u012bjumi (2000.\u20132022.\u00a0g.) no 482 identific\u0113tajiem atbilda p\u0101rskat\u012b\u0161anas krit\u0113rijiem.<\/li>\n<li>Fokusa jomas: ra\u017eas prognoz\u0113\u0161ana (37 %), lapu N statuss (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>Visbie\u017e\u0101k izmantot\u0101s satel\u012btu platformas: Sentinel-2, Landsat, MODIS; komerci\u0101l\u0101s: PlanetScope.<\/li>\n<li>R\u00b2 diapazoni: NDVI \u2013 ra\u017ea (0,23\u20130,84), NDRE \u2013 ra\u017ea (0,12\u20130,85), GNDVI \u2013 ra\u017ea (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Kartupe\u013cu ra\u017eas prognoz\u0113\u0161anas ieteikumi<\/h2>\n<p>Pamatojoties uz \u0161iem atkl\u0101jumiem, prakti\u0137iem vispirms j\u0101izv\u0113las saviem m\u0113r\u0137iem atbilsto\u0161a platforma. Re\u0123ion\u0101laj\u0101m ra\u017eas prognoz\u0113m bezmaksas Sentinel-2 dati nodro\u0161ina uzticamu p\u0101rkl\u0101jumu ar 10 m iz\u0161\u0137irtsp\u0113ju un 5 dienu atk\u0101rtotas apmekl\u0113juma grafiku.<\/p>\n<p>Lai preciz\u0113tu lok\u0101los apr\u0113\u0137inus, bezpilota lidapar\u0101tu (UAV) lidojumi, kas pl\u0101noti aptuveni 36\u201355 dienas p\u0113c st\u0101d\u012b\u0161anas, fiks\u0113 kritisko kupolu dinamiku un uzlabo satel\u012btu mode\u013cu kalibr\u0113\u0161anu. Zemes sensorus vislab\u0101k izmantot nejau\u0161\u0101m p\u0101rbaud\u0113m un att\u0101lo nov\u0113rojumu kalibr\u0113\u0161anai, \u012bpa\u0161i, apvienojot spektr\u0101los datus ar lauka m\u0113r\u012bjumiem.<\/p>\n<p>Run\u0101jot par ve\u0123et\u0101cijas indeksiem, prakti\u0137iem gal\u012bg\u0101s ra\u017eas prognoz\u0113\u0161anai priorit\u0101te j\u0101pie\u0161\u0137ir NDVI, NDRE un CI <sub>sarkanajai malai<\/sub> , jo tie past\u0101v\u012bgi uzr\u0101da sp\u0113c\u012bgu korel\u0101ciju.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/lv\/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=\"Kartupe\u013cu ra\u017eas prognoz\u0113\u0161anas ieteikumi\" 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>Nov\u0113rt\u0113jot hlorofila un sl\u0101pek\u013ca saturu, visprec\u012bz\u0101kos rezult\u0101tus ieg\u016bst, apvienojot sarkan\u0101s malas indeksus ar augsnes kori\u0123\u0113tiem VI, piem\u0113ram, TCARI\/OSAVI. Biomasas nov\u0113rt\u0113\u0161anai VI integr\u0113\u0161ana ar auga augstuma vai tekst\u016bras paz\u012bm\u0113m ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s sist\u0113m\u0101s v\u0113l vair\u0101k palielina precizit\u0101ti.<\/p>\n<p>Run\u0101jot par model\u0113\u0161anu, vienk\u0101r\u0161as line\u0101ras vai neline\u0101ras regresijas, izmantojot vienu indeksu, ir efekt\u012bvas, ja ir ierobe\u017eoti ticami dati. Tom\u0113r, ja ir pieejami vair\u0101ki indeksi un pal\u012bgdati (laika apst\u0101k\u013ci, augsne, apsaimnieko\u0161ana), ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s metodes, piem\u0113ram, nejau\u0161a me\u017ea vai neironu t\u012bkli, pied\u0101v\u0101 lab\u0101ku veiktsp\u0113ju. Svar\u012bgi ir tas, ka laika att\u0113li aptuveni 36\u201355 dienas p\u0113c st\u0101d\u012b\u0161anas ir \u013coti svar\u012bgi, jo \u0161is logs past\u0101v\u012bgi nodro\u0161ina visaugst\u0101ko prognoz\u0113\u0161anas precizit\u0101ti.<\/p>\n<h2>Secin\u0101jums<\/h2>\n<p>Nosl\u0113gum\u0101 j\u0101saka, ka t\u0101lizp\u0113te pied\u0101v\u0101 \u0101tru, elast\u012bgu un prec\u012bzu r\u012bku komplektu kartupe\u013cu aug\u0161anas uzraudz\u012bbai un bumbu\u013cu ra\u017eas prognoz\u0113\u0161anai. Izv\u0113loties atbilsto\u0161u platformu, informat\u012bv\u0101kos ve\u0123et\u0101cijas indeksus, datu v\u0101k\u0161anas laiku aptuveni 36\u201355 dienu vecum\u0101 un piem\u0113rojot piem\u0113rotas model\u0113\u0161anas metodes, p\u0113tnieki un prakti\u0137i var iev\u0113rojami uzlabot ra\u017eas prognozes.<\/p>\n<p>\u0160\u012b pieeja ne tikai ietaupa laiku, bet ar\u012b atbalsta gudr\u0101kus vad\u012bbas l\u0113mumus, galu gal\u0101 dodot labumu lauksaimniekiem, agronomiem un visai kartupe\u013cu pieg\u0101des \u0137\u0113dei.<\/p>\n<p><strong>Atsauce<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>u.c.<\/i>\u00a0Kartupe\u013cu aug\u0161anas monitoringa un bumbu\u013cu ra\u017eas prognoz\u0113\u0161anas ve\u0123et\u0101cijas indeksu sistem\u0101tisks p\u0101rskats, izmantojot t\u0101lizp\u0113ti.\u00a0<i>Kartupe\u013cu rez.<\/i>\u00a0<b>68<\/b>, 409.\u2013448.\u00a0lpp. (2025.\u00a0g.). <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>Kartupe\u013ci ir viena no pasaul\u0113 svar\u012bg\u0101kaj\u0101m p\u0101rtikas kult\u016br\u0101m, kas kalpo k\u0101 pamatprodukts miljoniem cilv\u0113ku. Pirmk\u0101rt, zinot, k\u0101 aug kartupe\u013cu augi\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_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 v28.2 - https:\/\/yoast.com\/product\/yoast-seo-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\/lv\/emuars\/talizpetes-vegetacijas-indeksi-parveido-kartupelu-razas-prognozesanu\/\" \/>\n<meta property=\"og:locale\" content=\"lv_LV\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Remote Sensing Vegetation Indices Transform Potato Yield Forecasting - GeoPard Agriculture\" \/>\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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