Soil biopore genesis is a dynamic and context-dependent process. Yet integrative investigations of biopore genesis under varying soil type, tillage and vegetation history are rare. Recent advances in Machine Learning (ML) made faster and more accurate image analysis possible. We validated a model trained on Convolutional Neural Network (CNN) using a multisite dataset from varying soil types (Luvisol, Cambisol and Kandosol), tillage (deep ploughing and without deep ploughing) and vegetation history (taprooted and fibrous-rooted crops) to automatically predict biopore formation. The model trained on the multisite dataset outperformed individually trained single-site models, especially when the dataset contained images with noise and/or fewer biopores. Our model successfully replicated previously established treatment effects but provided new insights at more detailed scales and for different pore-size classes. These insights demonstrated that effects of deep ploughing on soil biopores can persist for more than 50 years and are more pronounced on the Luvisol rather than the Cambisol soil type. The effects of perennial fodder crops with high biopore generating capacity were also shown to persist for at least a decade. re-growing the same fodder crops or a mixture with grass had no further impact on biopore density but generated a shift in pore-size classes from large to smaller biopores. We suspect this is likely to have resulted from three possible scenarios; (1) newly created fine pores (1–4 mm); (2) blockage of large-sized pores by earthworm faeces; (3) decrease in pore diameter. In summary, by using a single robust model trained on the multisite dataset, we were able to generate new insights on pore-size distribution as affected by site, vegetation, and deep ploughing. We have demonstrated that Deep Learning-based image analysis can provide easier biopore quantification and can generate models that provide novel insights across different research settings consistently and accurately.