Predicting Soil pH by Using Nearest Fields

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Title: Predicting Soil pH by Using Nearest Fields
Authors: Ngo, Quoc HungLe-Khac, Nhien-AnKechadi, 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
Origin Enterprises
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 & statisticsSoil predictionRegression techniquesPrecision agricultureData 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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