1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. URL:https://www.tensorflow.org/ software available from tensorflow.org.
2. Toward anthropomorphic machine learning;Angelov;Computer,2018
3. Anjos, L., Gaistardo, C.C., Deckers, J., Dondeyne, S., Eberhardt, E., Gerasimova, M., Harms, B., Jones, A., Krasilnikov, P., Reinsch, T., Vargas, R., Zhang, G.-l., 2015. World reference base for soil resources 2014. Rome, Italy: FAO. URL:http://www.fao.org/3/i3794en/I3794en.pdf.
4. Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils – critical review and research perspectives;Bellon-Maurel;Soil Biol. Biochem.,2011
5. FCM: The fuzzy c-means clustering algorithm;Bezdek;Comput. Geosci.,1984