---
title: "pacu: Precision Agriculture Computational Utilities - Yield monitor data"
author: "Caio dos Santos & Fernando Miguez"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{pacu: Precision Agriculture Computational Utilities - Yield monitor data}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
# Specific vignettes
1. [Satellite data](pacu_sat.html)
2. [Weather data](pacu_weather.html)
3. [Yield monitor data](pacu_ym.html)
4. [Frequently asked questions](pacu_faq.html)
# Yield monitor data
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(sf)
library(pacu)
```
## Proposed workflow
We propose the following workflow for processing raw yield
monitor data:
1. Read the data in
2. Compute summary statistics and visualize the data
3. Check the yield data
4. Fix potential issues
5. Process the data
6. Examine the yield map
To illustrate the functions that process at yield monitor
data, we will look at data from a research paper named
[Prairie strips improve biodiversity and the delivery of multiple ecosystem services from corn--soybean croplands](https://www.pnas.org/doi/abs/10.1073/pnas.1620229114).
Let us, specifically, look at the data from 2012.
### Reading the data in
```{r reading-the-data}
extd.dir <- system.file("extdata", package = "pacu")
raw.yield <- st_read(file.path(extd.dir, '2012-basswood.shp'), quiet = TRUE)
boundary <- st_read(file.path(extd.dir, 'boundary.shp'), quiet = TRUE)
```
We can see that the raw yield data has multiple columns.
Some of them are more or less relevant depending on how we
choose to process the data yield data to make a yield map.
```{r}
names(raw.yield)
```
### Compute summary statistics and visualize the data
Our main objective in this step of the proposed workflow is
to make sure that the data conforms to what we would expect
of yield monitor data. For instance, we can examine the raw
yield data or the distance between measurements.
#### Examining the raw yield data
```{r plotting-the-raw-data, fig.width=6, fig.height=5}
cols <- function(n) hcl.colors(n, 'Temps', rev = TRUE)
plot(raw.yield['DRY_BU_AC'], pal = cols)
```
The boxplot shows some observations that appear to be
abnormally high. These can be a result of sudden speed or
direction change, GPS errors, and other sources of unknown
variability.
```{r boxplot-raw-yield, fig.width=6, fig.height=5}
boxplot(raw.yield$DRY_BU_AC)
```
### Check the yield monitor data with pa_check_yield()
In this step of the proposed workflow, the
***pa_check_yield()*** function searches for potential
sources of problem in the data to give the user the
opportunity of dealing with these before processing the
yield data. For instance, when no units are supplied to the
***pa_yield()*** function, the function tries to guess the
units. Since the ***pa_check_yield()*** function outputs the
guessed units, the user can supply the units to the
***pa_yield()*** function in case the function's guess is
incorrect.
The user can choose to check for potential errors that would
occur with all the algorithms available to the function or
specify which algorithm should the function target.
```{r check-yield}
chk <- pa_check_yield(input = raw.yield,
algorithm = 'all')
chk
```
### Fix potential issues
The ***pa_check_yield()*** function will search for several
sources of problem that could prevent the ***pa_yield()***
function from working correctly. Therefore, it is important
to deal with the identified issues before processing the
yield data.
#### Example: missing yield column
Let us imagine a scenario in which we want to use the simple
algorithm to process the data. To do so, we need a column
with the yield data. The ***pa_yield()*** and
***pa_check_yield()*** functions will try to identify which
columns of the input data frame contain the relevant
information, and the units if those are not supplied. We can
create a toy example in which the yield column has an
uncommon name, preventing the functions from identifying it.
```{r example-missing-col}
toy.example <- raw.yield
names(toy.example) <- gsub('DRY_BU_AC', 'NOT_A_COMMON_NAME', names(toy.example))
chk <- pa_check_yield(toy.example, algorithm = 'simple')
chk
```
To deal with this issue, we can rename the column to a more
common name such as "yield" or "YIELD". Additionally, a
valid solution would be to provide the column name to the
***pa_yield()*** function directly when processing the data.
Here, we rename the column simply to "yield" and we can see
that the algorithm is able to identify it once again.
