Application and prospect of artificial intelligence in biochar for acidic soil amelioration

Abstract

Abstract

Effective amelioration of acidic soils with biochar is critical for mitigating soil degradation and promoting sustainable agriculture, yet this requires robust mechanistic understanding and predictive modeling. This review synthesizes the key biogeochemical pathways by which biochar neutralizes soil acidity and enhances soil pH buffering capacity. We identify critical data gaps—notably the overlooked relative contributions of organic and inorganic alkalis—that currently limit the predictive performance of artificial intelligence (AI) models. A statistical literature analysis reveals that ensemble learning algorithms, particularly Random Forest, are predominant across various application scenarios, such as crop yield prediction, due to their robustness against overfitting and their capacity for feature importance ranking. However, no single algorithm is universally optimal. The choice of algorithm depends on several factors, including the amount of available data, the complexity of the problem, and the computational resources. Both multi-source data fusion and multi-model ensembling can enhance the predictive performance of AI models when appropriately configured. Future data fusion should prioritize integrating remote sensing with proximal sensor, multi-modal microbial, and imaging-chemical data to elucidate interactions across the biochar–soil–microbe–plant continuum. In conclusion, the predictive accuracy for biochar’s performance in acidic soil amelioration can be enhanced by: (1) integrating sensing technologies to develop new methods with high resolution and low detection limits; (2) applying AI while ensuring model interpretability and avoiding overclaims of causality; (3) constructing benchmark datasets to expand the usability of biochar data; (4) improving the representation of microbial responses to biochar in AI models; and (5) strengthening interdisciplinary collaboration among soil scientists, model developers, and sensing technology experts.

Graphical Abstract

Highlights

•	Ensemble learning, especially Random Forest, dominates AI modeling for predicting biochar efficacy in acidic soils.
•	Decoupling organic and inorganic alkalis is critical for advancing mechanism-based AI predictions.
•	Multi-source data fusion integrates sensing technologies to overcome data limitations and enhance model performance.

Keywords

Biochar / Soil acidification / Artificial intelligence / Random Forest / Multi-source data fusion

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