Recent advances in machine/deep learning frameworks for biochar application in soil amendment and remediation

Document Type

Article

Department

Faculty of Arts and Sciences

Abstract

Global industrialization has substantially increased carbon emissions, creating an urgent need for sustainable environmental remediation and carbon sequestration strategies. Biochar (BC), a carbon-rich product derived from thermochemical conversion of biomass, exhibits variable physicochemical properties that critically influence its environmental performance. Despite growing interest in BC for soil amendment and pollutant removal, the systematic integration of advanced artificial intelligence (AI), including machine learning (ML) and deep learning (DL), to optimize BC production and application remains underexplored. This review systematically analyzes recent supervised, unsupervised, and hybrid ML/DL frameworks, including random forests, support vector machines, convolutional neural networks, Long Short-Term Memory models, and generative AI, to predict yields, physicochemical properties, and environmental applications of BC. Furthermore, applications of ML/DL frameworks are examined to identify patterns in BC performance in soil fertility enhancement and pollutant removal efficiency. In addition, life cycle assessment and cost-benefit analyses using AI frameworks are critically evaluated. Finally, this review identifies key challenges, including the scarcity of high-quality datasets, limited model interpretability, and high computational demands, among others, and proposes potential solutions through explainable AI frameworks and cross-disciplinary collaborations. The integration of ML/DL frameworks into BC research provides actionable insights for optimizing production processes, improving soil remediation strategies, and accelerating sustainable agricultural applications.

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AKU Student

no

Publication (Name of Journal)

Applied Soil Ecology

DOI

doi.org/10.1016/j.apsoil.2026.106908

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