Increasing incorporation of artificial intelligence (AI) within soil science has resulted in the origin new frontiers on both predictive and prescriptive basis, including management scheduling optimization, soil property forecasting and site-specific recommendations. Hybrid artificial intelligence (HAI) models combine the spatial feature extraction feature of solitary models with the temporal modelling attributes of large-spectrum models. HAI models provide active decision support, recommending fertilizer rates, irrigation schedules and land management practices calibrated to site-specific, climate and crop conditions. Bibliographic analysis of published literature from the past decade reveals a rapidly globalizing research landscape. With higher adaptation rate of AI-based technologies, HAI technologies have shifted from a niche sector to globally embraced, multi-dimensional research enterprise. The convergence of hybrid architectures, explainable modelling frameworks and expanding international collaborations signals that digital soil systems are no longer a theoretical concept but an emerging operational reality.
Lal, A., Sharma, R.P., 2026. Hybrid AI Models for Predictive and Prescriptive Soil Analytics. Biotica Research Today 8(2), 27-29. DOI: 10.54083/BRT/08.02.26/27-29.
Deep learning, Machine learning, Predictive soil management, Prescriptive soil management
Ding, Z., Liu, K., Grunwald, S., Smith, P., Ciais, P., Wang, B., Harrison, M.T., 2025. Advancing soil organic carbon prediction: A comprehensive review of technologies, AI, process‐based and hybrid modelling approaches. Advanced Science 12(31), e04152. DOI: https://doi.org/10.1002/advs.202504152.
Kammerlander, C., Kolb, V., Luegmair, M., Scheermann, L., Schmailzl, M., Seufert, M., Schön, T., 2025. Machine learning models for soil parameter prediction based on satellite, weather, clay and yield data. arXiv [preprint arXiv:2503.22276]. DOI: https://doi.org/10.48550/arXiv.2503.22276.
Ugwu, O.P.C., Ogenyi, F.C., Alum, E.U., Eze, V.H.U., Basajja, M., Ugwu, J.N., Ejim, U.D., 2025. Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability. Cogent Food & Agriculture 11(1), e2569982. DOI: https://doi.org/10.1080/23311932.2025.2569982.
Venkateswara, S.M., Padmanaban, J., 2025. Interpretable deep learning models for independent fertilizer and crop recommendation. Scientific Reports 15(1), 41721. DOI: https://doi.org/10.1038/s41598-025-26910-4.
Zhang, L., Heuvelink, G.B., Mulder, V.L., Chen, S., Deng, X., Yang, L., 2024. Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time. Science of the Total Environment 922, e170778. DOI: https://doi.org/10.1016/j.scitotenv.2024.170778.
