Leaf Disease Detection Using Color Histogram and Random Forest on
Pongamia Pinnata (L.) Pierre
Sarifah Agustiani, Agus Junaidi, Yoseph Tajul Arifin, Dwi Puji Hastuti, Sopiyan Dalis,
Kartika Yuliantari and Fauzi Syarief
Universitas Bina Sarana Informatika, Jakarta, Indonesia
Keywords:
Color Histogram, Random Forest, Pongamia Pinnata (L.) Pierre.
Abstract:
Increasing energy consumption, which is disproportionate to the energy supply, has resulted in an urge to
seek renewable alternative energy sources that are environmentally friendly to meet energy needs. One plant
with great potential to be used as an alternative fuel/biodiesel which environmentally friendly is Pongamia
Pinnata (L.) Pierre. Besides many benefits and advantages of growing fast in tropical and sub-tropical areas,
maintaining growth, and meeting the supply of bioenergy, it is necessary to have an intelligent system that
can detect diseases in these plants. The research aims to classify Pongamia Pinnata into healthy and diseased
categories. Hopefully, this system will prevent plant disease transmission and less-than-optimal growth. The
method uses a color histogram as a feature extraction to recognize the characteristics of each image. In
contrast, it uses a random forest algorithm for the classification process, and the accuracy reaches 99.79%.
1 INTRODUCTION
Indonesia is currently facing energy problems. The
energy situation in Indonesia is caused by an in-
crease in energy consumption which is not propor-
tional to the energy supply required. Due to the ever-
increasing demand, this oil-exporting country has be-
come an oil-importing country (Evans, 2021). Ac-
cording to the Central Statistics Agency (BPS), In-
donesia’s oil import volume from January to Septem-
ber 2022 was 30.06 million tonnes. This volume in-
creased by 16.89% compared to the same period in
the previous year. More precisely, imports of 11.23
million tonnes of oil increased by 7.7%, and implica-
tion of oil products amounted to 18.84 million tonnes,
an increase of 23.15% from January to September
2022, which can be seen in Figure 1. (Kusnandar,
2022)
It has resulted in an urge to seek renewable al-
ternative energy sources that are environmentally
friendly to meet energy needs. One uses biofuels from
biological sources, such as bioethanol, biodiesel, and
biogas. Several plants that can produce biofuels are
oil palm, coconut, jatropha, cotton, canola, rape-
seed for biodiesel, cassava, sweet potato, sugar cane,
sorghum, sago, palm sugar, nipa palm, and lontar for
bioethanol (Maretta et al., 2016). One plant with great
potential to be used as an alternative fuel/biodiesel
that is environmentally friendly is Pongamia pinnata
(L.) Pierre plant (Tamin and Puri, 2020). This plant is
one of the most potential biodiesel raw materials to be
developed in Indonesia, spread naturally from Suma-
tra to Papua (A. Aminah and A. Suryani, 2017). Apart
from the Pongamia pinnata plant, there are several
other bioenergy crops such as oil palm, corn and soy-
beans, and others, but these plants’ growth has been
detention due to dependence on fertile soil (Evans,
2021).
Figure 1: Oil Import Volume.
Meanwhile, the Pongamia pinnata plant grows
fast in tropical and subtropical regions, even with
Agustiani, S., Junaidi, A., Arifin, Y., Hastuti, D., Dalis, S., Yuliantari, K. and Syarief, F.
Leaf Disease Detection Using Color Histogram and Random Forest on Pongamia Pinnata (L.) Pierre.
DOI: 10.5220/0012445100003848
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Advanced Information Scientific Development (ICAISD 2023), pages 143-148
ISBN: 978-989-758-678-1
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
143

low rainfall (Dendang and Suhaendah, 2017). The
Pongamia pinnata plant, apart from being a source
of energy, is also versatile because it can be used as
a greening plant, medicinal plant, windbreaker plant,
animal feed, and vegetable pesticide (Suita and Syam-
suwida, 2016). Several studies on Pongamia pinnata
have been carried out, especially in the sustainabil-
ity of renewable energy (Radhiana, 2023)(Khasanah,
2020)(Mitra, 2021) composition and excellence in
agriculture (Usharani et al., 2019). But besides hav-
ing advantages, of course, every plant also has threats;
because there are many biotic and abiotic factors, the
normal growth of these plants is affected in most
cases, especially in the leaves, flowers, and fruit of
these plants, often resulting in a decrease in the qual-
ity and quantity of the growth of these plants. So that
growth is not optimal and even causes death for some
time, which ultimately has an impact on reducing the
environmental and economic value of plants (More
et al., 2019). Clear and limited structural changes
can characterize disease symptoms in these plants. It
shows spots or spots on plant parts such as leaves,
fruit, and roots (Sutarman, 2017). However, the most
obvious symptoms can be from the leaves because
changes in the color and shape of the spots on the
leaves are more obvious, so the leaves can be used
as a first step to detect disease in this plant.
