{"id":24513,"date":"2023-10-23T21:29:18","date_gmt":"2023-10-23T21:29:18","guid":{"rendered":"https:\/\/stacc.veebilahendused.ee\/?p=24513"},"modified":"2025-04-23T00:59:25","modified_gmt":"2025-04-23T00:59:25","slug":"stacc-eagronom-satelliidipiltide-ja-tehisnarvivorkude-viljad-pollumajanduses","status":"publish","type":"post","link":"https:\/\/stacc.veebilahendused.ee\/et\/stacc-eagronom-satelliidipiltide-ja-tehisnarvivorkude-viljad-pollumajanduses\/","title":{"rendered":"STACC & eAgronom: satelliidipiltide ja tehisn\u00e4rviv\u00f5rkude viljad p\u00f5llumajanduses"},"content":{"rendered":"\n

\u00dchiskondliku arengu ja majandusliku stabiilsuse seisukohast on maaelu ja p\u00f5llumajandus toonud kaasa nii v\u00e4ljakutseid kui ka v\u00f5imalusi. \u00dcks v\u00e4ljakutsetest on saagikuse ennustamine: kuidas teada, millal ja kui suur saak tuleb ning kuidas seda optimeerida? Vastus v\u00f5ib peituda hiljutises STACCi ja eAgronomi koost\u00f6\u00f6s l\u00e4biviidud projektis.<\/p>\n\n\n\n

Saagikuse ennustamine<\/h3>\n\n\n\n

eAgronom<\/a> on p\u00f5llumajandustarkvara arendav ettev\u00f5te, mis koost\u00f6\u00f6s STACCiga arendas nutika tehisn\u00e4gemise s\u00fcsteemi, mis v\u00f5imaldab satelliidipiltide p\u00f5hjal prognoosida talinisup\u00f5ldude k\u00e4esoleva hooaja saagikust. Nimelt v\u00f5imaldavad Euroopa Liidu Copernicus-programmi raames orbiidile saadetud Sentinel satelliidid teha peaaegu reaalajas maaseiret, seejuures tagades andmetele tasuta ligip\u00e4\u00e4su. Kosmosest tehtud piltidel p\u00f5hinevat teadmust kasutavad \u00e4ra mitmed p\u00f5llumajanduse k\u00f5ige kaasaegsemad teadus- ja arengusuunad ning nii ka eAgronomi viljasaagikuse ennustamise projekt. Projektis loodud rakendus oskab lugeda satelliidipiltide multispektraalseid andmeid, neid ajaseeriana t\u00f6\u00f6delda (joonis 1) ning tehisn\u00e4rviv\u00f5rguga hinnata talinisup\u00f5ldude viljasaaki.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Joonis 1.<\/strong> N\u00e4ide \u00fche p\u00f5llu saagikoristusele eelnevast ajaseeriast. Mudeli sisendiks katsetati kaht agregeerimise l\u00e4henemist: mediaan- ja histogramm.<\/p>\n\n\n\n

Tulemuslik mudel<\/h3>\n\n\n\n

Projektis v\u00f5rreldi potentsiaalse saagikuse ennustamise mudelina nii klassikalisi masin\u00f5ppe algoritme kui ka moodsaid tehisn\u00e4rviv\u00f5rkude arhitektuure, kuid erilist t\u00e4helepanu pidi p\u00f6\u00f6rama ka t\u00e4psusm\u00f5\u00f5dikule. T\u00fc\u00fcpiliselt kasutatakse sarnaste regressiooni\u00fclesannete tulemuste hindamiseks ruutkeskmist viga (RMSE). Samas, p\u00f5llusaagikuse ennustamisel v\u00f5ib saagi suurusj\u00e4rk kahe uurimuse vahel olla v\u00e4gagi erinev. N\u00e4iteks Poola talinisu saak v\u00f5ib olla keskmiselt palju suurem kui Eesti oma. Seet\u00f5ttu RMSE m\u00f5\u00f5dikud kahe uurimuse vahel, mis v\u00f5ivad keskenduda samale viljale, kuid erinevatele regioonidele, ei ole otseselt v\u00f5rreldavad. Selleks, et projekti tulemus siiski varasema teaduskirjandusega v\u00f5rreldav oleks, kasutati m\u00f5\u00f5diku normaliseeritud varianti NRMSE, kus RMSE jagatakse l\u00e4bi keskmise saagikusega.<\/p>\n\n\n\n

