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:

  1. filtering, to ensure the quality of the datasets[12][13],
  2. interpolation, to provide a continuous mapping of the property of interest[14][15][16],
  3. zoning, to define within-field zones for site-specific management[17][18], or
  4. aggregation so that multiple layers of information can be combined.[19][20]

To foster the adoption of such tools, all the aforementioned functions have to be specifically dedicated to the processing of agricultural data from potentially very differing productions systems. This is an important consideration as these data come with a lot of associated knowledge that has to be considered when processing these data. More specifically, significant local expertise to support decision making might be available as users, e.g., farmers, advisors and/or technicians, have normally been scouting the fields during all the growing season.[21][22][23] Site-specific management also requires the use of agricultural machinery with specific characteristics that have to be considered in these processing functions. This is to ensure that planned differential management is in accordance with the practical and operational limitations of machinery, e.g., working width, lag time, and application speed.[24][25]

From a general perspective, there are only a few dedicated software programs available to explicitly process precision agriculture data and incorporate expert knowledge into the process. Moreover, very few of them are open-source. Some freeware and shareware tools have been developed and proposed by the precision agriculture community, but these generally focus on specific processing tasks or on a particular type of data. For example, the Vesper program[26], developed by the University of Sydney, provides users with a graphical interface to spatially interpolate their data. Despite the quite advanced functions that are available, e.g., local punctual and block kriging, users only end up with a continuous map of their data without much more practical information. The Yield Editor software from the United States Department of Agriculture[13][27] deals effectively with the filtering of within-field yield datasets that are known to contain many defective observations[28], but it does not perform interpolation or other high-level functions. Another interesting example is a QGIS plugin that was put into place to process spatial data of vine shoot diameter arising from the mounted sensor Physiocap (E.RE.C.A, Vaulx-en-Velin, France). This tool mainly incorporates functions to filter these highly noisy datasets. Other platforms have been proposed by agronomist to give farmers access to crop models, but they are very specific in terms of crop, data, and use.[29] An open-source platform that takes raw data through to a decision point is not available to the precision agriculture community yet.

The aim of this paper is to present the GeoFIS software (https://www.geofis.org/), developed by a joint team from IRSTEA, INRA, and Montpellier SupAgro in France.[30] The goal of this platform is to provide users with up-to-date and reliable algorithms to process their precision agriculture data and incorporate expert knowledge from the fields. GeoFIS has been mainly developed for academic and research purposes, i.e., investigators and students willing to process their data, but also to a lesser extent for agronomists and advisors with a sufficient background in spatial analysis. The objective of this interface-based platform is to support users who do not necessarily have programming skills and to show that high level functions can be introduced in a GIS and could be integrated within precision agriculture programs. The first section introduces this open-source tool along with its architecture, design, interface, and main processing functions. Three different case studies on various crops are then considered to evaluate the ability of this software to answer most of the issues that are faced by the agricultural sector for processing their spatial data. The last section highlights the needs for future developments to promote precision agriculture adoption and the possibility to create connections with existing GIS software programs.

The GeoFIS software

Aim of the GeoFIS project

GeoFIS has been designed to facilitate the movement from spatial data to spatial information, and to spatial decision making. It is an open-source program that proposes a simple and easy-to-use interface to build decision support systems (DSS) from spatial data.[30] While its development has been inspired by agri-environmental applications, the framework itself is open and accessible to applications in other domains. It is designed to be adaptable to different usages and for different end users, mostly for academic and research applications, for student and teaching applications, and, to a lesser extent, for GIS-skilled agronomists and advisors.

GeoFIS deviates from other GIS software, e.g., QGIS, in the sense that specific tools have been implemented to answer the main expectations of agricultural professionals when it comes to processing precision agriculture data. These will be presented later on. It is acknowledged that multiple other open-source spatial programs (e.g., QGIS) or languages (e.g., R and Python) are available to process spatial and temporal data. However, these open-source tools do not have specific functions dedicated to the processing of precision agriculture data (as listed in the introduction section) and usually require users to have skills in programming. This is a major limiting factor for the practical use of spatial modelling in agriculture. Another strength of GeoFIS is that attention has been paid to the incorporation of expert knowledge into data analysis. This is not available in other related spatial processing tools. Agricultural professionals have significant local expert knowledge on their production system that needs to be taken into account. By incorporating this qualitative expert knowledge, the quality of the processing should be improved and the adoption of precision agriculture technologies should be enhanced.

