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AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

dc.contributor.authorMuhammad Awais
dc.contributor.authorSyed Muhammad Zaigham Abbas Naqvi
dc.contributor.authorHao Zhang
dc.contributor.authorLinze Li
dc.contributor.authorWei Zhang
dc.contributor.authorFuad A. Awwad
dc.contributor.authorEmad A. A. Ismail
dc.contributor.authorM. Ijaz Khan
dc.contributor.authorVijaya Raghavan
dc.contributor.authorJiandong Hu
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Henan Agricultural University
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Henan Agricultural University
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Henan Agricultural University
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Henan Agricultural University
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Henan Agricultural University
dc.contributor.otherDepartment of Quantitative Analysis, College of Business Administration, King Saud University
dc.contributor.otherDepartment of Quantitative Analysis, College of Business Administration, King Saud University
dc.contributor.otherDepartment of Mathematics and Statistics, Riphah International University
dc.contributor.otherDepartment of Bioresource Engineering, Faculty of Agriculture and Environmental Studies, McGill University
dc.contributor.otherCollege of Mechanical and Electrical Engineering, Henan Agricultural University
dc.date.accessioned2023-12-10T12:05:35Z
dc.date.accessioned2025-10-08T08:27:25Z
dc.date.available2025-10-08T08:27:25Z
dc.date.issued01-12-2023
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/35955
dc.description.abstractAbstract Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher’s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database. Graphical Abstract
dc.language.isoEN
dc.publisherSpringerOpen
dc.subject.lccTechnology
dc.titleAI and machine learning for soil analysis: an assessment of sustainable agricultural practices
dc.typeArticle
dc.description.keywordsIntelligent agriculture
dc.description.keywordsAgronomic forecasting
dc.description.keywordsSoil texture
dc.description.keywordsWater content analysis
dc.description.keywordsSmarter agriculture 4.0
dc.description.pages1-16
dc.description.doi10.1186/s40643-023-00710-y
dc.title.journalBioresources and Bioprocessing
dc.identifier.e-issn2197-4365
dc.identifier.oai73600dee20984f52bb686ad90314eb85
dc.journal.infoVolume 10, Issue 1


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