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

Abbas A. et al.: Characterizing soil salinity in irrigated agriculture using a remote sensing approach. Physics and Chemistry of the Earth, Parts a/B/C 55–57, 2013, 43–52.
  Google Scholar

Aksoy S. et al.: Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data. Advances in Space Research 2022.
  Google Scholar

Asfaw E. et al.: Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia 17(3), 2018, 250–258.
  Google Scholar

Bharathi S. et al.: Rainfall Analysis For Drought Investigation In Krishna Zone Of Andhra Pradesh. Agricultural Science Digest 31(2), 2011, 150–52.
  Google Scholar

Cherlinka V.: Soil Salinization Causes & How to Prevent and Manage It. 2021 [https://eos.com/blog/soil-salinization/].
  Google Scholar

Chhabra R.: Classification of Salt-Affected Soils. Arid Land Research and Management 19(1), 2004, 61–79.
  Google Scholar

Chinchmalatpure A.: Reclamation and Management of Salt Affected Soils for Increasing Farm Productivity and Farmers’ Income. 2017 [https://krishi.icar.gov.in/jspui/bitstream/123456789/10792/1/Reclamation%20and%20Management.pdf].
  Google Scholar

CSSRI et al.: Indo-Dutch Network Project (IDNP): A Methodology for Identification of Water-logging and Soil Salinity Conditions Using Remote Sensing. 2002 [https://edepot.wur.nl/87639].
  Google Scholar

Douaoui A. et al.: Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma 134(1–2), 2006, 217–30.
  Google Scholar

Extent and distribution of salt affected soils in India – ICAR-CSSRI: Central Soil Salinity Research Institute, 2024 [https://cssri.res.in/extent-and-distribution-of-salt-affected-soils-in-india/].
  Google Scholar

Fathizad H. et al.: Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma 365, 2020, 114233.
  Google Scholar

Fathololoumi S. et al.: Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran. Science of the Total Environment 721, 2020, 137703.
  Google Scholar

Gomes F. et al.: Velocidade de infiltração da água num plintossolo háplico de campo de murundu sob uma cronossequência de interferência antrópica. Revista Brasileira de Agricultura Irrigada 5(3), 2011, 245–253.
  Google Scholar

Hoa P. et al.: Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sensing 11(2), 2019, 128.
  Google Scholar

Hussain.: Present Scenario of Global Salt Affected Soils, Its Management and Importance of Salinity Research. International Research Journal of Biological Sciences 1–3, 2019, 1.
  Google Scholar

Jabbar M. et al.: Assessment of Soil Salinity Risk on the Agricultural Area in Basrah Province, Iraq: Using Remote Sensing and GIS Techniques. Journal of Earth Science 23(6), 2012, 881–891.
  Google Scholar

Kabiraj S. et al.: Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques. Environmental Monitoring and Assessment 195(3), 2023.
  Google Scholar

Kabiraj S. et al.: Comparative assessment of satellite images spectral characteristics in identifying the different levels of soil salinization using machine learning techniques in Google Earth Engine. Earth Science Informatics 15(4), 2022, 2275–2288.
  Google Scholar

Khan N. et al.: Mapping Salt-affected Soils Using Remote Sensing Indicators-A Simple Approach with the Use of GIS IDRISI. 2001 [https://a-a-r-s.org/proceeding/ACRS2001/Papers/AGS-05.pdf].
  Google Scholar

Krishna P.V. et al.: Health risk assessment of heavy metal accumulation in the food fish, Channa striata from Krishna river, Andhra Pradesh. International Journal of Fisheries and Aquatic Studies 9(2), 2021, 180–184.
  Google Scholar

Kumar N. et al.: Remote Sensing and Machine Learning for Identification of Salt-affected Soils. Studies in Big Data 2021, 267–287.
  Google Scholar

Kumar P. et al.: Soil Salinity and Food Security in India. Frontiers in Sustainable Food Systems 4, 2020.
  Google Scholar

Madhu T. et al.: Mapping and Analysis of Wasteland in Machilipatnam Mandal, Krishna District, Andhra Pradesh, India by Using Geographical Information System. International Journal of Advanced Remote Sensing and GIS 4(1), 2015, 1435–1448.
  Google Scholar

Mandal A.: Modern Tools and Techniques for Diagnosis and Prognosis of Salt Affected Soils and Poor-Quality Waters. Current Investigations in Agriculture and Current Research 2(5), 2018.
  Google Scholar

Ramana Murty M. V. et al.: Monitoring of Coastal Geo-Environment for Hazard Mitigation: A Case Study of Machilipatnam Region, Andhra Pradesh, India. American Journal of Geospatial Technology 1(2), 2023, 27–38.
  Google Scholar

Rani A. et al.: Identification of salt-affected soils using remote sensing data through random forest technique: a case study from India. Arabian Journal of Geosciences 15(5), 2022.
  Google Scholar

Rouse J. W. et al.: Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication 351, 1974, 309.
  Google Scholar

Scudiero E. et al.: Remote sensing is a viable tool for mapping soil salinity in agricultural lands. California Agriculture 71(4), 2017, 231–38.
  Google Scholar

Shankarnarayan K. A. et al.: Agroforestry in the arid zones of India. Agroforestry Systems 5(1), 1987, 69–88.
  Google Scholar

Venkateshwarlu P. D. et al.: Marine Magnetic Indication of a Possible Submerged Volcano off Machilipatnam in Bay of Bengal. Journal of Geological Society of India 39, 1992, 197–203.
  Google Scholar

Wang J. et al.: Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma 353, 2019, 172–187.
  Google Scholar

Wang J. et al.: Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China. Remote Sensing 13(2), 2021, 305.
  Google Scholar

Wu W. et al.: Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq. Land Degradation & Development 29(11), 2018, 4005–4014.
  Google Scholar

Zhu S. et al.: Zeolite diagenesis and its control on petroleum reservoir quality of Permian in northwestern margin of Junggar Basin, China. Science China Earth Sciences 55(3), 2012, 386–396.
  Google Scholar


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

Statystyki

Abstract views: 174
PDF downloads: 75


Licencja

Creative Commons License

Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa 4.0 Międzynarodowe.