Data processing pipeline for Experiment 92 data collected on Crop Reporter camera within BTI’s PhenoSight facility.

#install.packages("data.table")
library(data.table)

## Warning: package 'data.table' was built under R version 4.3.3

data <- fread("Exp00092_PS_analysis_new.TXT", sep = "\t")
data

colnames(data)

##  [1] "File"           "Date"           "Time"           "Obj.No"        
##  [5] "nTmPam"         "Obj.Size"       "Obj.Xc"         "Obj.Yc"        
##  [9] "Fv/Fm"          "s.d."           "Fq'/Fm'"        "s.d."          
## [13] "NPQ"            "s.d."           "F0'"            "s.d."          
## [17] "qP"             "s.d."           "qN"             "s.d."          
## [21] "qL"             "s.d."           "qE"             "s.d."          
## [25] "qI"             "s.d."           "фno"            "s.d."          
## [29] "фnpq"           "s.d."           "npq(t)"         "s.d."          
## [33] "ChlIdx"         "s.d."           "AriIdx"         "s.d."          
## [37] "NDVI"           "s.d."           "Border"         "Mask/Border"   
## [41] "Points"         "Area (CH)"      "Mask/Area (CH)" "X-Centre"      
## [45] "Y-Centre"       "Radius"         "Area (MC)"      "Mask/Area (MC)"
## [49] "Width"          "Height"         "Area (MR)"      "Mask/Area (MR)"
## [53] "Alpha"          "Size (SK)"      "Junction (SK)"  "Endpoint (SK)" 
## [57] "Path (SK)"      "V58"

PS <- subset(data, data$Obj.No == "All")
PS1 <- subset(PS, PS$nTmPam == 1)
PS2 <- subset(PS, PS$nTmPam == 2)
PS1

PS2

PS1 <- PS1[,c(1:6, 9, 11, 13, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37)]
PS2 <- PS2[,c(1:6, 9, 11, 13, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37)]
PS1

PS2

PS1 <- PS1[,c(1:4, 6:7)]
PS2 <- PS2[,c(1:4, 6, 8:20)]
PS_extra <- subset(PS, PS$nTmPam > 2)
PS_extra <- PS_extra[,c(1:6, 9, 11, 13, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37)]
PS_extra <- PS_extra[,c(1:7)]
PS_extra

PS_melt <- melt(PS_extra, id.vars = c("File", "Date", "Time", "Obj.No", "Obj.Size", "nTmPam"))
PS_melt

PS_cast <- dcast(PS_melt, File + Date + Time + Obj.No + Obj.Size ~ variable + nTmPam)
PS_cast

PS3 <- merge(PS1, PS_cast, by = c("File", "Date", "Time", "Obj.No", "Obj.Size"))
PS4 <- merge(PS3, PS2, by = c("File", "Date", "Time", "Obj.No", "Obj.Size"))
PS4

for(i in 1:nrow(PS4)){
  PS4$PhenoTray.ID[i] <- strsplit(PS4$File[i], "_")[[1]][3]
}
length(unique(PS4$PhenoTray.ID))

## [1] 3

for(i in 1:nrow(PS4)){
  PS4$month <- substr(PS4$Date, 5, 6)
  PS4$day <- substr(PS4$Date, 7, 8)
  PS4$hour <- substr(PS4$Time, 1, 2)
}
PS4

unique(PS4$month)

## [1] "04" "02" "03"

temp <- subset(PS4, PS4$month == "02")
min(temp$day)

## [1] "02"

temp <- subset(temp, temp$day == "02")
min(temp$hour)

## [1] "11"

so first day of the experiment is February 2nd and the measurements are starting around 11:00.

Let’s calculate Time of Experiment (TOE).