\n

<\/p>\n\u201c\u00dcks projekti k\u00f5ige silmapaistvamaid tulemusi on v\u00f5ime ennustada saagikust juba 6 n\u00e4dalat ette <\/strong>ning seda sarnase t\u00e4psusega, mis vahetult enne saagikoristust<\/strong>. \u00dchtlasi saab kinnitada, et projektis loodud mudel on t\u00e4psuselt samav\u00e4\u00e4rne v\u00e4rskeima teaduskirjanduse tulemustega.\u201d 
\u2014 Ingvar Baranin, projektis osalenud andmeteadlane<\/cite><\/blockquote>\n\n\n\n

Testitud lahendustest parima tulemuse andis LSTM neuronitel p\u00f5hinev n\u00e4rviv\u00f5rk, mis suutis saagikust mudeldada NRMSE veaga 0,25. Sarnase tulemuse on hiljutises teaduskirjanduses saanud nii Soome kui ka Austraalia teadlased [1, 2].<\/p>\n\n\n\n

Panus p\u00f5llumajanduse arengusse<\/h3>\n\n\n\n

eAgronomi ja STACCi koost\u00f6\u00f6projekt on toonud kaasa Eesti p\u00f5llumajanduses m\u00e4rkimisv\u00e4\u00e4rse arengu. Kasutades satelliidipilte ja tehisn\u00e4rviv\u00f5rke loodi mudel, mis v\u00f5imaldab ennustada saagi suurust aegsasti enne koristusperioodi algust. See on oluline samm, et aidata p\u00f5llumajandustootjatel oma ressursse t\u00f5husalt kasutada ning paremini tulevikku planeerida.<\/p>\n\n\n\n

\n

<\/p>\n\u201cSaagikuse v\u00f5imalikult t\u00e4pne ennustamine aitab viljakasvatajal planeerida koristust, viljaveo logistikat, laopinna vajadust, vilja kuivatamise protsessi, saagi m\u00fc\u00fcki ja ka koristuse j\u00e4rgseid toiminguid p\u00f5llul, nagu p\u00f5hu laiali jaotamine ja maaharimine.\u201d 
\u2014 eAgronomi agronoom Piibe Vaher<\/cite><\/blockquote>\n\n\n\n

Kui sinu ettev\u00f5te vajab sarnast uuringut, siis STACC on valmis teid aitama!<\/strong><\/p>\n\n\n\n

Allikad:<\/em>
1. Fiona H. Evans and Jianxiu Shen. Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning. Remote Sensing, 13(13), 2021.
2. Maria Yli-Heikkila, Samantha Wittke, Markku Luotamo, Eetu Puttonen, Mika Sulkava, Petri Pellikka, Janne Heiskanen, and Arto Klami. Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network. Remote Sensing, 14(17), 2022.<\/p>\n","protected":false},"excerpt":{"rendered":"

\u00dchiskondliku arengu ja majandusliku stabiilsuse seisukohast on maaelu ja p\u00f5llumajandus toonud kaasa nii v\u00e4ljakutseid kui ka v\u00f5imalusi. \u00dcks v\u00e4ljakutsetest on saagikuse ennustamine: kuidas teada, millal ja kui suur saak tuleb ning kuidas seda optimeerida? Vastus v\u00f5ib peituda hiljutises STACCi ja eAgronomi koost\u00f6\u00f6s l\u00e4biviidud projektis. Saagikuse ennustamine eAgronom on p\u00f5llumajandustarkvara arendav ettev\u00f5te, mis koost\u00f6\u00f6s STACCiga arendas<\/p>\n","protected":false},"author":2,"featured_media":24514,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,100],"tags":[81,104,155,136],"class_list":["post-24513","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-assistendid","category-toostus","tag-business-intelligence-et","tag-data-science-et","tag-machine-learning-et","tag-edulugu"],"yoast_head":"\nSTACC & eAgronom: satelliidipiltide ja tehisn\u00e4rviv\u00f5rkude viljad p\u00f5llumajanduses - STACC<\/title>\n<meta name=\"description\" content=\"\u00dchiskondliku arengu ja majandusliku stabiilsuse seisukohast on maaelu ja p\u00f5llumajandus toonud kaasa nii v\u00e4ljakutseid kui ka v\u00f5imalusi.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, 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