Difference between revisions of "Journal:GeoFIS: An open-source decision support tool for precision agriculture data"

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'''Keywords''': decision making, GeoFIS, geostatistics, open-source software, precision agriculture, spatial analysis
'''Keywords''': decision making, GeoFIS, geostatistics, open-source software, precision agriculture, spatial analysis


==Introduction==
Within-field variability is now a widely accepted and reported phenomenon by the precision agriculture community.<ref name="OliverGeo10">{{cite book |title=Geostatistical Applications for Precision Agriculture |editor=Oliver, M.A. |publisher=Springer |pages=331 |year=2010 |isbn=9789048191321 |doi=10.1007/978-90-481-9133-8}}</ref><ref name="PringleAPrelim03">{{cite journal |title=A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data |journal=Agricultural Systems |author=Pringle, M.J.; McBratney, A.B.; Whelan, B.M.; Taylor, J.M. |volume=76 |issue=1 |pages=273–92 |year=2003 |doi=10.1016/S0308-521X(02)00005-7}}</ref> Geolocalized data are effectively collected intensively within the fields by sensors embedded on agricultural machinery, satellites, flying platforms, static stations, or humans among others, to make sure that this variability is considered and accounted for.<ref name="Acevedo-OpazoThePot08">{{cite journal |title=The potential of high spatial resolution information to define within-vineyard zones related to vine water status |journal=Precision Agriculture |author=Acevedo-Opazo, C.; Tisseyre, B.; Guillaume, S.; Ojeda, H. |volume=9 |issue=5 |pages=285–302 |year=2008 |doi=10.1007/s11119-008-9073-1}}</ref><ref name="BramleyUnder05">{{cite journal |title=Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintages |journal=Australian Journal of Grape and Wine Research |author=Bramley, R.G.V. |volume=11 |issue=1 |pages=33–42 |year=2005 |doi=10.1111/j.1755-0238.2005.tb00277.x}}</ref><ref name="Verdugo-VásquezSpatial16">{{cite journal |title=Spatial variability of phenology in two irrigated grapevine cultivar growing under semi-arid conditions |journal=Precision Agriculture |author=Verdugo-Vásquez, N.; Acevedo-Opazo, C.; Valdés-Gómez, H. et al. |volume=17 |issue=2 |pages=218–45 |year=2016 |doi=10.1007/s11119-015-9418-5}}</ref> Spatial data have particular characteristics that are worth careful consideration during analysis. First of all, their spatial resolution (density) is of interest as it defines the capacity to identify short- and long-scale spatial variability.<ref name="BalujaAss12">{{cite journal |title=Assessment of the spatial variability of anthocyanins in grapes using a fluorescence sensor: Relationships with vine vigour and yield |journal=Precision Agriculture |author=Baluja, J.; Diago, M.P.; Goovaerts, P.; Tardaguila, J. |volume=13 |issue=4 |pages=457–72 |year=2012 |doi=10.1007/s11119-012-9261-x}}</ref><ref name="DebuissonUsing10">{{cite journal |title=Using Multiplex And Greenseeker To Manage Spatial Variation Of Vine Vigor In Champagne |journal=Proceedings of the 10th International Conference on Precision Agriculture |author=Debuisson, S.; Germain, C.; Garcia, O. et al. |year=2010 |url=https://www.ispag.org/proceedings/?action=abstract&id=197}}</ref> Spatial records are often associated with a high level of noise that originates for multiple reasons, such as the plant to plant variability, the accuracy of the sensor, or the conditions of data acquisition.<ref name="TaylorIdent10>{{cite journal |title=Identification and significance of sources of spatial variation in grapevine water status |journal=Australian Journal of Grape and Wine Research |author=Taylor, J.A.; Acevedo–Opazo, C.; Ojeda, H.; Tisseyre, B. |volume=16 |issue=1 |pages=218–26 |year=2010 |doi=10.1111/j.1755-0238.2009.00066.x}}</ref> Except for images in which data are regularly distributed on a grid of pixels, many spatial observations collected in agriculture are irregular and do not follow a fixed pattern within the fields.<ref name="TaylorEstab07">{{cite journal |title=Establishing Management Classes for Broadacre Agricultural Production |journal=Agronomy Journal |author=Taylor, J.A.; McBratney, A.B.; Whelan, B.M. |volume=99 |issue=5 |pages=1366-76 |year=2007 |doi=10.2134/agronj2007.0070}}</ref> This feature is of great concern because many image processing algorithms cannot be directly used on these irregular data.


==References==
==References==

Revision as of 17:21, 10 July 2018

Full article title GeoFIS: An open-source decision support tool for precision agriculture data
Journal Agriculture
Author(s) Leroux, Corentin; Jones, Hazaël; Pichon, Léo; Guillaume, Serge; Lamour, Julien;
Taylor, James; Naud, Olivier; Crestey, Thomas; Lablee, Jean-Luc; Tisseyre, Bruno
Author affiliation(s) University of Montpellier, SMAG, Compagnie Fruitière
Primary contact Email: cleroux at smag-group dot com
Year published 2018
Volume and issue 8(6)
Page(s) 73
DOI 10.3390/agriculture8060073
ISSN 2077-0472
Distribution license Creative Commons Attribution 4.0 International
Website http://www.mdpi.com/2077-0472/8/6/73/htm
Download http://www.mdpi.com/2077-0472/8/6/73/pdf (PDF)
This article should not be considered complete until this message box has been removed. This is a work in progress.

