Artificial Intelligence as a Catalyst for Soil Classification: Deep Learning

Abstract

Soil classification is a foundational process in agriculture, environmental management, geotechnical engineering, and land-use planning because it determines soil suitability, fertility, productivity, and ecological sustainability. Conventional soil classification approaches depend heavily on field sampling, laboratory analysis, and expert interpretation, which are often labor-intensive, time-consuming, and difficult to scale across large geographic regions. Recent advances in Artificial Intelligence (AI), particularly Deep Learning (DL), have transformed soil classification by enabling automated extraction of complex patterns from heterogeneous datasets including soil images, hyperspectral data, sensor measurements, and geospatial information. This review examines the role of AI as a catalyst for modern soil classification, focusing on deep learning architectures, emerging trends, applications, challenges, and future directions. Evidence from recent studies demonstrates that convolutional neural networks (CNNs), transformer-based models, hybrid architectures, and multimodal learning frameworks substantially improve classification accuracy and operational efficiency compared with traditional approaches. However, limitations associated with data availability, explainability, computational cost, and model transferability remain major concerns.