library(tidyverse)
library(ggplot2)
library(readxl)
library(dplyr)EFFECTS OF WATER STERSS ON DWARF CHERRY TOMATO
LOADING PACKAGES
LOADING PHENOSPEX DATA
data <- "C://Users//user//Downloads//Weekly_Treatment_Averages.xlsx"
# Import the data from the Excel file
data <- read_excel(data)
# Display the first few rows to confirm import
head(data)# A tibble: 6 × 13
Week treatment Cultivar `Water Stress Level` `Digital biomass mm³`
<dbl> <dbl> <chr> <chr> <dbl>
1 1 1 Micro dwarf little… Control 5948498.
2 1 2 Micro dwarf little… Mild Water Stress 4674495
3 1 3 Micro dwarf little… Severe Water Stress 5334812.
4 1 4 Micro dwarf vilma Control 4277298.
5 1 5 Micro dwarf vilma Mild Water Stress 4827718.
6 1 6 Micro dwarf vilma Severe Water Stress 3983117.
# ℹ 8 more variables: `greenness average` <dbl>, `Height mm` <dbl>,
# `Height Max mm` <dbl>, `hue average °` <dbl>, `Leaf angle °` <dbl>,
# `Leaf area mm²` <dbl>, `NDVI average` <dbl>, `PSRI average` <dbl>Exploratory Data Analysis
str(data)tibble [48 × 13] (S3: tbl_df/tbl/data.frame)
$ Week : num [1:48] 1 1 1 1 1 1 2 2 2 2 ...
$ treatment : num [1:48] 1 2 3 4 5 6 1 2 3 4 ...
$ Cultivar : chr [1:48] "Micro dwarf little red riding hood" "Micro dwarf little red riding hood" "Micro dwarf little red riding hood" "Micro dwarf vilma" ...
$ Water Stress Level : chr [1:48] "Control" "Mild Water Stress" "Severe Water Stress" "Control" ...
$ Digital biomass mm³: num [1:48] 5948498 4674495 5334812 4277298 4827718 ...
$ greenness average : num [1:48] 0.308 0.278 0.296 0.321 0.282 ...
$ Height mm : num [1:48] 125 133 157 114 157 ...
$ Height Max mm : num [1:48] 140 146 169 132 172 ...
$ hue average ° : num [1:48] 115 119 117 117 118 ...
$ Leaf angle ° : num [1:48] 44 44 45.4 42.4 42.9 ...
$ Leaf area mm² : num [1:48] 47239 35253 34298 37318 30715 ...
$ NDVI average : num [1:48] 0.713 0.696 0.707 0.719 0.691 ...
$ PSRI average : num [1:48] 0.0176 0.0116 0.0136 0.0149 0.0133 ...summary(data) Week treatment Cultivar Water Stress Level
Min. :1.00 Min. :1.0 Length:48 Length:48
1st Qu.:2.75 1st Qu.:2.0 Class :character Class :character
Median :4.50 Median :3.5 Mode :character Mode :character
Mean :4.50 Mean :3.5
3rd Qu.:6.25 3rd Qu.:5.0
Max. :8.00 Max. :6.0
Digital biomass mm³ greenness average Height mm Height Max mm
Min. : 3983117 Min. :0.2006 Min. :113.8 Min. :132.4
1st Qu.: 8851375 1st Qu.:0.2612 1st Qu.:166.0 1st Qu.:190.7
Median : 9988398 Median :0.2780 Median :179.8 Median :206.2
Mean : 9846058 Mean :0.2747 Mean :178.4 Mean :201.5
3rd Qu.:11217047 3rd Qu.:0.2921 3rd Qu.:198.0 3rd Qu.:220.1
Max. :14162383 Max. :0.3229 Max. :220.0 Max. :245.5
hue average ° Leaf angle ° Leaf area mm² NDVI average
Min. :109.1 Min. :37.99 Min. :28831 Min. :0.6563
1st Qu.:114.7 1st Qu.:41.04 1st Qu.:50122 1st Qu.:0.7093
Median :117.1 Median :42.61 Median :55351 Median :0.7220
Mean :116.5 Mean :42.88 Mean :54539 Mean :0.7188
3rd Qu.:118.6 3rd Qu.:44.54 3rd Qu.:61854 3rd Qu.:0.7304
Max. :122.9 Max. :48.77 Max. :71531 Max. :0.7438
PSRI average
