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Yield data preprocessing

Source: vignettes/Yield_data.Rmd
Yield_data.Rmd

Usually, yield data comes with many noisy observations. This vignette will show how to preprocess yield data to remove both, spatial and global outliers. The protocol for error removal follows the protocol proposed by Vega et al. (2019). Functions from this package are used in FastMapping software (Paccioretti, Córdoba, and Balzarini 2020). For the tutorial we will use the barley dataset that comes with the paar package. The barley data contains barley grain yield which were obtained using calibrated commercial yield monitors, mounted on combines equipped with DGPS. The data is not a sf object format. We will convert it to an sf object first.

First, we will load the paar package, the sf package for spatial data manipulation, ggplot2 for plotting, and the barley dataset that comes with the paar package.

library(paar)
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
require(ggplot2)
#> Loading required package: ggplot2

data("barley", package = 'paar')

The barley dataset is a data.frame object. We will convert it to a sf object using the st_as_sf function. The coords argument specifies the columns that contain the coordinates. The crs argument specifies the coordinate reference system. The barley dataset is in UTM zone 20S.

barley_sf <- st_as_sf(barley, 
                      coords = c("X", "Y"),
                      crs = 32720)

The barley_sf object is now an sf object. We can plot the data to visualize the yield data.

  • The plot function can be used to plot the data.
plot(barley_sf["Yield"])

  • The ggplot2 package can be used to plot the data.
ggplot(barley_sf) +
  geom_sf(aes(color = Yield)) +
  scale_color_viridis_c() +
  theme_minimal()

Let’s see the yield values distribution.

  • The hist function can be used to plot the histogram.
hist(barley_sf$Yield, main = 'Yield values distribution')

  • The ggplot2 package can be used to plot the histogram.
ggplot(barley_sf) +
  geom_histogram(aes(x = Yield)) +
  theme_minimal()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

The protocol proposed by (Vega et al. 2019), is implemented in the function depurate and consists of three steps: 1. Remove border observations (edges). 2. Remove global outliers (outliers). 3. Remove spatial outliers (inliers).

The depurate function takes an sf object as input and returns an object of class paar. Any combination of the three steps can be done using the depurate function. The argument to_remove specifies which steps to perform. The argument y specifies the column name of the variable to be cleaned. A field boundary is necessary to remove the edges observations. If a polygon is not provided in the poly_border argument, the function will make a hull, around the data and remove the observation that are 10m from the hull. The hull is made using concaveman::concaveman function if the package is installed, otherwise, the sf::st_convex_hull function is used.

barley_clean_paar <-
  depurate(barley_sf, 
           y = 'Yield',
           toremove = c("edges", "outlier", "inlier"))
#> Concave hull algorithm is computed with
#> concavity = 2 and length_threshold = 0

Summary of the cleaning process

The depurate function returns an object of class paar. The paar object contains the cleaned data ($depurated_data), and the condition of each observation ($condition). If the condition is NA means that the observation was not removed.

barley_clean_paar
#> Depurated data has 5673 rows.
#> The process removed 23% of original data.
#> 
#> $depurated_data
#> Simple feature collection with 5673 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 581322.1 ymin: 5953094 xmax: 582393.3 ymax: 5954175
#> Projected CRS: WGS 84 / UTM zone 20S
#> First 3 features:
#>       Yield                 geometry
#> 3  2.566069 POINT (582393.3 5953877)
#> 36 3.217464 POINT (582373.4 5953843)
#> 37 2.651020 POINT (582375.7 5953846)
#> 
#> 
#> $condition
#> vector of length 7394. First 3 elements:
#> [1] "border" "border" NA

The summary function can be used to get a summary of the percentage of considered outlier and the number of observations removed. The summary function returns a data.frame object.

summary_table <- summary(barley_clean_paar)
summary_table
#>       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%)

Filtered dataset can be extracted from the paar object using the $depurated_data

barley_clean <- barley_clean_paar$depurated_data

Final Yield values distribution can be plotted.

  • The plot function can be used to plot yield values.
plot(barley_clean["Yield"])

  • The ggplot2 package can be used to plot yield values.
ggplot(barley_clean) +
  geom_sf(aes(color = Yield)) +
  scale_color_viridis_c() +
  theme_minimal()

A comparison can be made between the original data and the cleaned data.

  • Original data
ggplot(barley_sf) +
  geom_sf(aes(color = Yield)) +
  scale_color_viridis_c() +
  theme_minimal()

  • Cleaned data
ggplot(barley_clean) +
  geom_sf(aes(color = Yield)) +
  scale_color_viridis_c() +
  theme_minimal()

Also, the distribution of the yield values can be compared.

  • Original data
ggplot(barley_sf, aes(x = Yield)) +
  geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  • Cleaned data
ggplot(barley_clean, aes(x = Yield)) +
  geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Plotting the condition of each observation

The condition of each observation can be combined to the original data using the cbind function. The paar object must be used as first argument in the cbind function.

barley_sf <- cbind(barley_clean_paar, barley_sf)

The barley_sf object now contains the condition of each observation. The condition column contains the condition of each observation. The condition can be NA if the observation was not removed, edges if the observation was removed in the edges step, outlier if the observation was removed in the outliers step, and inlier if the observation was removed in the inliers step. Results can be plotted to visualize the observations.

  • The plot function can be used to plot the condition of each observation.
plot(barley_sf[,'condition'], col = as.numeric(as.factor(barley_sf$condition)))
legend("topright", legend = levels(as.factor(barley_sf$condition)), fill = 1:4)

  • The ggplot2 package can be used to plot the condition of each observation.
ggplot(barley_sf) +
  geom_sf(aes(color = condition)) +
  scale_fill_viridis_d() +
  scale_color_discrete(
    labels = function(k) {k[is.na(k)] <- "normal"; k},
    na.value = "#44214234") +
  theme_minimal()

Paccioretti, P., M. Córdoba, and M. Balzarini. 2020. “FastMapping: Software to Create Field Maps and Identify Management Zones in Precision Agriculture.” Computers and Electronics in Agriculture 175 (August). https://doi.org/10.1016/j.compag.2020.105556.
Vega, Andrés, Mariano Córdoba, Mauricio Castro-Franco, and Mónica Balzarini. 2019. “Protocol for Automating Error Removal from Yield Maps.” Precision Agriculture 20 (5): 1030–44. https://doi.org/10.1007/s11119-018-09632-8.