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.com
V. 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, przewidywanie

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Opublikowane
2024-06-30

Cited By / Share

Anuradha, G., Chivukula, V. S. S. V., & Kothangundla, N. G. (2024). IDENTYFIKACJA GLEB ZASOLONYCH W STREFIE PRZYBRZEŻNEJ DYSTRYKTU KRISHNA, ANDHRA PRADESH, Z WYKORZYSTANIEM DANYCH TELEDETEKCYJNYCH I TECHNIK UCZENIA MASZYNOWEGO. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(2), 83–88. https://doi.org/10.35784/iapgos.5903

Autorzy

Govada Anuradha 

V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering Indie

Autorzy

Venkata Sai Sankara Vineeth Chivukula 
qualityhacker2002@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 Kothangundla 

V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering Indie
https://orcid.org/0009-0008-4830-726X

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