Architecture and design of GeoFIS

In the proposed GeoFIS architecture, all the open-source toolboxes and libraries have been selected for their ability to handle spatial data and to incorporate expert knowledge (Figure 1). Statistical and geostatistical functions dedicated to precision agriculture data (see next subsection) are implemented in R (https://www.r-project.org). Outside these specific functions, spatial data are handled through two open-source libraries, i.e., Geotools (http://www.geotools.org) and CGAL (Computational Geometry Algorithms Library, https://www.cgal.org). Geotools is used because its Java implementation allows the design of user-friendly interfaces. CGAL was chosen for its ability to provide very efficient and reliable geometric algorithms, as its functions are developed in C++. Finally, the incorporation of expert knowledge is made possible with FisPro (https://www.fispro.org), a system that uses fuzzy sets for conceptual modeling.[30]


Fig1 Leroux Agri2018 8-6.jpg

Figure 1: The GeoFIS architecture[30]. CGAL, Computational Geometry Algorithms Library; DSS, Decision Support Systems; GIS, Geographic Information System; 1D, One dimension

GeoFIS is available in four languages (French, English, Spanish, and Portuguese). The interface is designed with a man-machine cooperation objective. The goal is to facilitate the relationships between data, learning algorithms, and expert knowledge. Documentation, scientific papers, and video tutorials are available to better understand the implemented function and to facilitate the adoption of the GeoFIS software (https://www.geofis.org/). Notifications are made when a new version of the software is available.

Functionalities implemented in GeoFIS

GeoFIS contains a series of low and high-level non-spatial and spatial functionalities to interrogate spatial data. The general functionalities are introduced here and then expanded in several case studies in the following section. Figure 2 shows the generic flow required in precision agriculture, from raw data processing to decision making, with the functionalities within GeoFIS at each stage indicated. In agricultural systems, data are available in different formats (points, polygons, rasters) and at different scales. The quality of the data is also variable, with some sensors being inherently noisy and others less so. Different data need potentially different approaches to (i) data validation and clean-up (quality control), (ii) data display (visualization), and, when necessary, (iii) interpolation. These steps transform data into information layers. Within GeoFIS, data can be easily imported (Step 0) and displayed as a map (in its geographical space) and as a histogram (in its attribute space). This allows the user to "expertly" identify global outliers in both the geographical and attribute space and remove any erroneous data (Step 1). Interpolation is possible using inverse distance weighting (for small data sets) and via punctual kriging with a global variogram for larger data sets (>100 points). The kriging method includes the ability to plot the experimental variogram and specify a theoretical variogram, which is then passed to the kriging function. Interpolated outputs can be directly displayed as rasters within the display (Step 2).


Fig2 Leroux Agri2018 8-6.jpg

Figure 2: Generic flow of data in precision agriculture with main processing steps from raw data processing to decision-making.

"Precision agriculture" or "smart agriculture" is only effective when effective decisions are made. End users can transform these information layers into decision layers to improve the management of their fields. Three main functionalities for management (practical) applications have been incorporated within GeoFIS to address this. Firstly, practitioners are provided with a method to delineate within-field homogeneous zones (Step 3.1). Zoning is of importance for precision agriculture data, as the identified zones will (i) facilitate spatial data visualization and interpretation and (ii) provide a spatial resolution that is practical and effective for many differential field operations. GeoFIS uses a segmentation algorithm to "zone" data layers.[18] The segmentation algorithm operates either on irregular or gridded (interpolated) data to generate potential management zones.

Secondly, while data/information collection tends to be focused around production issues, there is no restriction on its use. It can equally be used for strategic as well as tactical decision making. The example of the technical opportunity index (TOI)[31], which is implemented in GeoFIS, is a case in point. The TOI uses the production data to assess a field’s suitability for site-specific management given machinery constraints and the observed production variation (Step 3.2). The algorithm processes the within-field data with a mathematical morphological filter based on erosion and dilation.[31] This filter allows end users to account for the passes of the agricultural machinery in the field and especially the minimum area (kernel) within which it can operate reliably. As the algorithm requires the data to be organized regularly on a grid, interpolating the data might therefore be required as a pre-processing step (Step 2).

