Authors : Faiz Akram; Anurag Bharti; Ranjit Choudhary; Shafaque Aziz; Pawan Kumar

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/2mebex3u

Scribd : https://tinyurl.com/5heryr62

DOI : https://doi.org/10.38124/ijisrt/26apr1966

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Soil property prediction plays a critical role in agriculture, environmental science, and geotechnical engineering, yet traditional methods often face challenges in scalability and accuracy. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to address these limitations, offering data-driven solutions for diverse soil-related tasks. This systematic literature review examines the current state of ML applications in soil property prediction, focusing on seven key dimensions: general soil property prediction, specific physical properties, soil mapping, crop-related predictions, chemical properties and contaminants, slope stability and flood prediction, and uncertainty evaluation. We synthesize existing research to identify trends, methodologies, and gaps in the field, then analyze how different ML techniques perform across these dimensions. The review highlights the dominance of ensemble methods and neural networks in handling nonlinear soil data relationships, while also revealing inconsistencies in model evaluation metrics and data preprocessing practices. Spatial and temporal variability in soil datasets often complicates model generalizability, hence we discuss strategies to improve robustness. Despite advancements, challenges such as interpretability, data scarcity, and integration with domain knowledge persist. The findings suggest that hybrid models combining ML with physical principles may offer a promising direction for future research. By consolidating insights from diverse studies, this review provides a comprehensive foundation for researchers and practitioners aiming to advance ML applications in soil science.

Keywords : Soil Property, Machine Learning, Deep Learning, Literature Review, Smart Agriculture.

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