{"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":"wskazniki-roslinnosci-uzyskane-metoda-teledetekcji-zmieniaja-prognozowanie-plonow-ziemniakow","status":"publish","type":"post","link":"https:\/\/geopard.tech\/pl\/blog\/remote-sensing-vegetation-indices-transform-potato-yield-forecasting\/","title":{"rendered":"Wska\u017aniki ro\u015blinno\u015bci oparte na teledetekcji zmieniaj\u0105 prognozowanie plon\u00f3w ziemniak\u00f3w"},"content":{"rendered":"<p>Ziemniaki s\u0105 jedn\u0105 z najwa\u017cniejszych upraw spo\u017cywczych na \u015bwiecie, stanowi\u0105c podstaw\u0119 wy\u017cywienia milion\u00f3w ludzi. Po pierwsze, wiedza o tym, jak rosn\u0105 ziemniaki i mo\u017cliwo\u015b\u0107 przewidywania ich plon\u00f3w, pomaga rolnikom skuteczniej zarz\u0105dza\u0107 nawadnianiem, nawo\u017ceniem i zwalczaniem szkodnik\u00f3w.<\/p>\n<p>Po drugie, zak\u0142ady przetw\u00f3rstwa \u017cywno\u015bci i magazyny mog\u0105 lepiej planowa\u0107 logistyk\u0119 i nak\u0142ad pracy, dysponuj\u0105c wiarygodnymi szacunkami plon\u00f3w. Jednak tradycyjne metody \u2013 takie jak fizyczne przechodzenie przez pola i r\u0119czne mierzenie ro\u015blin \u2013 s\u0105 czasoch\u0142onne i podatne na b\u0142\u0119dy ludzkie.<\/p>\n<p>Dlatego naukowcy zwr\u00f3cili si\u0119 w stron\u0119 teledetekcji, wykorzystuj\u0105c kamery i czujniki na satelitach, dronach lub urz\u0105dzeniach przeno\u015bnych, aby szybciej i dok\u0142adniej monitorowa\u0107 wzrost ziemniak\u00f3w i prognozowa\u0107 plony.<\/p>\n<h2>Zrozumienie prognoz plon\u00f3w ziemniak\u00f3w<\/h2>\n<p>W ci\u0105gu ostatnich dw\u00f3ch dekad zainteresowanie zastosowaniem teledetekcji w badaniach nad ziemniakami znacznie wzros\u0142o. W rzeczywisto\u015bci, przegl\u0105d systematyczny zidentyfikowa\u0142 79 bada\u0144 opublikowanych w latach 2000-2022 na ten temat, spo\u015br\u00f3d 482 wst\u0119pnie przeanalizowanych artyku\u0142\u00f3w.<\/p>\n<p>Aby zapewni\u0107 przejrzysto\u015b\u0107 i powtarzalno\u015b\u0107, autorzy zastosowali si\u0119 do ustalonych wytycznych (Kitchenham &amp; Charters 2007; framework PRISMA), przeszukuj\u0105c osiem g\u0142\u00f3wnych baz danych \u2014 Google Scholar, ScienceDirect, Scopus, Web of Science, IEEE Xplore, MDPI, Taylor &amp; Francis i SpringerLink \u2014 stosuj\u0105c terminy takie jak \u201cprognozowanie plon\u00f3w ziemniak\u00f3w\u201d ORAZ \u201cteledetekcja\u201d.\u201d<\/p>\n<p>W zwi\u0105zku z tym uwzgl\u0119dniono wy\u0142\u0105cznie oryginalne badania w j\u0119zyku angielskim, wykorzystuj\u0105ce dane teledetekcyjne do monitorowania wzrostu lub szacowania plon\u00f3w. Ponadto dane z ka\u017cdego wybranego artyku\u0142u zosta\u0142y wyodr\u0119bnione zgodnie z czterema kluczowymi pytaniami:<\/p>\n<ul>\n<li>Jak\u0105 platform\u0119 czujnikow\u0105 zastosowano (satelitarn\u0105, bezza\u0142ogow\u0105 czy naziemn\u0105)?