As we need to correct for different days being in different months… we need to add to each month the cummulative.days that we need to get for that specific month:

PS4$cum.days <- 0
for(i in 1:nrow(PS4)){
  if(PS4$month[i] == "02"){
    PS4$cum.days[i] <- 0}
  if(PS4$month[i] == "03"){
    PS4$cum.days[i] <- 29}
  if(PS4$month[i] == "04"){
    PS4$cum.days[i] <- 60}
}
PS4

PS4$TOE <- (as.numeric(as.character(PS4$day)) - 2)*24 + (PS4$cum.days*24) + (as.numeric(as.character(PS4$hour)) - 11)
unique(PS4$TOE)

##   [1] 1511   47  582  587  594  599  606  611  618  623  630  635   54  642  647
##  [16]  654  659  666  671  678  683  690  695   59  702  707  714  719  726  731
##  [31]  738  743  750  755   66  821  824  826  829  830  831  832   71  834  835
##  [46]  837  839  846  851  858  863  870  875   78  882  887  894  899  906  911
##  [61]  918  923  930  935   83  942  947  954  959  990  992  995   90 1002 1007
##  [76] 1014 1019 1026 1031 1038 1043 1050 1055   95 1062 1067 1074 1079 1086 1091
##  [91] 1098 1103 1110 1115  102 1122 1127 1151 1152 1153 1154 1159 1163 1226 1227
## [106]    0  107 1231 1234 1235 1238 1239 1242 1243 1246 1247  114 1250 1251 1254
## [121] 1259 1266 1271 1301 1422 1427 1434  119 1439 1446 1451 1468 1486 1499 1519
## [136]  126  131  138  143  150  155  162    6  167  174  179  186  191  198  203
## [151]  210  215  222   11  227  235  239  246  251  258  263  339  340   18  342
## [166]  345  347  350  352  353   23  355  356  357  359  366  371  378  383  390
## [181]  395   30  402  407  414  419  426  431  438  443  450  455   35  462  467
## [196]  474  479  486  491  498  503  510  515   42  522  527  534  539  546  551
## [211]  558  563  570  575  827 1228 1230 1232 1240 1491  234  236  264  348

PS4

There are still some odd things in the column names - that I would like to straighten out:

colnames(PS4)[6] <- "Fv.Fm"
colnames(PS4)[7] <- "Fv.Fm.3"
colnames(PS4)[8] <- "Fv.Fm.4"
colnames(PS4)[9] <- "Fv.Fm.5"
colnames(PS4)[10] <- "Fv.Fm.6"
colnames(PS4)[11] <- "Fq.Fm"
colnames(PS4)[18] <- "phi.no"
colnames(PS4)[19] <- "phi.npq"
colnames(PS4)[20] <- "npq.t"
PS4 <- PS4[,c(1:4, 24, 29, 5:23)]
PS4

PS4 <- subset(PS4, PS4$PhenoTray.ID != "92")
unique(PS4$PhenoTray.ID)

## [1] "24681" "24682"

write.table(PS4, file = "Exp00092_PS2_Analysis_new.TXT", sep="\t", row.names = F)

Data visualization

#install.packages("ggplot2")
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.3

FvFm_graph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm, group = PhenoTray.ID)) 
FvFm_graph <- FvFm_graph + geom_line(alpha = 0.7) 
FvFm_graph <- FvFm_graph + ylab("Fv/Fm") + xlab("Hours of Imaging")
FvFm_graph

FvFm3_graph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm.3, group = PhenoTray.ID)) 
FvFm3_graph <- FvFm3_graph + geom_line(alpha = 0.7) 
FvFm3_graph <- FvFm3_graph + ylab("Fv/Fm3") + xlab("Hours of Imaging")
FvFm3_graph

FvFm4_graph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm.4, group = PhenoTray.ID)) 
FvFm4_graph <- FvFm4_graph + geom_line(alpha = 0.7) 
FvFm4_graph <- FvFm4_graph + ylab("Fv/Fm4") + xlab("Hours of Imaging")
FvFm4_graph

FvFm5_graph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm.5, group = PhenoTray.ID)) 
FvFm5_graph <- FvFm5_graph + geom_line(alpha = 0.7) 
FvFm5_graph <- FvFm5_graph + ylab("Fv/Fm5") + xlab("Hours of Imaging")
FvFm5_graph

FvFm6_graph <- ggplot(data=PS4, aes(x= TOE, y=Fv.Fm.6, group = PhenoTray.ID)) 
FvFm6_graph <- FvFm6_graph + geom_line(alpha = 0.7) 
FvFm6_graph <- FvFm6_graph + ylab("Fv/Fm6") + xlab("Hours of Imaging")
FvFm6_graph

FqFm_graph <- ggplot(data=PS4, aes(x= TOE, y=Fq.Fm, group = PhenoTray.ID)) 
FqFm_graph <- FqFm_graph + geom_line(alpha = 0.7) 
FqFm_graph <- FqFm_graph + ylab("Fq/Fm") + xlab("Hours of Imaging")
FqFm_graph

