Comparison of Machine Learning Regression Methods to Simulate NO<sub>3</sub> Flux in Soil Solution under Potato Crops

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ISSN Print: 2152-7385
ISSN Online: 2152-7393
www.scirp.org/journal/am
E-mail: am@scirp.org

"Comparison of Machine Learning Regression Methods to Simulate NO₃ Flux in Soil Solution under Potato Crops"
written by J. G. Fortin, A. Morais, F. Anctil, L. E. Parent,
published by Applied Mathematics, Vol.5 No.5, 2014

has been cited by the following article(s):

[1]	Outputs: potassium losses from agricultural systems
	2021

[2]	A Low-Complexity Machine Learning Nitrate Loss Predictive Model–Towards Proactive Farm Management in a Networked Catchment
	2019

[3]	SVMLEACH–NK POTATO: A simple software tool to simulate nitrate and potassium co-leaching under potato crop
	Computers and Electronics in Agriculture, 2015

[4]	Data-driven low-complexity nitrate loss model utilizing sensor information–towards collaborative farm management with wireless sensor networks
	2015

[5]	Predicting discharge using a low complexity machine learning model
	Computers and Electronics in Agriculture, 2015

[6]	Enabling proactive agricultural drainage reuse for improved water quality through collaborative networks and low-complexity data-driven modelling
	2015

[7]	Data-driven low-complexity nitrate loss model utilizing sensor information—Towards collaborative farm management with wireless sensor networks
	2015

[1]	Improving Potassium Recommendations for Agricultural Crops 2021 DOI:10.1007/978-3-030-59197-7_3

[2]	A Low-Complexity Machine Learning Nitrate Loss Predictive Model–Towards Proactive Farm Management in a Networked Catchment IEEE Access, 2019 DOI:10.1109/ACCESS.2019.2901218

[3]	SVMLEACH – NK POTATO: A simple software tool to simulate nitrate and potassium co-leaching under potato crop Computers and Electronics in Agriculture, 2015 DOI:10.1016/j.compag.2014.11.025

[4]	Predicting discharge using a low complexity machine learning model Computers and Electronics in Agriculture, 2015 DOI:10.1016/j.compag.2015.09.012

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