<\/li>\n<li>Jakie wska\u017aniki ro\u015blinno\u015bci lub cechy widmowe poddano ocenie?<\/li>\n<li>Jakie cechy upraw monitorowano (biomas\u0119, powierzchni\u0119 li\u015bci, chlorofil, azot)?<\/li>\n<li>Jak dok\u0142adnie mo\u017cna przewidzie\u0107 ko\u0144cowy plon bulw (wsp\u00f3\u0142czynnik determinacji R\u00b2)?<\/li>\n<\/ul>\n<p>Pytania te pomog\u0142y recenzentom okre\u015bli\u0107 stan wiedzy i zidentyfikowa\u0107 luki, na kt\u00f3rych mo\u017cna by skupi\u0107 przysz\u0142e badania.<\/p>\n<h2>Platformy teledetekcyjne i wska\u017aniki ro\u015blinno\u015bci<\/h2>\n<p>Naukowcy wykorzystali trzy g\u0142\u00f3wne typy platform teledetekcyjnych, z kt\u00f3rych ka\u017cda ma swoje zalety i ograniczenia. Po pierwsze, satelity optyczne, takie jak Sentinel\u20112 (rozdzielczo\u015b\u0107 przestrzenna 10 m, rewizyta 5 dni) i Landsat 5\u20138 (30 m, rewizyta 16 dni), oferuj\u0105 szeroki zasi\u0119g i cz\u0119sto bezp\u0142atny dost\u0119p do danych.<\/p>\n<p>Po drugie, satelity takie jak MODIS\/TERRA\/Aqua (250\u20131000 m, codzienne lub dwudniowe przegl\u0105dy) i systemy komercyjne takie jak PlanetScope (3 m, codzienne, kosztuj\u0105ce oko\u0142o $218 za 100 km\u00b2) umo\u017cliwiaj\u0105 cz\u0119stsze monitorowanie lub monitorowanie z wy\u017csz\u0105 rozdzielczo\u015bci\u0105, cho\u0107 koszty mog\u0105 mie\u0107 znaczenie.<\/p>\n<p><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" data-attachment-id=\"11792\" data-permalink=\"https:\/\/geopard.tech\/pl\/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 teledetekcyjne i wska\u017aniki ro\u015blinno\u015bci\" 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 trzecie, bezza\u0142ogowe statki powietrzne (UAV) wyposa\u017cone w kamery multispektralne lub hiperspektralne zapewniaj\u0105 bardzo wysok\u0105 rozdzielczo\u015b\u0107 (nawet kilka centymetr\u00f3w na piksel) i mog\u0105 by\u0107 wykorzystywane na \u017c\u0105danie, ale obejmuj\u0105 mniejszy obszar i wymagaj\u0105 wi\u0119kszej logistyki.<\/p>\n<p>Wreszcie czujniki naziemne \u2014 takie jak przeno\u015bne mierniki NDVI i mierniki chlorofilu SPAD \u2014 umo\u017cliwiaj\u0105 pomiary punktowe o wysokiej precyzji, cho\u0107 ich stosowanie na du\u017cych polach jest czasoch\u0142onne.<\/p>\n<p>Wska\u017aniki wegetacji (VI) przekszta\u0142caj\u0105 surowe warto\u015bci wsp\u00f3\u0142czynnika odbicia \u015bwiat\u0142a w miarodajne szacunki cech ro\u015blin. Do najcz\u0119\u015bciej stosowanych wska\u017anik\u00f3w w badaniach nad ziemniakami nale\u017c\u0105:<\/p>\n<ul>\n<li>NDVI (znormalizowany wska\u017anik r\u00f3\u017cnicowy ro\u015blinno\u015bci): (NIR \u2013 czerwony) \/ (NIR + czerwony)<\/li>\n<li>GNDVI (Zielony NDVI): (NIR \u2013 Zielony) \/ (NIR + Zielony)<\/li>\n<li>NDRE (Normalizowana r\u00f3\u017cnica czerwonej kraw\u0119dzi): (NIR \u2013 RedEdge) \/ (NIR + RedEdge)<\/li>\n<li>OSAVI (zoptymalizowany wska\u017anik ro\u015blinno\u015bci dostosowany do gleby): 1,16 \u00d7 (NIR \u2013 czerwony) \/ (NIR + czerwony + 0,16)<\/li>\n<li>EVI (Enhanced Vegetation Index), CIred-edge, CIgreen i inne. .