Identification of soil type in Pakistan using remote sensing and machine learning
Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive labo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-10-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1109.pdf |
| _version_ | 1811243107178512384 |
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| author | Yasin Ul Haq Muhammad Shahbaz HM Shahzad Asif Ali Al-Laith Wesam Alsabban Muhammad Haris Aziz |
| author_facet | Yasin Ul Haq Muhammad Shahbaz HM Shahzad Asif Ali Al-Laith Wesam Alsabban Muhammad Haris Aziz |
| author_sort | Yasin Ul Haq |
| collection | DOAJ |
| description | Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning. |
| first_indexed | 2024-04-12T14:01:45Z |
| format | Article |
| id | doaj.art-1ff9663c16144fcda3fbaea96fd08515 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-12T14:01:45Z |
| publishDate | 2022-10-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-1ff9663c16144fcda3fbaea96fd085152022-12-22T03:30:10ZengPeerJ Inc.PeerJ Computer Science2376-59922022-10-018e110910.7717/peerj-cs.1109Identification of soil type in Pakistan using remote sensing and machine learningYasin Ul Haq0Muhammad Shahbaz1HM Shahzad Asif2Ali Al-Laith3Wesam Alsabban4Muhammad Haris Aziz5Department of Computer Science and Engineering, University of Engineering and Technology Lahore Narowal Campus, Narowal, PakistanDepartment of Computer Engineering, University of Engineering and Technology Lahore, Lahore, Punjab, PakistanDepartment of Computer Science, University of Engineering and Technology Lahore, Lahore, Kala Shah Kaku, Punjab, PakistanComputer Science Department, Copenhagen University, Copenhagen, DenmarkInformation Systems Department, Faculty of computer and Information Systems, Umm Al-Qura University, Makkah, Saudi ArabiaCollege of Engineering & Technology, University of Sargodha, Sargodha, Sargodha, PakistanSoil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.https://peerj.com/articles/cs-1109.pdfRemote sensingSpectral signaturesDigital soil mappingSoil typeRandom forest |
| spellingShingle | Yasin Ul Haq Muhammad Shahbaz HM Shahzad Asif Ali Al-Laith Wesam Alsabban Muhammad Haris Aziz Identification of soil type in Pakistan using remote sensing and machine learning PeerJ Computer Science Remote sensing Spectral signatures Digital soil mapping Soil type Random forest |
| title | Identification of soil type in Pakistan using remote sensing and machine learning |
| title_full | Identification of soil type in Pakistan using remote sensing and machine learning |
| title_fullStr | Identification of soil type in Pakistan using remote sensing and machine learning |
| title_full_unstemmed | Identification of soil type in Pakistan using remote sensing and machine learning |
| title_short | Identification of soil type in Pakistan using remote sensing and machine learning |
| title_sort | identification of soil type in pakistan using remote sensing and machine learning |
| topic | Remote sensing Spectral signatures Digital soil mapping Soil type Random forest |
| url | https://peerj.com/articles/cs-1109.pdf |
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