Статья

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

Muhammad AwaisCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, ChinaSyed Muhammad Zaigham Abbas NaqviCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, ChinaHao ZhangCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, ChinaLinze LiCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, ChinaWei ZhangCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, ChinaFuad A. AwwadDepartment of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi ArabiaEmad A. A. IsmailDepartment of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi ArabiaM. Ijaz KhanDepartment of Mathematics and Statistics, Riphah International University, I-14, Islamabad, 44000, PakistanVijaya RaghavanDepartment of Bioresource Engineering, Faculty of Agriculture and Environmental Studies, McGill University, Sainte-Anne-de-Bellevue, QC, H9X 3V9, CanadaJiandong HuCollege of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China. [email protected]

2023en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1186/s40643-023-00710-y

Цитирования и источники

Цитирований: 2Использованных источников: 0

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