```{r}
names(toy.example) <- gsub('NOT_A_COMMON_NAME', 'yield', names(toy.example))
chk <- pa_check_yield(toy.example, algorithm = 'simple')
chk
```
### Processing the data and examining the yield maps and diagnostic plots
The **pa_yield()** function can produce yield maps using two
algorithms: simple and ritas. The **simple** algorithm
allows moisture standardization, data cleaning, and
smoothing. By default, it does not conduct any areal
aggregations. The **ritas** algorithm ([Damiano & Niemi, 2020](https://arxiv.org/abs/2209.11313)) follows a
constructive framework, in which the steps are:
**r**ectangle creation; **i**ntersection assignment;
**t**asselation; **a**pportioning; and **s**moothing.
#### Simple aggregation of yield data
##### Initial example
When algorithm is **simple**, the function will search for
the columns that indicate yield, moisture, and time. Note
that, since different crops have a different conversion
factors from bushel to pounds, it is important to specify
the **lbs.per.bushel** argument when the US standard system
of units is used. In this case, we are producing a yield map
for a maize crop, thus, 56 lbs/bushel. Additionally, the
function supports the output of the yield map in both U.S.
standard and metric unit systems. In this
example, we have chosen "metric".
```{r first-ymp, message=FALSE}
ymp1 <- pa_yield(input = raw.yield,
boundary = boundary,
algorithm = 'simple',
lbs.per.bushel = 56,
unit.system = 'metric',
verbose = FALSE)
```
The "ymp1" object contains information on the yield
processing algorithm, smoothing method, conversion factor
used, moisture, and a summary of the yield data.
```{r first-ymp-attr}
ymp1
```
We can visualize the yield map by plotting it
```{r plotting-first-ymp, fig.width=6, fig.height=5}
pa_plot(ymp1)
```
##### Unit conversion and moisture adjustment
In the previous map, we can see that the units are "t/ha".
However, a user might want to produce yield maps using US
standard units. Additionally, the user can set the moisture
level to the market standard moisture. When no moisture
adjustment is provided, the function adjusts the moisture
level to the average moisture in the data set.
```{r ymp2, message=FALSE}
ymp2 <- pa_yield(input = raw.yield,
boundary = boundary,
algorithm = 'simple',
unit.system = 'standard',
moisture.adj = 15.5,
lbs.per.bushel = 56,
verbose = FALSE)
```
```{r ymp2-attr}
ymp2
```
We can visualize the yield map in bushels/acre and at a
moisture level of 15.5%
```{r plotting-ymp2, fig.width=6, fig.height=5}
pa_plot(ymp2)
```
##### Outlier removal
In the previous maps we have conducted simple aggregation of
yield data and standardization of the moisture content. We
can, additionally, add a cleaning step to remove outliers in
the raw yield data.
```{r ymp3, message=FALSE}
ymp3 <- pa_yield(input = raw.yield,
boundary = boundary,
algorithm = 'simple',
unit.system = 'metric',
clean = TRUE,
clean.sd = 3,
lbs.per.bushel = 56,
verbose = FALSE)
```
This cleaning step will remove observations outside of a 3
standard deviation range from the field mean. This will often
leave empty spots in the field if no smoothing method is selected.
```{r plotting-ymp3, fig.width=6, fig.height=5}
pa_plot(ymp3)
```
##### Inverse distance weighted interpolation
In cases in which we want to interpolate the date, the
function can interpolate the yield map using two prediction
methods: inverse distance weighted (IDW) and kriging. The
IDW method is deterministic and is much faster than the
kriging method. The kriging method is stochastic and the
computation time scales with $O(n^{3})$, where *n* is the
number of observations.
Here, we can produce and interpolated yield map using the
IDW method. By default, the function uses a power of 2, but
that can be overridden by passing the argument **idp** to
the function.
If the argument **grid** is not specified, the function will
provide smoothed predictions at the same points as the input
data. This will provide predictions for any geometries data would have been removed during the cleaning step.
