Simulating yield datasets: an opportunity to improve data filtering algorithms
| Autor: | Corentin Leroux, B. Dreux, Bruno Tisseyre, Hazaël Jones, Anthony Clenet, M. Becu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Yield (finance) 0211 other engineering and technologies Process (computing) 04 agricultural and veterinary sciences 02 engineering and technology General Medicine Two stages Field (computer science) Data filtering 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Algorithm 021101 geological & geomatics engineering |
| Zdroj: | Advances in Animal Biosciences. 8:600-605 |
| ISSN: | 2040-4700 |
| DOI: | 10.1017/s2040470017000899 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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