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Simulating yield datasets: an opportunity to improve data filtering algorithms

  • C. Leroux (a1) (a2), H. Jones (a2), A. Clenet (a1), B. Dreux (a3), M. Becu (a3) and B. Tisseyre (a2)...


Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.


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Ben-Gal, I 2005. Outlier detection. Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers.
Bivand, RS, Pebesma, EJ and Gomez-Rubio, V 2008. Applied Spatial Data Analysis with R. Springer, New York, NY, USA.
Breunig, MM, Kriegel, H-P, Ng, RT and Sander, J 2000. Lof: identifying density-based local outliers. In Proceedings of 2000 ACM SIGMOD International Conference on Management of Data. ACM Press, pp. 93–104.
Drummond, ST, Fraisse, CW and Sudduth, KA 1999. Combine harvest area determination by vector processing of GPS position data. Transactions of ASAE 42 (5), 12211227.
Griffin, T, Dobbins, C, Vyn, T, Florax, R and Lowenberg-DeBoer, J 2008. Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making. Precision Agriculture 9 (5), 269283.
Lyle, G, Bryan, BA and Ostendorf, B 2013. Post-processing methods to eliminate erroneous grain yield measurements: review and directions for future development. Precision Agriculture 15 (4), 377402.
Molin, JP 2002. Methodology for identification , characterization and removal of errors on yield maps. ASAE Meeting Presentation 0300 (02), 17.
Robinson, TP and Metternicht, G 2005. Comparing the performance of techniques to improve the quality of yield maps. Agricultural Systems 85 (1), 1941.
Simbahan, CG, Dobermann, A and Ping, LJ 2004. Screening Yield Monitor Data Improves Grain Yield Maps. American Society of Agronomy 1102 (14303), 10911102.
Sudduth, KA and Drummond, ST 2007. Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal 99 (6), 1471.
Sudduth, KA, Drummond, ST, Myers, DB and Anatole, H 2012. Yield editor 2.0: Software for automated removal of yield map errors. In: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE).
Sun, W, Whelan, B, McBratney, AB and Minasny, B 2013. An integrated framework for software to provide yield data cleaning and estimation of an opportunity index for site-specific crop management. Precision Agriculture 14 (4), 376391.


Simulating yield datasets: an opportunity to improve data filtering algorithms

  • C. Leroux (a1) (a2), H. Jones (a2), A. Clenet (a1), B. Dreux (a3), M. Becu (a3) and B. Tisseyre (a2)...


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