Abstract

The world we live in is an increasingly spatial and temporal data-rich environment, and the agriculture industry is no exception. However, data needs to be processed in order to first get information and then make informed management decisions. The concepts of "precision agriculture" and "smart agriculture" can and will be fully effective when methods and tools are available to practitioners to support this transformation. An open-source program called GeoFIS has been designed with this objective. It was designed to cover the whole process from spatial data to spatial information and decision support. The purpose of this paper is to evaluate the abilities of GeoFIS along with its embedded algorithms to address the main features required by farmers, advisors, or spatial analysts when dealing with precision agriculture data. Three case studies are investigated in the paper: (i) mapping of the spatial variability in the data, (ii) evaluation and cross-comparison of the opportunity for site-specific management in multiple fields, and (iii) delineation of within-field zones for variable-rate applications when these latter are considered opportune. These case studies were applied to three contrasting crop types: banana, wheat, and grapes. These were chosen to highlight the diversity of applications and data characteristics that might be handled with GeoFIS. For each case-study, up-to-date algorithms arising from research studies and implemented in GeoFIS were used to process these precision agriculture data. Areas for future development and possible relations with existing geographic information systems (GIS) software is also discussed.

Keywords: decision making, GeoFIS, geostatistics, open-source software, precision agriculture, spatial analysis

Introduction

Within-field variability is now a widely accepted and reported phenomenon by the precision agriculture community.[1][2] Geolocalized data are effectively collected intensively within the fields by sensors embedded on agricultural machinery, satellites, flying platforms, static stations, or humans among others, to make sure that this variability is considered and accounted for.[3][4][5] Spatial data have particular characteristics that are worth careful consideration during analysis. First of all, their spatial resolution (density) is of interest as it defines the capacity to identify short- and long-scale spatial variability.[6][7] Spatial records are often associated with a high level of noise that originates for multiple reasons, such as the plant to plant variability, the accuracy of the sensor, or the conditions of data acquisition.[8] Except for images in which data are regularly distributed on a grid of pixels, many spatial observations collected in agriculture are irregular and do not follow a fixed pattern within the fields.[9] This feature is of great concern because many image processing algorithms cannot be directly used on these irregular data.

References

  1. ↑ Oliver, M.A., ed. (2010). Geostatistical Applications for Precision Agriculture. Springer. pp. 331. doi:10.1007/978-90-481-9133-8. ISBN 9789048191321. 
  2. ↑ Pringle, M.J.; McBratney, A.B.; Whelan, B.M.; Taylor, J.M. (2003). "A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data". Agricultural Systems 76 (1): 273–92. doi:10.1016/S0308-521X(02)00005-7. 
  3. ↑ Acevedo-Opazo, C.; Tisseyre, B.; Guillaume, S.; Ojeda, H. (2008). "The potential of high spatial resolution information to define within-vineyard zones related to vine water status". Precision Agriculture 9 (5): 285–302. doi:10.1007/s11119-008-9073-1. 
  4. ↑ Bramley, R.G.V. (2005). "Understanding variability in winegrape production systems 2. Within vineyard variation in quality over several vintages". Australian Journal of Grape and Wine Research 11 (1): 33–42. doi:10.1111/j.1755-0238.2005.tb00277.x. 
  5. ↑ Verdugo-Vásquez, N.; Acevedo-Opazo, C.; Valdés-Gómez, H. et al. (2016). "Spatial variability of phenology in two irrigated grapevine cultivar growing under semi-arid conditions". Precision Agriculture 17 (2): 218–45. doi:10.1007/s11119-015-9418-5. 
  6. ↑ Baluja, J.; Diago, M.P.; Goovaerts, P.; Tardaguila, J. (2012). "Assessment of the spatial variability of anthocyanins in grapes using a fluorescence sensor: Relationships with vine vigour and yield". Precision Agriculture 13 (4): 457–72. doi:10.1007/s11119-012-9261-x. 
  7. ↑ Debuisson, S.; Germain, C.; Garcia, O. et al. (2010). "Using Multiplex And Greenseeker To Manage Spatial Variation Of Vine Vigor In Champagne". Proceedings of the 10th International Conference on Precision Agriculture. https://www.ispag.org/proceedings/?action=abstract&id=197. 
  8. ↑ Taylor, J.A.; Acevedo–Opazo, C.; Ojeda, H.; Tisseyre, B. (2010). "Identification and significance of sources of spatial variation in grapevine water status". Australian Journal of Grape and Wine Research 16 (1): 218–26. doi:10.1111/j.1755-0238.2009.00066.x. 
  9. ↑ Taylor, J.A.; McBratney, A.B.; Whelan, B.M. (2007). "Establishing Management Classes for Broadacre Agricultural Production". Agronomy Journal 99 (5): 1366-76. doi:10.2134/agronj2007.0070. 

Notes

This presentation is faithful to the original, with only a few minor changes to grammar, spelling, and presentation, including the addition of PMCID and DOI when they were missing from the original reference.

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