Min. :-0.002165
1st Qu.: 0.007163
Median : 0.011192
Mean : 0.012660
3rd Qu.: 0.016230
Max. : 0.034163 Digital Biomass Analysis
# Optional: Label treatments T1–T6
data$treatment_label <- factor(data$treatment,
levels = 1:6,
labels = c("T1", "T2", "T3", "T4", "T5", "T6")
)
# Create boxplot (outliers hidden)
ggplot(data, aes(x = treatment_label, y = `Digital biomass mm³`, fill = treatment_label)) +
geom_boxplot(outlier.shape = NA) +
labs(
x = "Treatment",
y = "Digital Biomass (mm³)",
fill = "Treatment"
) +
theme_minimal() +
theme(
axis.title = element_text(size = 13),
axis.text = element_text(size = 11),
legend.position = "none"
)
ANOVA for Digital Biomass
data$treatment <- factor(data$treatment)
model <- aov(`Digital biomass mm³` ~ treatment, data = data)
summary(model) Df Sum Sq Mean Sq F value Pr(>F)
treatment 5 1.190e+13 2.380e+12 0.341 0.885
Residuals 42 2.935e+14 6.988e+12 Greenness Average Analysis
# Optional: Ensure treatment labels are set (T1 to T6)
data$treatment_label <- factor(data$treatment,
levels = 1:6,
labels = c("T1", "T2", "T3", "T4", "T5", "T6")
)
# Create boxplot without outliers
ggplot(data, aes(x = treatment_label, y = `greenness average`, fill = treatment_label)) +
geom_boxplot(outlier.shape = NA) +
labs(
x = "Treatment",
y = "Greenness Average",
fill = "Treatment"
) +
theme_minimal() +
theme(
axis.title = element_text(size = 13),
axis.text = element_text(size = 11),
legend.position = "none"
)
ANOVA for Greenness Average
data$treatment <- factor(data$treatment)
model_greenness <- aov(`greenness average` ~ treatment, data = data)
summary(model_greenness) Df Sum Sq Mean Sq F value Pr(>F)
treatment 5 0.009429 0.0018857 3.418 0.0111 *
Residuals 42 0.023173 0.0005517
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Plant Height Analysis
# Ensure treatment labels are defined as T1 to T6
data$treatment_label <- factor(data$treatment,
levels = 1:6,
labels = c("T1", "T2", "T3", "T4", "T5", "T6")
)
# Create the boxplot
ggplot(data, aes(x = treatment_label, y = `Height mm`, fill = treatment_label)) +
geom_boxplot(outlier.shape = NA) + # Hide outliers
labs(
x = "Treatment",
y = "Height (mm)",
fill = "Treatment"
) +
theme_minimal() +
theme(
axis.title = element_text(size = 13),
axis.text = element_text(size = 11),
legend.position = "none"
)
ANOVA for Plant Height
data$treatment <- factor(data$treatment, levels = 1:6)
model_height <- aov(`Height mm` ~ treatment, data = data)
summary(model_height) Df Sum Sq Mean Sq F value Pr(>F)
treatment 5 6098 1219.7 2.503 0.0452 *
Residuals 42 20467 487.3
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Leaf Area Analysis
# Ensure treatment labels are defined (T1 to T6)
data$treatment_label <- factor(data$treatment,
levels = 1:6,
labels = c("T1", "T2", "T3", "T4", "T5", "T6")
)
# Boxplot for Leaf Area with outliers removed
ggplot(data, aes(x = treatment_label, y = `Leaf area mm²`, fill = treatment_label)) +
geom_boxplot(outlier.shape = NA) +
labs(
x = "Treatment",
y = "Leaf Area (mm²)",
fill = "Treatment"
