imageField {rPAex}

Matching Pixels With Field Book

Description

The function uses the raster image of all bands. It generates the limits of the unit and extracts the values of each pixel of the plot n x m units (n, m = 1,2, ...). The function requires the dimensions of the unit observed and the number of units per row (width) and column (length). The result is a table with image information and the characteristics of the experimental unit.

Usage

imageField(r, Q, ny, nx, dy, dx,  start=1, plotting = TRUE, ...)

Arguments

r

raster image

Q

References points of de area

ny

Number of experimental units along the plot (y axis)

nx

Number of experimental units across the plot (x axis)

dy

Wide of unit plots

dx

Length of unit plots

start

Number of the first experimental unit

plotting

Overlap the units in the area, TRUE or FALSE

...

Other parameters the plot

Value

parameters

Parameters of experimental design in precision agriculture

Qbase

Image data frame with location in field

coordinates.EU

The limits of each experimental unit

See Also

EUsPoint, fixedPoint, fourPoint, designRaster, cassava

Examples

library(rPAex)
data(cassava)
r <- terra::rast(cassava, type="xyz")
# x11()
# terra::image(r)
# p<-locator(2)
# e<-terra::ext(unlist(p))
e <- terra::ext(287691.9, 287708.6, 8664188, 8664203)
rc <- terra::crop(r,e)
# Selection of experimental units, p1 and p2 in terra::image(r)
p1<-list(x=c(287698.34, 287701.14, 287702.33), 
         y=c(8664200.89, 8664201.65, 8664190.67))
p2<-list(x=c(287701.56, 287704.37, 287705.24),
	 y=c(8664198.68, 8664199.44, 8664191.46))        
q1<-fourPoint(p1)
q2<-fourPoint(p2)
# dimension of the experimental unit
dy=1; dx=0.9
op<-par(mar=c(0,0,3,0))
terra::image(rc,main="Selection of experimental units\nCassava crop",axes=FALSE)
img1<-imageField(rc, q1, ny=11, nx=3, dy, dx, plotting = TRUE, border="blue",lwd=1)
img2<-imageField(rc, q2, ny=8, nx=3, dy, dx, start=34,plotting = TRUE, border="blue",lwd=1)
# Spectral data of selected units
R<-rbind(img1$Qbase,img2$Qbase)
head(R)
Q<-agricolae::tapply.stat(R[,2:3],R[,1],mean)
text(Q[,2],Q[,3],Q[,1],cex=1)
par(op)