IDENTYFIKACJA GLEB ZASOLONYCH W STREFIE PRZYBRZEŻNEJ DYSTRYKTU KRISHNA, ANDHRA PRADESH, Z WYKORZYSTANIEM DANYCH TELEDETEKCYJNYCH I TECHNIK UCZENIA MASZYNOWEGO
Govada Anuradha
V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering (Indie)
Venkata Sai Sankara Vineeth Chivukula
qualityhacker2002@gmail.comV. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering (Indie)
https://orcid.org/0009-0007-8919-599X
Naga Ganesh Kothangundla
V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering (Indie)
https://orcid.org/0009-0008-4830-726X
Abstrakt
W rolniczej analizie gleby, wyzwanie zasolenia gleby w regionach takich jak dystrykt Krishna, Andhra Pradesh, głęboko wpływa na zdrowie gleby, plony i użyteczność gruntów, wpływając na około 77 598 hektarów ziemi. Aby rozwiązać tę kwestię, porównano trzy algorytmy uczenia maszynowego do klasyfikacji poziomów zasolenia w obszarze przybrzeżnym dystryktu Krishna, Machilipatnam. W badaniu wykorzystano obrazy Landsat-8 z lat 2014-2021, korygując je pod kątem zachmurzenia i tworząc kompozycję w prawdziwych kolorach. Obszar badań został zdefiniowany i zwizualizowany. Do analizy włączono dwanaście wskaźników pochodzących ze zdjęć Landsat. Wskaźniki te, w tym pasma widmowe i wyrażenia matematyczne, są dodawane jako pasma obrazu. Mediana tych wskaźników jest obliczana, a przykładowe punkty reprezentujące zarówno obszary niezasolone, jak i zasolone są wykorzystywane do nadzorowanego uczenia maszynowego. Dane są podzielone na dwa zestawy: treningowy i walidacyjny. W badaniu oceniono Random Forest, Classification and Regression Trees i Support Vector Machines pod kątem klasyfikacji poziomów zasolenia gleby przy użyciu tych wskaźników. Algorytm RF uzyskał dokładność 92,1%, CART 91,3%, a SVM 86%. Wyniki są wyświetlane na mapie, przedstawiając przewidywane poziomy zasolenia za pomocą różnych kolorów. Oceniane są wskaźniki wydajności i wydajność algorytmów. Przeprowadzone badania dają wgląd w klasyfikację zasolenia gleby przy użyciu uczenia maszynowego, co może stanowić skuteczne rozwiązanie problemu zasolenia gleby w Machilipatnam.
Słowa kluczowe:
zasolenie gleby, wskaźnik zasolenia, teledetekcja, uczenie maszynowe, przewidywanieBibliografia
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Autorzy
Govada AnuradhaV. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering Indie
Autorzy
Venkata Sai Sankara Vineeth Chivukulaqualityhacker2002@gmail.com
V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering Indie
https://orcid.org/0009-0007-8919-599X
Autorzy
Naga Ganesh KothangundlaV. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering Indie
https://orcid.org/0009-0008-4830-726X
Statystyki
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