The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

paar

The goal of paar is to provide useful tools for cleaning and processing spatial data in precision agriculture.

Installation

You can install the released version of paar from CRAN with:

install.packages("paar")

You can install the development version from GitHub with:

# install.packages("pak")
pak::pkg_install("PPaccioretti/paar")

Example

The package provides a complete protocol for automated error removal. Default values of all functions are optimized for precision agriculture data.

library(paar)
library(sf)
#> Warning: package 'sf' was built under R version 4.5.2
data("barley", package = 'paar')

The barley dataset contains grain yield data collected were using calibrated commercial yield monitors, mounted on combines equipped with DGPS.

#Convert barley data to an spatial object
barley_sf <- st_as_sf(barley, coords = c("X", "Y"), crs = 32720)

barley_dep <-
  depurate(barley_sf, "Yield")
#> Concave hull algorithm is computed with
#> concavity = 2 and length_threshold = 0

# Summary of depurated data
summary(barley_dep)
#>       normal point             border spatial outlier MP spatial outlier LM 
#>         5673 (77%)          964 (13%)         343 (4.6%)         309 (4.2%) 
#>         global min            outlier 
#>          99 (1.3%)         6 (0.081%)

Spatial yield values before and after the depuration process can be visualized

plot(barley_sf["Yield"], main = "Before depuration")
plot(barley_dep$depurated_data["Yield"], main = "After depuration")

The distribution of yield values can also be compared

boxplot(barley_sf[["Yield"]], main = "Before depuration")
boxplot(barley_dep$depurated_data[["Yield"]], main = "After depuration")

References

• Vega, A., Córdoba, M., Castro-Franco, M. et al. (2019). Protocol for automating error removal from yield maps. Precision Agric 20, 1030–1044 https://doi.org/10.1007/s11119-018-09632-8
• Paccioretti, P., Córdoba, M., & Balzarini, M. (2020). FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture, 175, 105556 https://doi.org/10.1016/j.compag.2020.105556.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.