Identification of salt-affected soils in the coastal area Of Krishna District, Andhra Pradesh, using remote sensing data and machine learning techniques

• Autorzy: Anuradha Govada , Chivukula Venkata Sai Sankara Vineeth , Kothagundla Naga Ganesh

Identyfikatory

DOI

10.35784/iapgos.5903

Warianty tytułu

PL

Identyfikacja gleb zasolonych w strefie przybrzeżnej dystryktu Krishna, Andhra Pradesh, z wykorzystaniem danych teledetekcyjnych i technik uczenia maszynowego

• Języki publikacji: EN

Abstrakty

EN

In agricultural soil analysis, the challenge of soil salinization in regions like Krishna District, Andhra Pradesh, profoundly impacts soil health, crop yield, and land usability, affecting approximately 77,598 hectares of land. To address this issue, three machine learning algorithms are compared for classifying salinity levels in the coastal area of Krishna district, Machilipatnam. This study utilizes Landsat-8 images from 2014 to 2021, correcting for cloud cover and creating a true-color composite. The study area is defined and visualized. Twelve indices, derived from Landsat imagery, are incorporated into the analysis. These indices, including spectral bands and mathematical expressions, are added as image bands. The median of these indices is calculated, and sample points representing both non-saline and saline areas are used for supervised machine learning. The data is divided into two sets: training and validation. The study evaluates Random Forest, Classification and Regression Trees, and Support Vector Machines for classifying soil salinity levels using these indices. The RF algorithm produced an accuracy of 92.1%, CART produced 91.3%, and SVM produced 86%. Results are displayed on the map, representing predicted salinity levels with distinct colors. Performance metrics are evaluated, and they assess algorithm performance. The research involved gives insights into the classification of soil salinity using machine learning, which could represent an efficient solution to the problem of soil salinization in Machilipatnam.

PL

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

EN

soil salinity; salinity index; remote sensing; machine learning; prediction

PL

zasolenie gleby; wskaźnik zasolenia; teledetekcja; uczenie maszynowe; przewidywanie

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 2
• Strony: 83--88
• Opis fizyczny: Bibliogr. 34 poz., fot., tab., wykr.

Twórcy

autor

Anuradha Govada

• V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering, Vinobha Nagar, Ibrahimpatnam, India

• https://orcid.org/0000-0002-0999-0376

autor

Chivukula Venkata Sai Sankara Vineeth

• V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering, Vinobha Nagar, Ibrahimpatnam, India

• https://orcid.org/0009-0007-8919-599X

autor

Kothagundla Naga Ganesh

• V. R. Siddhartha Engineering College, Faculty of Department of Computer Science and Engineering, Vinobha Nagar, Ibrahimpatnam, India

• https://orcid.org/0009-0008-4830-726X

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