Analyzing Soil Pollution by Image Processing and Machine Learning at Contaminated Agricultural Field

Year 2024, Volume: 9 Issue: 2, 335 - 346, 30.10.2024

Priya Vij , Patil Manisha Prashant

https://doi.org/10.28978/nesciences.1575484

Abstract

Due to the fast advancement of big data, applying Machine Learning (ML) techniques to detect Soil Pollution (SP) at Potentially Contaminated Sites (PCS) across many sectors and regional sizes has emerged as a prominent research focus. The challenges in acquiring essential indices of SP sources and routes result in present methodologies exhibiting low predictive accuracy and an inadequate scientific foundation. This study gathered environmental data concerning heavy metal and organic contamination from 200 PCS across six representative sectors. Twenty-one indices derived from fundamental data, potential SP from products and materials, SP efficacy, and the migrating capability of SP were employed to build the SP detection index method. The research integrated the score into the new characteristic group, including 11 indicators using consolidation computation. The newly selected feature subset was utilized for training ML designs, including Random Forests (RF), Support Vector Machines (SVM), and Multilayer Perceptrons (MLP), and evaluated to ascertain its impact on SP recognition methods. The study findings indicated that the four newly developed indices by feature fusion exhibit an association with SP comparable to that of the original index. The component analysis suggests that several indices related to fundamental information, contamination potential from products and raw materials, and SP prevention levels significantly influence SP to varying extents. The index of the migratory capability of soil contaminants has minimal influence on the classification job of SP detection inside PCS. This research introduces a novel technological approach for identifying SP via big data and ML techniques while offering an overview and scientific foundation for PCS's environmental administration and SP mitigation.