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. 
  12. ↑ Leroux, C.; Jones, H.; Clenet, A. et al. (2018). "A general method to filter out defective spatial observations from yield mapping datasets". Precision Agriculture: 1–20. doi:10.1007/s11119-017-9555-0. 
  13. ↑ 13.0 13.1 Sudduth, K.A.; Drummond, S.T. (2006). "Yield Editor". Agronomy Journal 99 (6): 1471–82. doi:10.2134/agronj2006.0326. 
  14. ↑ Hengl, T.; Heuvelink, G.B.M.; Stein, A. (2004). "A generic framework for spatial prediction of soil variables based on regression-kriging". Geoderma 120 (1–2): 75–93. doi:10.1016/j.geoderma.2003.08.018. 
  15. ↑ Oliver, M.A.; Webster, R. (2014). "A tutorial guide to geostatistics: Computing and modelling variograms and kriging". CATENA 113 (2): 56–69. doi:10.1016/j.catena.2013.09.006. 
  16. ↑ Robinson, T.P.; Mettemicht, G. (2006). "Testing the performance of spatial interpolation techniques for mapping soil properties". Computers and Electronics in Agriculture 50 (2): 97–108. doi:10.1016/j.compag.2005.07.003. 
  17. ↑ Cid-Garcia, N.M.; Albornoz, V.; Rios-Solis, Y.A.; Ortega, R. (2013). "Rectangular shape management zone delineation using integer linear programming". Computers and Electronics in Agriculture 93 (4): 1–9. doi:10.1016/j.compag.2013.01.009. 
  18. ↑ 18.0 18.1 Pedroso, M.; Taylor, J.; Tisseyre, B. et al. (2010). "A segmentation algorithm for the delineation of agricultural management zones". Computers and Electronics in Agriculture 70 (1): 199–208. doi:10.1016/j.compag.2009.10.007. 
  19. ↑ Blackmore, S.; Godwin, R.J.; Fountas, S. (2003). "The Analysis of Spatial and Temporal Trends in Yield Map Data over Six Years". Biosystems Engineering 84 (4): 455–66. doi:10.1016/S1537-5110(03)00038-2. 
  20. ↑ Li, Y.; Shi, Z.; Li, F.; Li, H.-Y. (2007). "Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land". Computers and Electronics in Agriculture 56 (2): 174–86. doi:10.1016/j.compag.2007.01.013. 
  21. ↑ Oliver, Y.M.; Robertson, M.J.; Wong, M.T.F. (2010). "Integrating farmer knowledge, precision agriculture tools, and crop simulation modelling to evaluate management options for poor-performing patches in cropping fields". European Journal of Agronomy 32 (1): 40–50. doi:10.1016/j.eja.2009.05.002. 
  22. ↑ Pichon, L.; Besqueut, G.; Tisseyre, B. (2017). "A systemic approach to identify relevant information provided by UAV in precision viticulture". Advances in Animal Biosciences 8 (2): 823–7. doi:10.1017/S2040470017001194. 
  23. ↑ Schenatto, K.; de Souza, E.G.; Bazzi, C.L. et al. (2017). "Use of the farmer’s experience variable in the generation of management zones". Semina, Ciências Agrárias 38 (4): 2305–21. doi:10.5433/1679-0359.2017v38n4Supl1p2305. 
  24. ↑ Leroux, C.; Jones, H.; Clenet, A.; Tisseyre, B. (2017). "A new approach for zoning irregularly-spaced, within-field data". Computers and Electronics in Agriculture 141 (9): 196–206. doi:10.1016/j.compag.2017.07.025. 
  25. ↑ Roudier, P.; Tisseyre, B.; Poilvé, H.; Roger, J.-M. (2008). "Management zone delineation using a modified watershed algorithm". Precision Agriculture 9: 233. doi:10.1007/s11119-008-9067-z. 
  26. ↑ Whelan, B.M.; McBratney, A.B.; Minasny, B. (2001). "Vesper—Spatial prediction software for precision agriculture". ECPA 2001, Proceedings of the 3rd European Conference on Precision Agriculture: 139–44. https://www.semanticscholar.org/paper/Vesper-%E2%80%93-Spatial-Prediction-Software-for-Precision-Whelan-Mcbratney/52caaed8c82c943d760e3166e75d783c26d3dfe4. 
  27. ↑ Sudduth, K.A.; Drummond, S.T.; Myers, D.B. (2012). "Yield Editor 2.0: Software for Automated Removal of Yield Map Errors". Proceedings of the 2012 ASABE Annual International Meeting: 1–14. http://extension.missouri.edu/sare/documents/asabeyieldeditor2012.pdf. 
  28. ↑ Simbahan, G.C.; Dobermann, A.; Ping, J.L. (2003). "Screening Yield Monitor Data Improves Grain Yield Maps". Agronomy Journal 96 (4): 1091–102. doi:10.2134/agronj2004.1091. 
  29. ↑ Krishnan, P.; Sharma, R.K.; Dass, A. et al. (2016). "Web-based crop model: Web InfoCrop – Wheat to simulate the growth and yield of wheat". Computers and Electronics in Agriculture 127 (9): 324–35. doi:10.1016/j.compag.2016.06.008. 
  30. ↑ 30.0 30.1 30.2 30.3 Guillaume, S.; Charnomordic, B.; Tisseyre, B.; Taylor, J. (2013). "Soft computing-based decision support tools for spatial data". International Journal of Computational Intelligence Systems 6 (Sup. 1): 18–33. doi:10.1080/18756891.2013.818185. 
  31. ↑ 31.0 31.1 Tisseyre, B.; McBratney, A.B. (2008). "A technical opportunity index based on mathematical morphology for site-specific management: An application to viticulture". Precision Agriculture 9 (1–2): 101–13. doi:10.1007/s11119-008-9053-5. 

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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