library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.1.8
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(patchwork)
## Leaves Category
leaves_data <- tibble(
Disease = c("Anthracnose", "Healthy", "Powdery Mildew", "Sooty Mould", "Scab"),
NumberofLeaves = c(521, 500, 320, 212, 0)
)
leavesPartion <- tibble(
Identification = c("Train", "Validation"),
Result = c(1236, 308)
)
allData <- ggplot(leaves_data) +
aes(x = fct_reorder(Disease,NumberofLeaves,.desc = T), y = NumberofLeaves) +
geom_col(fill = "green", width = 0.6) +
theme_minimal() +
geom_text(aes(label = paste(format(NumberofLeaves, nsmall = 0))), nudge_y = 20) +
labs(
title = "Amount of Data",
y = "",
x = ""
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(size = 12, face = "bold", angle = 50, hjust = .8)
)
Partition <- ggplot(leavesPartion) +
aes(x = Identification, y = Result, fill = Identification) +
geom_col(width = 0.4, show.legend = F) +
theme_minimal() +
scale_fill_manual(values = c(
Train = "#13D2D2", Validation = "#13D277"
))+
geom_text(aes(label = paste(format(Result, nsmall = 0))), nudge_y = 100) +
labs(
title = "Partition of Data with 80/20 split",
y = "",
x = ""
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(size = 10, face = "bold")
)
(allData | Partition) +
plot_annotation(title = "Leaf Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
## Fruit Category
FruitData <- tibble(
Disease = c("Anthracnose", "Scab","Sooty Mould", "Stem-End Rot"),
NumberofFuits = c(206, 216, 222, 219)
)
FruitPartion <- tibble(
Identification = c("Train", "Validation"),
Result = c(594, 146)
)
AllFruitData <- ggplot(FruitData) +
aes(x = fct_reorder(Disease,NumberofFuits,.desc = T), y = NumberofFuits) +
geom_col(fill = "yellow", width = 0.6) +
theme_minimal() +
geom_text(aes(label = paste(format(NumberofFuits, nsmall = 0))), nudge_y = 20) +
labs(
title = "Amount of Data",
y = "",
x = ""
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(size = 12, face = "bold", angle = 50, hjust = .8)
)
PartitionFruit <- ggplot(FruitPartion) +
aes(x = Identification, y = Result, fill = Identification) +
geom_col(width = 0.4, show.legend = F) +
theme_minimal() +
scale_fill_manual(values = c(
Train = "#13D2D2", Validation = "#13D277"
))+
geom_text(aes(label = paste(format(Result, nsmall = 0))), nudge_y = 50) +
labs(
title = "Partition of Data with 80/20 split",
y = "",
x = ""
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(size = 10, face = "bold")
)
(AllFruitData | PartitionFruit) +
plot_annotation(title = "Fruit Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
FruitData
## # A tibble: 4 × 2
## Disease NumberofFuits
## <chr> <dbl>
## 1 Anthracnose 206
## 2 Scab 216
## 3 Sooty Mould 222
## 4 Stem-End Rot 219
## Pest Category
PestData <- tibble(
Disease = c("Capsid Bug", "Mango Fruit Fly","Mango Seed Borer", "Mango Stem Borer", "Scale Insect"),
NumberofPests = c(229, 207, 203, 204,216)
)
PestsPartion <- tibble(
Identification = c("Train", "Validation"),
Result = c(842, 207)
)
AllPestData <- ggplot(PestData) +
aes(x = fct_reorder(Disease,NumberofPests,.desc = T), y = NumberofPests) +
geom_col(fill = "purple", width = 0.6) +
theme_minimal() +
geom_text(aes(label = paste(format(NumberofPests, nsmall = 0))), nudge_y = 20) +
labs(
title = "Amount of Data",
y = "",
x = ""
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(size = 10, face = "bold", angle = 50, hjust = .8)
)
PartitionPest <- ggplot(PestsPartion) +
aes(x = Identification, y = Result, fill = Identification) +
geom_col(width = 0.4, show.legend = F) +
theme_minimal() +
scale_fill_manual(values = c(
Train = "#13D2D2", Validation = "#13D277"
))+
geom_text(aes(label = paste(format(Result, nsmall = 0))), nudge_y = 50) +
labs(
title = "Partition of Data with 80/20 split",
y = "",
x = ""
) +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(size = 10, face = "bold")
)
(AllPestData | PartitionFruit) +
plot_annotation(title = "Pest Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
PestData
## # A tibble: 5 × 2
## Disease NumberofPests
## <chr> <dbl>
## 1 Capsid Bug 229
## 2 Mango Fruit Fly 207
## 3 Mango Seed Borer 203
## 4 Mango Stem Borer 204
## 5 Scale Insect 216
## Accuracy and Loss
epochsResultAccuracy <- tibble(
