Evaluation of soil texture classification from orthodox interpolation and machine learning techniques

Lei Feng; Umer Khalil; Bilal Aslam; Bushra Ghaffar; Aqil Tariq; Ahsan Jamil; Muhammad Farhan; Muhammad Aslam; Walid Soufan

doi:10.1016/j.envres.2023.118075

Evaluation of soil texture classification from orthodox interpolation and machine learning techniques

Environ Res. 2024 Apr 1:246:118075. doi: 10.1016/j.envres.2023.118075. Epub 2023 Dec 28.

Authors

Lei Feng¹, Umer Khalil², Bilal Aslam³, Bushra Ghaffar⁴, Aqil Tariq⁵, Ahsan Jamil⁶, Muhammad Farhan⁷, Muhammad Aslam⁸, Walid Soufan⁹

Affiliations

¹ Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; College of Environment and Ecology, Chongqing University, Chongqing, China.
² ITC Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the Netherlands.
³ Department of Earth Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
⁴ Department of Environmental Science, Faculty of Sciences, International Islamic University, Islamabad, Pakistan.
⁵ Department of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS, 39762-9690, USA. Electronic address: at2139@msstate.edu.
⁶ Department of Plant and Environmental Sciences, New Mexico State University, 3170S Espina Str., Las Cruces, NM, 88003, USA.
⁷ School of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, China.
⁸ Department of Computer Science, Aberystwyth University, UK.
⁹ Plant Production Department, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh, 11451, Saudi Arabia.

PMID: 38159666
DOI: 10.1016/j.envres.2023.118075

Abstract

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.

Keywords: Artificial neural networks; Interpolation methods; MATLAB; Precision; Soil texture.

MeSH terms

Agriculture
Clay
Environmental Monitoring / methods
Sand*
Soil*

Substances

Soil
Clay
Sand