Keywords

Soil pollution, image processing, machine learning, agriculture

References

• Angin, P., Anisi, M.H., Göksel, F., Gürsoy, C., & Büyükgülcü, A. (2020). Agrilora: a digital twin framework for smart agriculture. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 11(4), 77-96.
• Askari, M. S., Alamdari, P., Chahardoli, S., & Afshari, A. (2020). Quantification of heavy metal pollution for environmental assessment of soil condition. Environmental Monitoring and Assessment, 192, 1-17.
• Baier, C., Modersohn, A., Jalowy, F., Glaser, B., & Gross, A. (2022). Effects of recultivation on soil organic carbon sequestration in abandoned coal mining sites: a meta-analysis. Scientific Reports, 12(1), 20090. https://doi.org/10.1038/s41598-022-22937-z
• Camgözlü, Y., & Kutlu, Y. (2023). Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Natural and Engineering Sciences, 8(3), 214-232.
• Danková, Z., Štyriaková, I., Kovaničová, Ľ., Čechovská, K., Košuth, M., Šuba, J., Nováková, J., Konečný, P., Tuček, Ľ., Žecová, K., Lenhardtová, E., & Németh, Z. (2021). Chemical Leaching of Contaminated Soil – Case Study. Archives for Technical Sciences, 1(24), 65–72.
• Deiss, L., Margenot, A. J., Culman, S. W., & Demyan, M. S. (2020). Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy. Geoderma, 365, 114227. https://doi.org/10.1016/j.geoderma.2020.114227
• Elizondo-Martinez, E. J., Andres-Valeri, V. C., Rodriguez-Hernandez, J., & Sangiorgi, C. (2020). Selection of additives and fibers for improving the mechanical and safety properties of porous concrete pavements through multi-criteria decision-making analysis. Sustainability, 12(6), 2392. https://doi.org/10.3390/su12062392
• Hu, B., Shao, S., Ni, H., Fu, Z., Huang, M., Chen, Q., & Shi, Z. (2021). Assessment of potentially toxic element pollution in soils and related health risks in 271 cities across China. Environmental Pollution, 270, 116196. https://doi.org/10.1016/j.envpol.2020.116196
• Huang, Y., Harilal, S. S., Bais, A., & Hussein, A. E. (2023). Progress toward machine learning methodologies for laser-induced breakdown spectroscopy with an emphasis on soil analysis. IEEE Transactions on Plasma Science, 51(7), 1729-1749.
• Jas, K., & Dodagoudar, G. R. (2023). Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP. Soil Dynamics and Earthquake Engineering, 165, 107662. https://doi.org/10.1016/j.soildyn.2022.107662
• Khan, S., Naushad, M., Lima, E. C., Zhang, S., Shaheen, S. M., & Rinklebe, J. (2021). Global soil pollution by toxic elements: Current status and future perspectives on the risk assessment and remediation strategies–A review. Journal of Hazardous Materials, 417, 126039. https://doi.org/10.1016/j.jhazmat.2021.126039
• Li, K., & Sun, R. (2024). Understanding the driving mechanisms of site contamination in China through a data-driven approach. Environmental Pollution, 342, 123105. https://doi.org/10.1016/j.envpol.2023.123105
• Li, X., Wu, Y., Leng, Y., Xiu, D., Pei, N., Li, S., & Tian, Y. (2023). Risk assessment, spatial distribution, and source identification of heavy metals in surface soils in Zhijin County, Guizhou Province, China. Environmental Monitoring and Assessment, 195(1), 132. https://doi.org/10.1007/s10661-022-10674-9
• Maurya, S., Abraham, J. S., Somasundaram, S., Toteja, R., Gupta, R., & Makhija, S. (2020). Indicators for assessment of soil quality: a mini-review. Environmental Monitoring and Assessment, 192, 1-22.
• Mohamed, S., Kumaran, U., & Rakesh, N. (2024). An Approach towards Forecasting Time Series Air Pollution Data Using LSTM-based Auto-Encoders. Journal of Internet Services and Information Security, 14(2), 32-46.
• Mosavi, A., Samadianfard, S., Darbandi, S., Nabipour, N., Qasem, S. N., Salwana, E., & Band, S. S. (2021). Predicting soil electrical conductivity using multi-layer perceptron integrated with grey wolf optimizer. Journal of Geochemical Exploration, 220, 106639. https://doi.org/10.1016/j.gexplo.2020.106639
• Obiri-Nyarko, F., Duah, A. A., Karikari, A. Y., Agyekum, W. A., Manu, E., & Tagoe, R. (2021). Assessment of heavy metal contamination in soils at the Kpone landfill site, Ghana: Implication for ecological and health risk assessment. Chemosphere, 282, 131007. https://doi.org/10.1016/j.chemosphere.2021.131007
• Paul, P. K., Sinha, R. R., Aithal, P. S., Aremu, B., & Saavedra, R. (2020). Agricultural Informatics: An Overview of Integration of Agricultural Sciences and Information Science. Indian Journal of Information Sources and Services, 10(1), 48–55.
• Ramesh, D., & Sanampudi, S. K. (2022). An automated essay scoring systems: a systematic literature review. Artificial Intelligence Review, 55(3), 2495-2527
• Ray, A., Kumar, V., Kumar, A., Rai, R., Khandelwal, M., & Singh, T. N. (2020). Stability prediction of Himalayan residual soil slope using artificial neural network. Natural Hazards, 103(3), 3523-3540.
• Van Der Westhuizen, S., Heuvelink, G. B., & Hofmeyr, D. P. (2023). Multivariate random forest for digital soil mapping. Geoderma, 431, 116365. https://doi.org/10.1016/j.geoderma.2023.116365
• Vinante, C., Sacco, P., Orzes, G., & Borgianni, Y. (2021). Circular economy metrics: Literature review and company-level classification framework. Journal of Cleaner Production, 288, 125090. https://doi.org/10.1016/j.jclepro.2020.125090
• Wood, S. A., & Blankinship, J. C. (2022). Making soil health science practical: guiding research for agronomic and environmental benefits. Soil Biology and Biochemistry, 172, 108776. https://doi.org/10.1016/j.soilbio.2022.108776
• Ye, M., Zhu, L., Li, X., Ke, Y., Huang, Y., Chen, B., & Feng, H. (2023). Estimation of the soil arsenic concentration using a geographically weighted XGBoost model based on hyperspectral data. Science of The Total Environment, 858, 159798. https://doi.org/10.1016/j.scitotenv.2022.159798
• Zhai, H., Lv, C., Liu, W., Yang, C., Fan, D., Wang, Z., & Guan, Q. (2021). Understanding spatio-temporal patterns of land use/land cover change under urbanization in Wuhan, China, 2000–2019. Remote Sensing, 13(16), 3331. https://doi.org/10.3390/rs13163331