epochs = c(1:10),
train = c(0.6828479170799255, 0.8616504669189453, 0.9822006225585938, 0.995954692363739, 0.995954692363739, 0.9983818531036377, 1.0, 1.0, 1.0, 1.0),
validation = c(0.8344155550003052, 0.9155844449996948, 0.9870129823684692, 0.9870129823684692, 0.9805194735527039, 0.9870129823684692, 0.9870129823684692, 0.9902597665786743, 0.9902597665786743, 0.9902597665786743)
)
epochsResultAccuracy <- epochsResultAccuracy %>% pivot_longer(cols=c('train', 'validation'),
names_to='Definations',
values_to='Percentage')
epochsResultAccuracy <- epochsResultAccuracy %>%
mutate(Percentage = round(Percentage * 100, 2))
epochsResultLoss <- tibble(
epochs = c(1:10),
train = c(1.7624154090881348, 0.25737470388412476, 0.1196829080581665, 0.048868823796510696, 0.03255379945039749, 0.016790855675935745, 0.012283263728022575, 0.009626168757677078, 0.007322642486542463, 0.005301191005855799),
validation = c(0.5218960046768188, 0.18979902565479279, 0.0836237370967865, 0.054363932460546494, 0.045896977186203, 0.034609224647283554, 0.03138633072376251, 0.028076760470867157, 0.022517498582601547, 0.023578153923153877)
)
epochsResultLoss <- epochsResultLoss %>% pivot_longer(cols=c('train', 'validation'),
names_to='Definations',
values_to='Percentage')
epochsResultLoss <- epochsResultLoss %>%
mutate(Percentage = round(Percentage * 100, 2))
Acc <- epochsResultAccuracy %>% ggplot(aes(x = epochs, y = Percentage, color = Definations, groups = Definations)) +
geom_line(linewidth = 1,
) +
scale_color_manual(
values = c(train = "#7FC97F",
validation = "#666666")
) +
ggtitle("Training and Validation ACCURACY while Fitting in model") +
theme_minimal() +
theme(
legend.position = "none",
plot.title = element_text( hjust = 0.4, size = 10)
)
loss <- epochsResultLoss %>% ggplot(aes(x = epochs, y = Percentage, color = Definations, groups = Definations)) +
geom_line(size = 1) +
scale_color_manual(
values = c(train = "#7FC97F",
validation = "#666666")
) +
ggtitle("Training and Validation LOSS while Fitting in model") +
theme_minimal() +
theme(
plot.title = element_text(hjust = .5, size = 10),
)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
(Acc | loss) +
plot_annotation(title = "Leaves Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
## Accuracy and Loss
epochsResultAccuracy <- tibble(
epochs = c(1:10),
train = c(0.5651558041572571, 0.8654391169548035, 0.9461756348609924, 0.9815863966941833, 0.9915013909339905, 0.9957507252693176, 0.9985835552215576, 1.0, 1.0, 0.9985835552215576),
validation = c(0.7183908224105835, 0.8275862336158752, 0.8678160905838013, 0.8678160905838013, 0.8793103694915771, 0.9137930870056152, 0.9137930870056152, 0.9252873659133911, 0.9195402264595032, 0.9252873659133911)
)
epochsResultAccuracy <- epochsResultAccuracy %>% pivot_longer(cols=c('train', 'validation'),
names_to='Definations',
values_to='Percentage')
epochsResultAccuracy <- epochsResultAccuracy %>%
mutate(Percentage = round(Percentage * 100, 2))
epochsResultLoss <- tibble(
epochs = c(1:10),
train = c(1.667818546295166, 0.3990238308906555, 0.18011310696601868, 0.09337666630744934, 0.05730747431516647, 0.032070472836494446, 0.022132962942123413, 0.014580195769667625, 0.011054443195462227, 0.009471590630710125),
validation = c(0.7390711903572083, 0.5420188307762146, 0.42723801732063293, 0.478878378868103, 0.42655304074287415, 0.40426507592201233, 0.39441055059432983, 0.368407666683197, 0.38018521666526794, 0.4083041548728943)
)
epochsResultLoss <- epochsResultLoss %>% pivot_longer(cols=c('train', 'validation'),
names_to='Definations',
values_to='Percentage')
epochsResultLoss <- epochsResultLoss %>%
mutate(Percentage = round(Percentage * 100, 2))
Acc <- epochsResultAccuracy %>% ggplot(aes(x = epochs, y = Percentage, color = Definations, groups = Definations)) +
geom_line(size = 1,
) +
scale_color_manual(
values = c(train = "#7FC97F",
validation = "#666666")
) +
ggtitle("Training and Validation ACCURACY while Fitting in model") +
theme_minimal() +
theme(
legend.position = "none",
plot.title = element_text( hjust = 0.4, size = 10)
)
loss <- epochsResultLoss %>% ggplot(aes(x = epochs, y = Percentage, color = Definations, groups = Definations)) +
geom_line(linewidth = 1) +
scale_color_manual(
