Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer
Soil texture analysis is critical for advancing agricultural productivity, ensuring environmental sustainability, and maintaining ecosystem balance. Traditional sedimentation-based methods, such as the hydrometer technique, are fast and practical but prone to inaccuracies due to the effects of water...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-01-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2663.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576003727163392 |
|---|---|
| author | Emre Kilinc Umut Orhan |
| author_facet | Emre Kilinc Umut Orhan |
| author_sort | Emre Kilinc |
| collection | DOAJ |
| description | Soil texture analysis is critical for advancing agricultural productivity, ensuring environmental sustainability, and maintaining ecosystem balance. Traditional sedimentation-based methods, such as the hydrometer technique, are fast and practical but prone to inaccuracies due to the effects of water-soluble substances. This study focuses on the practical framework of integrating pH (potential of hydrogen) and EC (electrical conductivity), as indicators of dissolved substances that influence soil texture estimation. Using the Ultrasound Penetration-based Digital Soil Texture Analyzer (USTA), this research combined ultrasound time series data with pH and EC measurements to predict sand, silt, and clay ratios through machine learning methods—support vector regression (SVR), Random Forest (RF), and multi-layer perceptron neural network (MLPNN). Simulations showed that RF yielded the best results, improving R2 values to 0.52, 0.33, and 0.31 for sand, silt, and clay, respectively. The enhanced model performance demonstrates the viability of integrating pH and EC with advanced machine learning techniques to improve soil texture analysis accuracy. These findings suggest that automated systems like USTA, with modular pH and EC sensors, can provide cost-effective, efficient alternatives to traditional methods, offering practical implications for soil management and agricultural optimization. |
| format | Article |
| id | doaj-art-92034e6947454211a830355d768bae71 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-92034e6947454211a830355d768bae712025-01-31T15:05:11ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e266310.7717/peerj-cs.2663Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzerEmre Kilinc0Umut Orhan1Computer Programming/Patnos Vocational High School, Agri İbrahim Cecen University, Agri, TurkeyComputer Engineering/Faculty of Engineering, Cukurova University, Adana, TurkeySoil texture analysis is critical for advancing agricultural productivity, ensuring environmental sustainability, and maintaining ecosystem balance. Traditional sedimentation-based methods, such as the hydrometer technique, are fast and practical but prone to inaccuracies due to the effects of water-soluble substances. This study focuses on the practical framework of integrating pH (potential of hydrogen) and EC (electrical conductivity), as indicators of dissolved substances that influence soil texture estimation. Using the Ultrasound Penetration-based Digital Soil Texture Analyzer (USTA), this research combined ultrasound time series data with pH and EC measurements to predict sand, silt, and clay ratios through machine learning methods—support vector regression (SVR), Random Forest (RF), and multi-layer perceptron neural network (MLPNN). Simulations showed that RF yielded the best results, improving R2 values to 0.52, 0.33, and 0.31 for sand, silt, and clay, respectively. The enhanced model performance demonstrates the viability of integrating pH and EC with advanced machine learning techniques to improve soil texture analysis accuracy. These findings suggest that automated systems like USTA, with modular pH and EC sensors, can provide cost-effective, efficient alternatives to traditional methods, offering practical implications for soil management and agricultural optimization.https://peerj.com/articles/cs-2663.pdfpH and electro conductivity on soil analysisUltrasound penetration-based digital soil texture analyzerTime seriesDetection of water-soluble substancesMachine learningSupport vector regression |
| spellingShingle | Emre Kilinc Umut Orhan Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer PeerJ Computer Science pH and electro conductivity on soil analysis Ultrasound penetration-based digital soil texture analyzer Time series Detection of water-soluble substances Machine learning Support vector regression |
| title | Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer |
| title_full | Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer |
| title_fullStr | Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer |
| title_full_unstemmed | Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer |
| title_short | Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer |
| title_sort | improving the accuracy of soil texture determination using ph and electro conductivity values with ultrasound penetration based digital soil texture analyzer |
| topic | pH and electro conductivity on soil analysis Ultrasound penetration-based digital soil texture analyzer Time series Detection of water-soluble substances Machine learning Support vector regression |
| url | https://peerj.com/articles/cs-2663.pdf |
| work_keys_str_mv | AT emrekilinc improvingtheaccuracyofsoiltexturedeterminationusingphandelectroconductivityvalueswithultrasoundpenetrationbaseddigitalsoiltextureanalyzer AT umutorhan improvingtheaccuracyofsoiltexturedeterminationusingphandelectroconductivityvalueswithultrasoundpenetrationbaseddigitalsoiltextureanalyzer |