NPQ_graph <- ggplot(data=PS4, aes(x= TOE, y=NPQ, group = PhenoTray.ID)) 
NPQ_graph <- NPQ_graph + geom_line(alpha = 0.7) 
NPQ_graph <- NPQ_graph + ylab("NPQ") + xlab("Hours of Imaging")
NPQ_graph

qP_graph <- ggplot(data=PS4, aes(x= TOE, y=qP, group = PhenoTray.ID)) 
qP_graph <- qP_graph + geom_line(alpha = 0.7) 
qP_graph <- qP_graph + ylab("qP") + xlab("Hours of Imaging")
qP_graph

qN_graph <- ggplot(data=PS4, aes(x= TOE, y=qN, group = PhenoTray.ID)) 
qN_graph <- qN_graph + geom_line(alpha = 0.7) 
qN_graph <- qN_graph + ylab("qN") + xlab("Hours of Imaging")
qN_graph

qL_graph <- ggplot(data=PS4, aes(x= TOE, y=qL, group = PhenoTray.ID)) 
qL_graph <- qL_graph + geom_line(alpha = 0.7) 
qL_graph <- qL_graph + ylab("qL") + xlab("Hours of Imaging")
qL_graph

qE_graph <- ggplot(data=PS4, aes(x= TOE, y=qE, group = PhenoTray.ID)) 
qE_graph <- qE_graph + geom_line(alpha = 0.7) 
qE_graph <- qE_graph + ylab("qE") + xlab("Hours of Imaging")
qE_graph

qI_graph <- ggplot(data=PS4, aes(x= TOE, y=qI, group = PhenoTray.ID)) 
qI_graph <- qI_graph + geom_line(alpha = 0.7) 
qI_graph <- qI_graph + ylab("qI") + xlab("Hours of Imaging")
qI_graph

phi.no_graph <- ggplot(data=PS4, aes(x= TOE, y=phi.no, group = PhenoTray.ID)) 
phi.no_graph <- phi.no_graph + geom_line(alpha = 0.7) 
phi.no_graph <- phi.no_graph + ylab("phi.no") + xlab("Hours of Imaging")
phi.no_graph

phi.npq_graph <- ggplot(data=PS4, aes(x= TOE, y=phi.npq, group = PhenoTray.ID)) 
phi.npq_graph <- phi.npq_graph + geom_line(alpha = 0.7) 
phi.npq_graph <- phi.npq_graph + ylab("phi.npq") + xlab("Hours of Imaging")
phi.npq_graph

npq.t_graph <- ggplot(data=PS4, aes(x= TOE, y=npq.t, group = PhenoTray.ID)) 
npq.t_graph <- npq.t_graph + geom_line(alpha = 0.7) 
npq.t_graph <- npq.t_graph + ylab("npq(t)") + xlab("Hours of Imaging")
npq.t_graph

ChlIdx_graph <- ggplot(data=PS4, aes(x= TOE, y=ChlIdx, group = PhenoTray.ID)) 
ChlIdx_graph <- ChlIdx_graph + geom_line(alpha = 0.7) 
ChlIdx_graph <- ChlIdx_graph + ylab("ChlIdx") + xlab("Hours of Imaging")
ChlIdx_graph

AriIdx_graph <- ggplot(data=PS4, aes(x= TOE, y=AriIdx, group = PhenoTray.ID)) 
AriIdx_graph <- AriIdx_graph + geom_line(alpha = 0.7) 
AriIdx_graph <- AriIdx_graph + ylab("AriIdx") + xlab("Hours of Imaging")
AriIdx_graph

NDVI_graph <- ggplot(data=PS4, aes(x= TOE, y=NDVI, group = PhenoTray.ID)) 
NDVI_graph <- NDVI_graph + geom_line(alpha = 0.7) 
NDVI_graph <- NDVI_graph + ylab("NDVI") + xlab("Hours of Imaging")
NDVI_graph

library(cowplot)
pdf("Exp00092_plots.pdf", height = 10, width = 7)
plot_grid(FvFm_graph, FqFm_graph, npq.t_graph, NPQ_graph, NDVI_graph, AriIdx_graph, ChlIdx_graph, labels = "AUTO", ncol = 2)
dev.off()

## quartz_off_screen 
##                 2

Experiment 92 processing

Data visualization