values = c(train = "#7FC97F",
validation = "#666666")
) +
ggtitle("Training and Validation LOSS while Fitting in model") +
theme_minimal() +
theme(
plot.title = element_text(hjust = .5, size = 10),
)
(Acc | loss) +
plot_annotation(title = "Fruit Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
## Accuracy and Loss
epochsResultAccuracy <- tibble(
epochs = c(1:10),
train = c(0.6496437191963196, 0.95961993932724, 0.9940617680549622, 0.9988123774528503, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0),
validation = c(0.8260869383811951, 0.8888888955116272, 0.9516907930374146, 0.9613526463508606, 0.990338146686554, 0.9855072498321533, 0.9806763529777527, 0.9855072498321533, 0.990338146686554, 0.990338146686554)
)
epochsResultAccuracy <- epochsResultAccuracy %>% pivot_longer(cols=c('train', 'validation'),
names_to='Definations',
values_to='Percentage')
epochsResultAccuracy <- epochsResultAccuracy %>%
mutate(Percentage = round(Percentage * 100, 2))
epochsResultLoss <- tibble(
epochs = c(1:10),
train = c(1.4157789945602417, 0.13098226487636566, 0.02590300142765045, 0.01123075745999813, 0.005555710289627314, 0.004017434548586607, 0.0031212433241307735, 0.00268653966486454, 0.0018341781105846167, 0.0018461743602529168),
validation = c(0.5210107564926147, 0.2624638080596924, 0.17602533102035522, 0.1257099062204361, 0.06716671586036682, 0.06309960037469864, 0.06333737820386887, 0.04452718794345856, 0.04162444546818733, 0.04105208069086075)
)
epochsResultLoss <- epochsResultLoss %>% pivot_longer(cols=c('train', 'validation'),
names_to='Definations',
values_to='Percentage')
epochsResultLoss <- epochsResultLoss %>%
mutate(Percentage = round(Percentage * 100, 2))
Acc <- epochsResultAccuracy %>% ggplot(aes(x = epochs, y = Percentage, color = Definations, groups = Definations)) +
geom_line(size = 1,
) +
scale_color_manual(
values = c(train = "#7FC97F",
validation = "#666666")
) +
ggtitle("Training and Validation ACCURACY while Fitting in model") +
theme_minimal() +
theme(
legend.position = "none",
plot.title = element_text( hjust = 0.4, size = 10)
)
loss <- epochsResultLoss %>% ggplot(aes(x = epochs, y = Percentage, color = Definations, groups = Definations)) +
geom_line(size = 1) +
scale_color_manual(
values = c(train = "#7FC97F",
validation = "#666666")
) +
ggtitle("Training and Validation LOSS while Fitting in model") +
theme_minimal() +
theme(
plot.title = element_text(hjust = .5, size = 10),
)
(Acc | loss) +
plot_annotation(title = "Pest Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
Leaves_barGraph_Acc_Loss <- tibble(
Identification = c("Train", "Validation"),
Accuracy = c(100, 99.02),
Loss = c(0.53, 2.36)
)
acc <- Leaves_barGraph_Acc_Loss %>%
ggplot(aes(x = Identification, y = Accuracy))+
geom_col(width = 0.4, fill = "#228B22")+
geom_text(aes(label = paste(Accuracy, "%")), nudge_y = 5)+
labs(
title = "Accuracy",
y = "",
x = ""
)+
theme_minimal() +
theme(
plot.title = element_text( hjust = 0.4, size = 12)
)
Loss <- Leaves_barGraph_Acc_Loss %>%
ggplot(aes(x = Identification, y = Loss))+
geom_col(width = 0.4, fill = "red")+
geom_text(aes(label = paste(Loss, "%")), nudge_y = 5)+
labs(
title = "Loss",
y = "",
x = ""
)+
theme_minimal()+
theme(
plot.title = element_text( hjust = 0.4, size = 12)
)
(acc | Loss) +
plot_annotation(title = "Leaf Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
Leaves_barGraph_Acc_Loss <- tibble(
Identification = c("Train", "Validation"),
Accuracy = c(100, 99.03),
Loss = c(0.18, 4.1)
)
acc <- Leaves_barGraph_Acc_Loss %>%
ggplot(aes(x = Identification, y = Accuracy))+
geom_col(width = 0.4, fill = "#228B22")+
geom_text(aes(label = paste(Accuracy, "%")), nudge_y = 5)+
labs(
title = "Accuracy",
y = "",
x = ""
)+
theme_minimal() +
theme(
plot.title = element_text( hjust = 0.4, size = 12)
)
Loss <- Leaves_barGraph_Acc_Loss %>%
ggplot(aes(x = Identification, y = Loss))+
geom_col(width = 0.4, fill = "red")+
geom_text(aes(label = paste(Loss, "%")), nudge_y = 5)+
labs(
title = "Loss",
y = "",
x = ""
)+
theme_minimal()+
theme(
plot.title = element_text( hjust = 0.4, size = 12)
)
(acc | Loss) +
plot_annotation(title = "Pest Category", theme = theme(
plot.title = element_text(hjust = 0.5, size = 20L, face = "bold")
))