The most modern approach is machine learning
or deep learning using various algorithms to increase
recognition and accuracy in detecting and diagnosing
plant diseases (Maniyath, 2018), as previous research
has been done in classifying leaf images on two differ-
ent plants, namely Jatropha Curcas L and Pongamia
Pinnata L, which uses machine learning with segmen-
tation and classification techniques (Chouhan et al.,
2021). This study also aims to classify the images of
Pongamia pinnata leaves into two categories, healthy
and diseased leaves, using the color histogram as a
feature extraction. For the classification process, the
random forest algorithm is used.
In some studies, the color histogram is used to
track long-distance vehicles. The results show that
the proposed method improves accuracy in real-time
applications, as expected in the study (Yankun et al.,
2021); other studies conducted in video data re-
trieval showed appropriate performance (Saravanan
and Surendiran, 2019). Likewise, the use of the ran-
dom forest algorithm has been carried out in several
previous studies; it has an accuracy value of 99.65%
in classifying rice leaf disease (Agustiani et al., 2022).
Other research was predicting rainfall with an accu-
racy of 99.45% (Primajaya and Sari, 2018), the price
prediction of mobile phones (Saadah and Salsabila,
2021), forecasting crypto assets in the futures market
(Orte et al., 3 01), Forest Mapping (Purwanto et al.,
2023), Alzheimer’s disease prediction (Singh et al.,
2023) lupus disease prediction (Chen, 2022). Thus,
in this research, it is proposed to apply the random
forest method with color histogram feature extraction
to classify Pongamia leaf images in healthy and dis-
eased classes with the hope that this model can pro-
vide accurate model performance in the classification
process.
2 METHODS
The stages started with collecting data in the form
of secondary data, determining the location of the
dataset, then making arrangements beginning from
the number of images in each class, the size of the
pixels, and the dimensions of the images used in the
research, then implementing feature extraction to take
one of the features of each image; thus the difference
between one image and another is known, the appli-
cation of this feature extraction will later produce fea-
ture values that can be used in the classification pro-
cess. The feature extraction is color feature extrac-
tion, namely the color histogram, while the random
forest is used for the classification process in this re-
search.
Figure 2: Research Method.
ICAISD 2023 - International Conference on Advanced Information Scientific Development
144

2.1 Dataset
The dataset used in this research is one of 12 types of
plants found in the Collection of Different Categories
of Leaf Images dataset. Each dataset is named from
P0 to P11, and the Pongamia Pinnata (P7) dataset is
used. All leaf images are collected from Shri Mata
Vaishno Devi University, Katra. Shooting is done in a
closed environment. This acquisition process is fully
wi-fi enabled. All photos were taken using the built-
in Nikon D5300 camera with performance times for
JPEG shooting in single shot mode (seconds/frame,
max resolution) = 0.58 and for RAW+JPEG = 0.63.
Images were taken in jpg format with an 18-55mm
lens with sRGB color representation, 24-bit depth,
two resolution units, 1000-ISO, and no flash. Dataset
details can be seen in Table 1:
Table 1: Pongamia Pinnata Dataset (Chouhan et al., 2019).
No Citra Sample Class Total
1 Diseased 276
2 Healthy 322
Total Data 598
2.2 Feature Extraction
Feature Extraction or feature extraction is a feature
taken by the score that will analyze for further pro-
cessing. Feature extraction aims to find significant
feature areas in images depending on their intrinsic
characteristics and application. These regions can be
defined in a global or local environment and distin-
guished by shape, texture, size, intensity, statistical
properties, etc. (Verma and Salour, 2020)
2.3 Color Histogram
Color Histogram is a way of describing color content
by counting each color that appears in an image. A
color quantization process is needed for stains to de-
velop faster because calculating the number of RGB
color variations that occur takes a long time. Color
quantization is the separation of color components
into a certain range of distances (Beauty and Sari,
2019). A histogram can consist of 48 color ranges;
each square defines a pixel. These areas represent
the different intensity levels of each RGB component.
Then the value of each bin is normalized by dividing
that value by the total number of pixels in the image
(Simarmata et al., 2019). When specifying a color
histogram, you can use the image’s red, Green, and
Blue (RGB) colors. This combination forms different
colors, determining the value range (Gandhi and Lina,
2021).
The Color Histogram is one of the most effec-
tive and suitable color feature representations because
the color space is clearly chosen and then displays
the histogram concisely. In digital image process-
ing, the RGB color space is generally used consid-
ering the advantages of concise representation, low
computational complexity, and robustness to geomet-
ric changes (Zhou et al., 2018).
