Ngo, Quoc HungQuoc HungNgoLe-Khac, Nhien-AnNhien-AnLe-KhacKechadi, TaharTaharKechadi2021-05-262021-05-262019 Sprin2019-12-19978-3-030-34884-7http://hdl.handle.net/10197/12205The 39th SGAI International Conference on Artificial Intelligence (AI 2019), Cambridge, United Kingdom, 17-19 December 2019In precision agriculture (PA), soil sampling and testing op-eration is prior to planting any new crop. It is an expensive operationsince there are many soil characteristics to take into account. This papergives an overview of soil characteristics and their relationships with cropyield and soil profiling. We propose an approach for predicting soil pHbased on nearest neighbour fields. It implements spatial radius queriesand various regression techniques in data mining. We use soil dataset containing about 4,000 fields profiles to evaluate them and analyse theirrobustness. A comparative study indicates that LR, SVR, andGBRTtechniques achieved high accuracy, with the R2 values of about 0.718 and MAEvalues of 0.29. The experimental results showed that the pro-posed approach is very promising and can contribute significantly to PA.enThe final publication is available at www.springerlink.com.Machine learning & statisticsSoil predictionRegression techniquesPrecision agricultureData miningConference Publication10.1007/978-3-030-34885-4_402020-06-2316/SPP/3296https://creativecommons.org/licenses/by-nc-nd/3.0/ie/