<\/li>\n<\/ul>\n<p>Indeksy te s\u0105 dobierane na podstawie ich wra\u017cliwo\u015bci na zwarcie koron drzew, zawarto\u015b\u0107 chlorofilu i pod\u0142o\u017ce glebowe. W zwi\u0105zku z tym stanowi\u0105 podstaw\u0119 do oceny kondycji ro\u015blin i prognozowania plon\u00f3w.<\/p>\n<h2>Monitorowanie wzrostu ziemniak\u00f3w i prognozowanie plon\u00f3w<\/h2>\n<p>Za pomoc\u0105 teledetekcji naukowcy monitoruj\u0105 kluczowe cechy upraw ziemniak\u00f3w \u2013 biomas\u0119 nadziemn\u0105 (AGB), wska\u017anik powierzchni li\u015bci (LAI), zawarto\u015b\u0107 chlorofilu w koronie (CCC) i status azotu w li\u015bciach \u2013 a nast\u0119pnie odnosz\u0105 te dane do ko\u0144cowego plonu bulw.<\/p>\n<p>Po pierwsze, szacowanie AGB wy\u0142\u0105cznie przy u\u017cyciu wska\u017anik\u00f3w VI mo\u017ce by\u0107 trudne, gdy pokrywa koron drzew jest g\u0119sta, poniewa\u017c wiele wska\u017anik\u00f3w ulega nasyceniu; w zwi\u0105zku z tym \u0142\u0105czenie wska\u017anik\u00f3w VI z cechami wysoko\u015bci lub tekstury ro\u015blin w modelach uczenia maszynowego cz\u0119sto poprawia dok\u0142adno\u015b\u0107.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11793\" data-permalink=\"https:\/\/geopard.tech\/pl\/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=\"Monitorowanie wzrostu ziemniak\u00f3w i prognozowanie plon\u00f3w\" 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 drugie, ocena LAI \u2014 ca\u0142kowitej jednostronnej powierzchni li\u015bci na powierzchni gruntu \u2014 pozwoli\u0142a na osi\u0105gni\u0119cie warto\u015bci R\u00b2 na poziomie 0,84 dzi\u0119ki wykorzystaniu danych szereg\u00f3w czasowych pochodz\u0105cych zar\u00f3wno z hiperspektralnych czujnik\u00f3w bezza\u0142ogowych statk\u00f3w powietrznych, jak i multispektralnych czujnik\u00f3w satelitarnych.<\/p>\n<p>Po trzecie, szacunki CCC, pochodz\u0105ce z indeks\u00f3w takich jak CIred\u2011edge, CIgreen, TCARI\/OSAVI i TCARI + OSAVI, osi\u0105gn\u0119\u0142y R\u00b2 \u2248 0,85 w fazie wegetatywnej, co wskazuje na siln\u0105 korelacj\u0119 z laboratoryjnie mierzonym chlorofilem.<\/p>\n<p>Na koniec, status azotu w li\u015bciach, niezb\u0119dny do prawid\u0142owego wzrostu, przewidywano przy R\u00b2 w zakresie od 0,52 do 0,95 przy u\u017cyciu czujnik\u00f3w naziemnych oraz modeli regresji lub lasu losowego.<\/p>\n<p>Je\u015bli chodzi o prognozowanie plon\u00f3w bulw, wyr\u00f3\u017cnia si\u0119 dwa g\u0142\u00f3wne podej\u015bcia do modelowania:<\/p>\n<p>Empiryczne modele regresji: W tym przypadku pojedynczy wska\u017anik VI \u2014 najcz\u0119\u015bciej NDVI, GNDVI lub NDRE \u2014 jest dopasowywany do danych dotycz\u0105cych plon\u00f3w rzeczywistych. Podane warto\u015bci R\u00b2 dla NDVI w funkcji plonu wahaj\u0105 si\u0119 od 0,23 do 0,84 (mediana \u2248 0,67), natomiast korelacje NDRE\u2013wydajno\u015b\u0107 wahaj\u0105 si\u0119 od 0,12 do 0,85 (mediana \u2248 0,61).