An evaluation of machine learning for soil analysis in internet of things-enabled smart farming.

Soil plays a foundational role in sustaining agricultural productivity and ecological stability, yet traditional soil analysis methods remain labour-intensive, slow, and often inadequate for real-time decision-making in modern precision agriculture. With the rise of Agriculture 4.0, machine learning (ML) and Internet of Things (IoT) technologies offer transformative potential for accurate, scalable, and data-driven soil assessment. Current research in this domain remains relatively fragmented, with a lack of clarity in models, sensing methodologies, and algorithmic strategies that produce the highest accuracy and operational value across diverse agricultural contexts. To address this gap, this research used a PRISMA-guided systematic literature review to systematically examine contemporary machine learning (ML) and IoT-based soil analysis methodologies. The review utilized sophisticated search queries across databases: Scopus, IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar, for identifying studies published between 2019 and 2024. After rigorous screening that involved removing duplication and full-text assessment of the retrieved entries, 77 high-quality articles were identified that met the eligibility criteria from an initial set of 180 entries. Data extraction was performed under descriptive and thematic synthesis, thereby allowing the comparative evaluation of supervised and unsupervised learning models, IoT sensing frameworks, soil parameters, dataset characteristics, evaluation metrics, and deployment constraints. Comparative analysis revealed that the use of supervised models, such as Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Convolutional Neural Networks (CNN), and deep ensembles, produces higher accuracy in the classification of soil quality, fertility, pH, and nutrient levels, especially in structured datasets like the Soil Fertility Dataset. IoT-based sensing systems significantly improve the reliability of predictions by offering continuous and detailed measurements of soil moisture, nutrient status, and environmental conditions.