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. 2017 Sep 29;12(9):e0181921.
doi: 10.1371/journal.pone.0181921. eCollection 2017.

Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression

Efstathios Panayi  1 , Gareth W Peters  1 , George Kyriakides  1   2
Affiliations

Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression

Efstathios Panayi et al. PLoS One. .

Erratum in

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.

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Conflict of interest statement

Competing Interests: We have the following interests: George Kyriakides is employed by Kyiakides Mushrooms Ltd. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Fig 1
Fig 1. A photo of a typical growing room at Kyriakides Mushrooms.
Fig 2
Fig 2. The smoothed density of the total yields in the two growing groups.
The absolute values are obscured due to commercial considerations.
Fig 3
Fig 3. The basis functions used in our temperature curve representation.
Note the x-axis reflects the time index for each 30min observation interval.
Fig 4
Fig 4. Cubic B-splines fit to the temperature time-series data (points) for a single growing period.
Note the x-axis reflects the time index for each 30min observation interval.
Fig 5
Fig 5. Raw oxygen data.
Blue lines come from growing processes in group G1 and red lines from group G2. Note the x-axis reflects the time index for each 30min observation interval.
Fig 6
Fig 6. Raw temperature data.
Blue lines come from growing processes in group G1 and red lines from group G2. Note the x-axis reflects the time index for each 30min observation interval.
Fig 7
Fig 7. Mean oxygen data.
Spline was fit with K = 15 knot points. Note the x-axis reflects the time index for each 30min observation interval.
Fig 8
Fig 8. Mean temperature data.
Spline was fit with K = 20 knot points. Note the x-axis reflects the time index for each 30min observation interval.
Fig 9
Fig 9. The estimated coefficient surface from the VDFR with a single functional regressor (oxygen).
Note the t-axis reflects the time index for each 30min observation interval. The T-axis reflects the index of the number of days for which the production process was performed before harvesting for the given experiment.
Fig 10
Fig 10. Cuts through the surface in Fig 9.
Note the x-axis reflects the time index for each 30min observation interval.
Fig 11
Fig 11. Raw residuals—Co2.
Fig 12
Fig 12. Raw residuals—Humidity deficit.
Fig 13
Fig 13. The estimated coefficient surface from the VDFR with a single functional regressor (air temperature).
Note the t-axis reflects the time index for each 30min observation interval. The T-axis reflects the length of time before harvest for the given growing trial.
Fig 14
Fig 14. Cuts through this surface of the estimated coefficient surface from the VDFR with a single functional regressor (air temperature).
Note the t-axis reflects the time index for each 30min observation interval. The T-axis reflects the length of time before harvest for the given growing trial.
Fig 15
Fig 15. Cuts through the estimated coefficient surface for airtemperature from the VDFR with two functional regressors (airtemperature and oxygen).
Adjusted r-squared for this regression is 0.175. Note the x-axis reflects the time index for each 30min observation interval.
Fig 16
Fig 16. Cuts through the estimated coefficient surface for oxygen from the VDFR with two functional regressors (airtemperature and oxygen).
Adjusted r-squared for this regression is 0.175. Note the x-axis reflects the time index for each 30min observation interval.
Fig 17
Fig 17. Raw residuals—Airtemperature.
Fig 18
Fig 18. Raw residuals—Oxygen.
See this image and copyright information in PMC

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