Statistical Modelling for Precision Agriculture: A Case Study in Optimal Environmental Schedules for Agaricus Bisporus Production via Variable Domain Functional Regression

26 Pages Posted: 8 Aug 2017

See all articles by Efstathios Panayi

Efstathios Panayi

University College London - Financial Computing and Analytics Group, Department of Computer Science

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

George Kyriakides

Kyiakides Mushrooms Ltd.

Date Written: August 2, 2017

Abstract

Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields.

Keywords: Functional Regression, Variable Domain Functional Regression, Precision Agriculture, Yield Models

Suggested Citation

Panayi, Efstathios and Peters, Gareth and Kyriakides, George, Statistical Modelling for Precision Agriculture: A Case Study in Optimal Environmental Schedules for Agaricus Bisporus Production via Variable Domain Functional Regression (August 2, 2017). Available at SSRN: https://ssrn.com/abstract=3012709 or http://dx.doi.org/10.2139/ssrn.3012709

Efstathios Panayi

University College London - Financial Computing and Analytics Group, Department of Computer Science ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Gareth Peters (Contact Author)

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

George Kyriakides

Kyiakides Mushrooms Ltd. ( email )

Apostolou Louka 1, Kolossi
P.O. Box 56834
Limassol, 3310
Cyprus

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