<\/p>\n<p>Modele uczenia maszynowego: Nale\u017c\u0105 do nich lasy losowe, maszyny wektor\u00f3w no\u015bnych oraz sieci neuronowe \u0142\u0105cz\u0105ce wiele VI, pasma widmowe i czynniki niespektralne, takie jak pogoda, gleba i zarz\u0105dzanie. W niekt\u00f3rych badaniach takie modele podnios\u0142y R\u00b2 do 0,93.<\/p>\n<p>Co wi\u0119cej, moment zbierania danych ma ogromny wp\u0142yw na dok\u0142adno\u015b\u0107 prognoz. W wielu badaniach pomiary VI wykonane w okresie 36\u201355 dni po posadzeniu (DAP) wykaza\u0142y najwy\u017csz\u0105 korelacj\u0119 z ko\u0144cowym plonem bulw.<\/p>\n<p>Ten etap zbiega si\u0119 z maksymalnym pokryciem gleby i pocz\u0105tkiem zawi\u0105zywania bulw, co sprawia, \u017ce struktura ro\u015bliny jest najbardziej miarodajna dla ostatecznego plonu. Oto kilka kluczowych statystyk:<\/p>\n<ul>\n<li>Spo\u015br\u00f3d 482 zidentyfikowanych bada\u0144 79 (2000\u20132022) spe\u0142ni\u0142o kryteria przegl\u0105du.<\/li>\n<li>Obszary zainteresowania: prognozowanie plonu (37 %), status azotu w li\u015bciach (21 %), AGB (15 %), LAI (15 %), CCC (12 %).<\/li>\n<li>Najcz\u0119\u015bciej u\u017cywane platformy satelitarne: Sentinel\u20112, Landsat, MODIS; komercyjne: PlanetScope.<\/li>\n<li>Zakresy R\u00b2: NDVI\u2013wydajno\u015b\u0107 (0,23\u20130,84), NDRE\u2013wydajno\u015b\u0107 (0,12\u20130,85), GNDVI\u2013wydajno\u015b\u0107 (0,26\u20130,75).<\/li>\n<\/ul>\n<h2>Zalecenia dotycz\u0105ce prognozowania plon\u00f3w ziemniak\u00f3w<\/h2>\n<p>Na podstawie tych ustale\u0144 praktycy powinni najpierw wybra\u0107 platform\u0119 odpowiedni\u0105 do swoich cel\u00f3w. W przypadku regionalnych prognoz plon\u00f3w, bezp\u0142atne dane Sentinel\u20112 zapewniaj\u0105 wiarygodne pokrycie z rozdzielczo\u015bci\u0105 10 m i 5-dniowym harmonogramem rewizyt.<\/p>\n<p>Aby doprecyzowa\u0107 lokalne szacunki, loty bezza\u0142ogowych statk\u00f3w powietrznych (UAV) zaplanowane na oko\u0142o 36\u201355 dni po posadzeniu ro\u015blin rejestruj\u0105 krytyczn\u0105 dynamik\u0119 koron drzew i poprawiaj\u0105 kalibracj\u0119 modeli satelitarnych. Czujniki naziemne najlepiej sprawdzaj\u0105 si\u0119 w przypadku kontroli punktowych i kalibracji zdalnych obserwacji, zw\u0142aszcza w przypadku \u0142\u0105czenia danych spektralnych z pomiarami terenowymi.<\/p>\n<p>Je\u015bli chodzi o wska\u017aniki ro\u015blinno\u015bci, praktycy powinni w przewidywaniu plon\u00f3w ko\u0144cowych priorytetowo traktowa\u0107 wska\u017aniki NDVI, NDRE i CI <sub>red-edge<\/sub> , poniewa\u017c stale wykazuj\u0105 one silne korelacje.