Predicting Soil pH by Using Nearest Fields
|Title:||Predicting Soil pH by Using Nearest Fields||Authors:||Ngo, Quoc Hung; Le-Khac, Nhien-An; Kechadi, Tahar||Permanent link:||http://hdl.handle.net/10197/12205||Date:||19-Dec-2019||Online since:||2021-05-26T10:50:41Z||Abstract:||In 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.||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre
|Type of material:||Conference Publication||Publisher:||Springer||Series/Report no.:||Lecture Notes in Computer Science; 11927; Lecture Notes in Artificial Intelligence; 11927||Copyright (published version):||2019 Springer||Keywords:||Machine learning & statistics; Soil prediction; Regression techniques; Precision agriculture; Data mining||DOI:||10.1007/978-3-030-34885-4_40||Language:||en||Status of Item:||Peer reviewed||Is part of:||Bramer, M., Petridis, M. (eds.). Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceeding||Conference Details:||The 39th SGAI International Conference on Artificial Intelligence (AI 2019), Cambridge, United Kingdom, 17-19 December 2019||ISBN:||978-3-030-34884-7||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Insight Research Collection|
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