English
Sažetak
Duboka integracija procesa vođenih vještačkom inteligencijom (AI) s adaptivnim dizajnom praćenja usjeva pojavila se kao ključna pokretačka snaga za modeliranje okoliša dinamičnih poljoprivrednih pejzaža. Mapiranje zasnovano na vještačkoj inteligenciji predstavlja fundamentalnu promjenu za kartografska rješenja u inženjerstvu, prirodnim i tehničkim naukama, jer ugrađuje automatizaciju u metodologije. Ovo je posebno važno za geografske informacione sisteme (GIS) gdje je automatizacija obrade prostornih podataka neophodna. Poljoprivredni pejzaži se transformišu sezonski i godišnje, što zahtijeva precizno predviđanje okoliša. U ovoj studiji dajemo pregled nedavnih metodoloških napredaka u tri interdisciplinarna područja: praćenje okoliša dinamike poljoprivrednih pejzaža u studijama tla, primjena vještačke inteligencije u GIS-u (tehnike mašinskog učenja (ML) i dubokog učenja (DL)) i bibliometrijska analiza korištenjem biblioteka zasnovanih na R-u (Bibliometrix, Treemap i Wordcloud) i Mendeley referentnog sistema. Istražujemo kako su nove metodologije vještačke inteligencije i strojnog učenja primijenjene na skalabilnu analizu zasnovanu na podacima u poljoprivredi i studijama tla i raspravljamo o pitanjima povezanim s njihovom primjenom. Ovaj pregled se zasniva na kritičkom skupu od preko 100 radova indeksiranih u priznatim bazama podataka Scopus, Web of Science (WoS), PubMed i Google Scholar za dubinsku analizu primjene umjetne inteligencije u studijama tla i okoliša. Iznosimo buduće perspektive za umjetnu inteligenciju u analizi okoliša, identificirajući najbolje prakse za implementaciju umjetne inteligencije u GIS-u i sistematsko poređenje vrijednosti u daljinskom istraživanju tla.
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2026/03/25
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