```{r ymp4, message=FALSE}
ymp4 <- pa_yield(input = raw.yield,
boundary = boundary,
algorithm = 'simple',
unit.system = 'metric',
clean = TRUE,
clean.sd = 3,
smooth.method = 'idw',
lbs.per.bushel = 56,
verbose = FALSE)
```
```{r plotting-ymp4, fig.width=6, fig.height=5}
ymp4
pa_plot(ymp4)
```
##### Kriging
Alternatively, we can interpolate the maps using the Kriging
method. As noted earlier, the computational time scales
quickly as the number of observations increases. Therefore,
we will add the argument "maxdist" to decrease computational
time. It should be noted that the value at which the user
sets the "maxdist" argument should be determined after
examining the variogram.
```{r ymp5, eval = FALSE}
ymp5 <- pa_yield(input = raw.yield,
boundary = boundary,
algorithm = 'simple',
unit.system = 'metric',
clean = TRUE,
clean.sd = 3,
smooth.method = 'krige',
lbs.per.bushel = 56,
verbose = FALSE,
maxdist = 50)
```
```{r, include=FALSE}
extd.dir <- system.file("extdata", package = "pacu")
ymp5 <- readRDS(file.path(extd.dir, 'yield-map-5.rds'))
```
```{r plotting-ymp5, fig.width=6, fig.height=5}
ymp5
pa_plot(ymp5, plot.var = 'yield')
```
We can also investigate the variogram
```{r plot-ymp5-variogram, fig.width=6, fig.height=5}
pa_plot(ymp5, plot.type = 'variogram')
```
#### RITAS
##### Initial example
When algorithm is **ritas**, the function expects an
argument **data.columns** that indicates crop mass or crop
flow, moisture, interval, angle, swath, and distance.
Additionally, the function expects an argument
**data.units** to indicate the units of the data columns.
When neither of the two previous arguments is provided, the
function uses a dictionary of common names to try and search
for these columns. Additionally, the function will attempt
to guess the units of the columns.
The **ritas** algorithm is more computationally intensive
than the **simple** algorithm. Some of the steps can be sped
up by specifying the argument "cores". This will allow
paralellization of some of the processing steps.
```{r ritas-initial-example, eval =FALSE}
ymp6 <- pa_yield(input = raw.yield,
algorithm = 'ritas',
lbs.per.bushel = 56,
unit.system = 'metric',
verbose = FALSE)
```
In this case, since the function is able to guess the
columns and the units correctly, this would be the same as
the chunk below.
```{r ritas-sp-units, eval=FALSE}
ymp6 <- pa_yield(input = raw.yield,
data.columns = c(flow = 'FLOW', moisture = 'MOISTURE', interval = 'CYCLES', width = 'SWATH', distance = 'DISTANCE'),
data.units = c(flow = 'lb/s', moisture = '%', interval = 's', width = 'in', distance = 'in'),
unit.system = 'metric',
algorithm = 'ritas',
verbose = FALSE)
```
```{r, include=FALSE}
ymp6 <- readRDS(file.path(extd.dir, 'yield-map-6.rds'))
```
```{r plotting-ymp6, fig.width=6, fig.height=5}
pa_plot(ymp6)
```
##### Complete RITAS algorithm
In the previous yield map, we did not conduct any smoothing, so technically we did not complete the **ritas** algorithm. Following the algorithm's steps, the best results should be expected by setting **smooth.method** to "krige". The function also includes the option to Krige in the log scale when the distribution of the yield data is heavily skewed. In this case, the argument **fun** should be set to **'log'**.
```{r ritas-final, eval=FALSE}
ymp7 <- pa_yield(input = raw.yield,
boundary = boundary,
algorithm = 'ritas',
smooth.method = 'krige',
unit.system = 'metric',
lbs.per.bushel = 56,
verbose = FALSE,
maxdist = 50)
```
```{r, include=FALSE}
ymp7 <- readRDS(file.path(extd.dir, 'yield-map-7.rds'))
```
```{r plotting-ymp7, fig.width=6, fig.height=5}
ymp7
pa_plot(ymp7)
```