) +
theme_minimal() +
theme(
axis.title = element_text(size = 13),
axis.text = element_text(size = 11),
legend.position = "none"
)
ANOVA for Leaf Area
data$treatment <- factor(data$treatment, levels = 1:6)
model_leaf_area <- aov(`Leaf area mm²` ~ treatment, data = data)
summary(model_leaf_area) Df Sum Sq Mean Sq F value Pr(>F)
treatment 5 4.143e+08 82852373 0.727 0.607
Residuals 42 4.788e+09 114005904 NDVI Analysis
# Make sure treatment labels are defined
data$treatment_label <- factor(data$treatment,
levels = 1:6,
labels = c("T1", "T2", "T3", "T4", "T5", "T6")
)
# Boxplot for NDVI Average
ggplot(data, aes(x = treatment_label, y = `NDVI average`, fill = treatment_label)) +
geom_boxplot(outlier.shape = NA) +
labs(
x = "Treatment",
y = "NDVI Average",
fill = "Treatment"
) +
theme_minimal() +
theme(
axis.title = element_text(size = 13),
axis.text = element_text(size = 11),
legend.position = "none"
)
ANOVA for NDVI
model_ndvi <- aov(`NDVI average` ~ treatment, data = data)
summary(model_ndvi) Df Sum Sq Mean Sq F value Pr(>F)
treatment 5 0.003149 0.0006299 2.302 0.0617 .
Residuals 42 0.011491 0.0002736
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LOADING THE YIELD DATA
data2 <- read_excel("C://Users//user//Downloads//2.xlsx")
# Display the first few rows to confirm import
head(data2)# A tibble: 6 × 5
DATE Treatment `Fruit number` `Fruit weight (Kg)`
<dttm> <dbl> <dbl> <dbl>
1 2025-05-29 00:00:00 1 111 1.05
2 2025-05-29 00:00:00 2 77 0.625
3 2025-05-29 00:00:00 3 76 0.55
4 2025-05-29 00:00:00 4 82 0.77
5 2025-05-29 00:00:00 5 122 0.905
6 2025-05-29 00:00:00 6 66 0.43
# ℹ 1 more variable: `Average Brix (°Bx)` <dbl>Total Fruit Number
ggplot(data2, aes(x = factor(Treatment), y = `Fruit number`, fill = factor(Treatment))) +
geom_bar(stat = "identity") +
labs(x = "Treatment", y = "Total Fruit Number", fill = "Treatment") +
theme_minimal()
aov_fruit <- aov(`Fruit number` ~ factor(Treatment), data = data2)
summary(aov_fruit) Df Sum Sq Mean Sq F value Pr(>F)
factor(Treatment) 5 7665 1532.9 1.591 0.201
Residuals 24 23128 963.7 Fruit Weight Analysis
ggplot(data2, aes(x = factor(Treatment), y = `Fruit weight (Kg)`, fill = factor(Treatment))) +
geom_bar(stat = "identity") +
labs(x = "Treatment", y = "Fruit Weight (Kg)", fill = "Treatment") +
theme_minimal()
ANOVA for Fruit Weight
aov_weight <- aov(`Fruit weight (Kg)` ~ factor(Treatment), data = data2)
summary(aov_weight) Df Sum Sq Mean Sq F value Pr(>F)
factor(Treatment) 5 0.7703 0.15405 3.734 0.0121 *
Residuals 24 0.9901 0.04125
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Brix Analysis
ggplot(data2, aes(x = factor(Treatment), y = `Average Brix (°Bx)`, fill = factor(Treatment))) +
geom_bar(stat = "identity") +
labs(x = "Treatment", y = "Average Brix (°Bx)", fill = "Treatment") +
theme_minimal()
ANOVA for BRIX
aov_brix <- aov(`Average Brix (°Bx)` ~ factor(Treatment), data = data2)
summary(aov_brix) Df Sum Sq Mean Sq F value Pr(>F)
factor(Treatment) 5 16.64 3.328 1.29 0.301
Residuals 24 61.90 2.579