Journal:GeoFIS: An open-source decision support tool for precision agriculture data

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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)
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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.

To benefit from this increasing flow of data, users should be provided with software or tools that allow them to:

  1. visualize the data they have collected (simple or low-level functions),
  2. process these data (advanced or high-level functions), and
  3. incorporate the knowledge they have on these data into the data processing.

It is acknowledged that basic visualization tools—e.g., data import, georeferencing, data display—are available in many general (e.g., Quantum Geographic Information System (QGIS), gvSIG, Google Earth, Whitebow Geospatial Analysis Tools) and more specific[10][11] open-source platforms, including those not specific to agricultural applications. It is clear that such functionalities are of major importance for handling spatial data. However, when it comes to making informed management decisions, these visualization functions are not sufficient. It is necessary to provide users with more advanced or high-level functions so that they can turn this raw spatial data into information and decision layers. The most commonly required procedures in the precision agriculture domain are functions such as:


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. 
  10. ↑ Jeong, J.S.; García-Moruno, L.; Hernández-Blanco, J. (2012). "Integrating buildings into a rural landscape using a multi-criteria spatial decision analysis in GIS-enabled web environment". Biosystems Engineering 112 (2): 82–92. doi:10.1016/j.biosystemseng.2012.03.002. 
  11. ↑ Yalew, S.G.; van Griensven, A.; van der Zaag, P. (2016). "AgriSuit: A web-based GIS-MCDA framework for agricultural land suitability assessment". Computers and Electronics in Agriculture 128 (10): 1–8. doi:10.1016/j.compag.2016.08.008. 

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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