<\/p>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" data-attachment-id=\"11794\" data-permalink=\"https:\/\/geopard.tech\/pl\/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=\"Zalecenia dotycz\u0105ce prognozowania plon\u00f3w ziemniak\u00f3w\" 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>Przy szacowaniu zawarto\u015bci chlorofilu i azotu, po\u0142\u0105czenie wska\u017anik\u00f3w kraw\u0119dzi czerwonej z wska\u017anikami VI skorygowanymi o gleb\u0119 \u2013 takimi jak TCARI\/OSAVI \u2013 daje najdok\u0142adniejsze wyniki. W przypadku szacowania biomasy, integracja wska\u017anik\u00f3w VI z wysoko\u015bci\u0105 lub tekstur\u0105 ro\u015blin w ramach uczenia maszynowego dodatkowo zwi\u0119ksza dok\u0142adno\u015b\u0107.<\/p>\n<p>Je\u015bli chodzi o modelowanie, proste regresje liniowe lub nieliniowe z wykorzystaniem jednego indeksu s\u0105 skuteczne, gdy dane bazowe s\u0105 ograniczone. Jednak w przypadku dost\u0119pno\u015bci wielu indeks\u00f3w i danych pomocniczych (pogoda, gleba, zarz\u0105dzanie) metody uczenia maszynowego, takie jak las losowy lub sieci neuronowe, oferuj\u0105 lepsz\u0105 wydajno\u015b\u0107. Co wa\u017cne, kluczowe znaczenie ma czas wykonania zdj\u0119\u0107 oko\u0142o 36\u201355 dni po posadzeniu, poniewa\u017c ten okres niezmiennie zapewnia najwy\u017csz\u0105 dok\u0142adno\u015b\u0107 prognoz.<\/p>\n<h2>Wniosek<\/h2>\n<p>Podsumowuj\u0105c, teledetekcja oferuje szybki, elastyczny i dok\u0142adny zestaw narz\u0119dzi do monitorowania wzrostu ziemniak\u00f3w i prognozowania plon\u00f3w bulw. Wybieraj\u0105c odpowiedni\u0105 platform\u0119, najbardziej informatywne wska\u017aniki wegetacji, ustalaj\u0105c czas zbierania danych na 36\u201355 DAP oraz stosuj\u0105c odpowiednie techniki modelowania, naukowcy i praktycy mog\u0105 znacz\u0105co udoskonali\u0107 prognozy plon\u00f3w.<\/p>\n<p>Takie podej\u015bcie nie tylko oszcz\u0119dza czas, ale tak\u017ce pozwala podejmowa\u0107 m\u0105drzejsze decyzje zarz\u0105dcze, co ostatecznie przynosi korzy\u015bci rolnikom, agronomom i ca\u0142emu \u0142a\u0144cuchowi dostaw ziemniak\u00f3w.<\/p>\n<p><strong>Odniesienie<\/strong>: Mukiibi, A., Machakaire, ATB, Franke, AC.\u00a0<i>i wsp.<\/i>\u00a0Systematyczny przegl\u0105d wska\u017anik\u00f3w wegetacji do monitorowania wzrostu ziemniak\u00f3w i prognozowania plon\u00f3w bulw za pomoc\u0105 teledetekcji.\u00a0<i>Res. ziemniak\u00f3w.<\/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>Ziemniaki s\u0105 jedn\u0105 z najwa\u017cniejszych upraw spo\u017cywczych na \u015bwiecie, stanowi\u0105c podstaw\u0119 wy\u017cywienia milion\u00f3w ludzi. Po pierwsze, warto wiedzie\u0107, jak rosn\u0105 ziemniaki\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\/pl\/blog\/wskazniki-roslinnosci-uzyskane-metoda-teledetekcji-zmieniaja-prognozowanie-plonow-ziemniakow\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\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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