2.4 Random Forest
The RF comprises some decision trees, and each tree
obtains its positional effect by utilizing a different
classification. This method allows evaluation of the
sampling allocation using the random sampling tech-
nique (Murugan et al., 2019) RF classification is an
ensemble technique that continuously uses bootstrap-
ping. Bootstrapping guarantees that whatever deci-
sion tree is in the random forest is different, lowering
the RF variance. RF classification combines multiple
decision trees for the final assessment; consequently,
the RF classifier has strong generalizability. RF
consistently outperforms all other classification tech-
niques in terms of precision without difficulty with
unbalanced and overfitting datasets (Balyan, 2022)
RF is based on constructing multiple decision
trees, each of which functions as a classifier. In RF,
each tree is sampled from the original data set to cre-
ate sub-datasets. Sub-datasets are entered into each
decision tree, and each decision tree outputs its re-
sults. The final decision result is determined by vot-
ing from all decision trees. Trees in RF don’t use all
the features to choose a form. Instead, some parts are
randomly selected from all elements. Then the op-
timal amount is set from the randomly selected fea-
tures. Because of this, the forest deviation usually in-
creases slightly (relative to the variation of one non-
random tree), but the variance also decreases, which
can not only compensate for the increased deviation
but also produce better results (Li et al., 2020)—the
concept of tree decision calculated via the equation
entropy value and information gain value. The fol-
lowing is the equation for the entropy value and the
information gain value (Sandag, 2020).
Entropy(Y ) = −
∑
t
P(c|Y)Log
2
P(c|Y) (1)
Note:
Leaf Disease Detection Using Color Histogram and Random Forest on Pongamia Pinnata (L.) Pierre
145

Y = Case Set
P(c—Y) = Proporsion Y’score to C class
In f ormationgain(Y, a) =
Entropy(Y ) −
∑
vevalues(a)|
Y
v
Y
a
|Entropy(Y
v
)
(2)
Information:
Values (a) = Possible values in a case set a
Y
v
=Subclass of Y with class v, which is related to
class a.
Y
a
= All values corresponding to a
3 RESULT AND DISCUSSION
Based on research that has been done to classify the
image of Pongamia pinnata leaves in the category of
sick and healthy. The application of a color histogram
can extract color features from a photo and represent
the image as one-dimensional RGB, then form a his-
togram of an image. As in picture 3.
Figure 3: Extraction results of Pongamia Pinnata L.
Besides producing in the form of a histogram, this
feature extraction also has different feature values in
each image. The feature values resulting from feature
extraction can be seen in table 2.
Table 2: Extraction results of Pongamia Pinnata L.
No Leaf Sample Feature Value
1 Leaf Healthy 1 774337
2 Leaf Healthy 2 891746
3 Leaf Healthy 3 891497
4 Leaf Healthy 4 856789
5 Leaf Diseased 1 375609
6 Leaf Diseased 2 344622
7 Leaf Diseased 3 398179
8 Leaf Diseased 4 593341
Table 2 shows the results of the extraction of sample
images in the form of feature values; from these re-
sults, the feature values produced in the sick class and
the healthy class are different. The unhealthy type
has the lowest score of 344622 and the highest score
of 593341, while the therapeutic class has the lowest
score of 774337 and the highest of 891746. Then clas-
sification is carried out from this feature value. The
classification process is carried out using a random
forest, with a data ratio of 8:2, meaning that 80% is
used for training data, and 20% is used for test data.
Apart from using RF, several other classification al-
gorithms were tested as a comparison to see the per-
formance of the different algorithms in classifying the
Duan Pongamia image. The results of the test can be
seen in Table 3.
Table 3: Machine Learning Algorithm Comparison.
Methods Accuracy Standard Deviation
RF 0,997917 0,006250
CART 0,991578 0,010316
LR 0,989583 0,016796
KNN 0,987367 0,016845
NB 0,985372 0,016289
LDA 0,910106 0,036019
Table 3 shows that RF produces the highest accu-
racy value reaching 0.997917, and the lowest accu-
racy is made from LDA, which equals 0.910106. The
difference in accuracy results for the different meth-
ods can also be seen in Figure 4.
Figure 4: Algoritma Clarification Comparison.
3.1 Conclusion
Based on the results carried out on the Pongamia Pin-
nata dataset, which bring through the application of
color histogram feature extraction and classification
using a random forest which resulted in an accuracy
performance of 99.79%. This study also compared
several other classification algorithms such as Logis-
tic Regression which produced an accuracy value of
ICAISD 2023 - International Conference on Advanced Information Scientific Development
146

98.95%, Linear Discriminant Analysis 91.01%, K-
Nearest Neighbors 98.73%, Classification and Re-
gression Tree 99.15% and Na
¨
ıve Bayes 98.53%. The
test results show that the Random Forest algorithm
produces the highest accuracy value compared to sev-
eral other methods.
As noted, the research does not aim to replace the
plant disease diagnosis process carried out by experts
but only seeks to complement it. The existence of
machine learning methods can only predict with the
level of accuracy by the accuracy obtained; still, test-
ing through the laboratory is the most reliable way to
diagnose plant diseases. However, the model imple-
mentation is used to help smallholders who need help
to get a quick response from experts.
For further research, other feature extraction can
be applied, which is not just one type; a feature extrac-
tion combination sees the difference in performance
results in the extraction process. If there are larger
data, it can be continued using deep learning and im-
plemented in the system.
ACKNOWLEDGMENTS
The author would like to thank all parties who
have supported this research process. The author
also thanks Siddharth Singh Chouhan, Uday Pratap
Singh, Ajay Kaul, and Sanjeev Jain for publishing the
Pongamia Pinnata Leaf Image Data Repository
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