Protocol for automating error removal from yield maps
- 34 Downloads
Yield mapping is one of the most widely used precision farming technologies. However, the value of the maps can be compromised by the presence of systematic and random errors in raw within field data. In this paper, an automated method to clean yield maps is proposed so as to ensure the quality of further data processing and management decisions. First, data were screened by filtering null and edge yield values as well global outliers. Second, spatial outliers or local defective observations were deleted. The local Moran’s index of spatial autocorrelation and Moran’s plot were used as tool to identify the spatial outliers. The protocol to filter out global and local outliers was evaluated on 595 real yield datasets from different grain crops. Significant improvements in the distribution and spatial structure of yield datasets was found. Approximately 30% of the dataset size was removed from each monitor dataset, with one third of the removal occurring during filtering of spatial outliers. The automation of null, edge yield values and the removal of global outliers improved yield distributions, whereas the cleaning of local outliers impacted the yield spatial structure for all yield maps and crops. The algorithm proposed to clean yield maps is easy to apply for preprocessing the growing number of available yield maps.
KeywordsGlobal outliers Local outliers Spatial data mining
We thank the Argentinian National Scientific and Technological Promotion Agency (ANPCyT-PICT 2014-1071), Ministry of Science and Technology of Córdoba province (MinCyT-PIODO), Science and Technology Secretary of National University of Córdoba (SECyT-UNC), and the National Scientific and Technical Research Council (CONICET), for their support of this research.
- Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In M. Fischer, H. Scholten, & D. Unwin (Eds.), Spatial analytical perspectives on GIS (pp. 111–125). London, UK: Taylor and Francis.Google Scholar
- Blackmore, B. S., & Marshall, C. J. (1996). Yield mapping: Errors and algorithms. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), 3rd International conference on precision agriculture (pp. 403–416). Madison, WI, USA: ASA/CSSA/SSSA.Google Scholar
- Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographical information systems. Oxford, UK: Oxford University Press.Google Scholar
- Deutsch, C. V., & Journel, A. G. (1998). GSLIB: Geostatistical software library and user’s guide (2nd ed.). New York, NY, USA: Oxford University Press.Google Scholar
- Drummond, S. T., & Sudduth, K. A. (2005). Analysis of errors affecting yield map accuracy. In D. J. Mulla (Ed.), 7th International conference on precision agriculture, CD-ROM (pp. 1478–1490). St. Paul, USA: University of Minnesota.Google Scholar
- Gozdowski, D., Samborski, S., & Dobers, E. S. (2010). Evaluation of methods for the detection of spatial outliers in the yield data of winter wheat. Colloquium Biometricum, 40, 41–51.Google Scholar
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. New York, USA: Springer Publishing Company, Incorporated.Google Scholar
- Lehmann, E. L., & D’abrera, H. J. M. (1975). Nonparametrics. San Francisco, CA, USA: Holden-Day.Google Scholar
- McCallum, Q. E., & Weston, S. (2011). In M. L. M. Blanchette (Ed.), Parallel R. Sebastopol, CA: O’Reilly Media.Google Scholar
- Morgan, M., Obenchain, V., Lang, M., Thompson, R., & Turaga, N. (2016). BiocParallel: Bioconductor facilities for parallel evaluation. R package version 1.4.3. Retrieved October 6, 2018, from https://github.com/Bioconductor/BiocParallel.
- Noack, P. O., Muhr, T., & Demmel, M. (2005). Effect of interpolation methods and filtering on the quality of yieldmaps. In J. V. Stafford (Ed.), Proceedings of the 5 th European conference on precision agriculture (pp. 701–706). Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
- R Core Team. (2016). R: A language and environment for statistical computing. Vienna: Austria.Google Scholar
- Schabenberger, O., & Pierce, F. J. (2002). Contemporary statistical models for the plant and soil sciences. Boca Raton, FL, USA: CRC Press LLC.Google Scholar
- Spekken, M., A Anselmi, A., & Molin, J. (2013). A simple method for filtering spatial data. In: Stafford, J. V. (Ed.), Precision agriculture 2013, proceedings of the 9th European conference on precision agriculture (pp. 259–266). Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
- Stafford, J. V. (1996). Essential technology for precision agriculture. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), 3rd International Conference on Precision Agriculture (pp. 595–604). Madison, WI, USA: ASA/CSSA/SSSA.Google Scholar
- Su, P. C. (2011). Statistical Geocomputing: Spatial outlier detection in precision agriculture. Masters thesis, University of Waterloo, Ontario, Canada.Google Scholar
- Thylén, L., Algerbo, P. A., & Giebel, A. (2000). An expert filter removing erroneous yield data. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), 5th International conference on precision agriculture (pp. 1–9). ASA/CSSA/SSSA: Madison, WI, USA.Google Scholar