Abstract
This paper proposed an algorithm to predict soil pH value using ultra-wideband (UWB) radar echoes. Compared with the existing work, instead of classifying soil pH value via echoes, this paper predicted soil pH value using machine learning (ML) method—extreme gradient boosting (XGBoost) at the first time as far as we know. In this experiment, we collected a total of 7 types of soil UWB radar echoes with different pH values. The echoes were split into train set and test set. The prediction results were compared with actual pH values via mean squared errors (MSE). Analysis results show that this method can achieve a very low MSE that is \(3.6\times 10^{-7}\).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (61671138, 61731006), and was partly supported by the 111 Project No. B17008.
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Wang, T., Yang, C., Liang, J. (2020). Soil pH Value Prediction Using UWB Radar Echoes Based on XGBoost. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_235
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DOI: https://doi.org/10.1007/978-981-13-9409-6_235
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