=Paper=
{{Paper
|id=Vol-3944/paper2
|storemode=property
|title=Advanced Binary Classification for Disease Detection in Trees Using a novel Machine-Deep Learning method
|pdfUrl=https://ceur-ws.org/Vol-3944/paper2.pdf
|volume=Vol-3944
|authors=Marouane Kihal,Lamia Hamza,Mohammed Charif Kihal
|dblpUrl=https://dblp.org/rec/conf/cari/KihalHK24
}}
==Advanced Binary Classification for Disease Detection in Trees Using a novel Machine-Deep Learning method==
https://ceur-ws.org/Vol-3944/paper2.pdf
Advanced Binary Classification for Disease Detection in
Trees Using a novel Machine-Deep Learning method
Marouane Kihal1 , Lamia Hamza1 and Mohammed Charif Kihal2
1
Laboratory of Medical Informatics (LIMED), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria
2
Laboratory of electrical engineering and industrial electronics (L2EI), Faculty of Science and Technology, Department of Electrical
Engineering, University of Jijel, 18000 Jijel, Algeria
Abstract
Detecting plant health is crucial to prevent losses in the productivity and quality of agricultural products. This
study focuses on identifying plant diseases through the visual examination of leaf patterns. Specifically, we
aim to efficiently determine the health status (diseased or healthy) of lemon trees by analyzing the condition
of their leaves using nine different machine learning algorithms optimized with a deep learning approach. Our
experimental results demonstrate that this method achieves a high accuracy rate of 93%, surpassing other machine
learning techniques. The integration of multiple machine learning algorithms followed by deep learning proves
to be a promising solution for effective detection of tree diseases.
Keywords
Trees diseases detection, Binary detection, Machine learning, Deep Learning, Citrus fruit
1. Introduction
Before the advent of AI-based methods, early detection of disease in trees was often hampered by
rudimentary, empirical methods. Observers often had to rely on visible signs such as changes in
leaf color or obvious external symptoms, limiting the ability to identify diseases at an early stage of
development. In addition, the diversity of diseases and the variability of forest environments made
it difficult to implement uniform and reliable detection protocols. These challenges highlighted the
urgent need for innovative solutions to improve the efficiency and accuracy of tree disease monitoring.
Machine Learning (ML) in the field of tree disease detection involves using algorithms to analyze
data such as images or sensory data to identify characteristic signs of disease. This approach enables
computer systems to learn from data without being explicitly programmed, thus improving the accuracy
and efficiency of diagnosis. On the other hand, Deep Learning (DL), an advanced branch of machine
learning, uses artificial neural networks to perform complex recognition and classification tasks. In
tree disease detection, deep learning enables in-depth analysis of high-resolution images, facilitating
early detection of infection or structural damage thanks to its ability to extract significant features
and patterns from large quantities of data. Thus, detecting plant health with ML and DL algorithms is
crucial to prevent losses in yield and quality of agricultural products by examining visually observable
patterns on plants, such as leaves, stems, and fruits.
In this paper, we aim at efficiently binary detection of the health status of lemon trees (diseased or
healthy) from the state of the leaves using an approach based on deep learning optimization of nine
machine learning algorithms. The main contribution of this article is to design the following:
• Binary detection of the health status of trees from the state of the leaves using an approach based
on deep learning optimization of nine machine learning algorithms.
• Comparison of our approach with nine Machine Learning algorithms.
• Application of four different evaluation metrics to compare results.
Proceedings of the DAAfrica’2024 workshop
⇤
Lamia Hamza
†
These authors contributed equally.
� marouane.kihal@univ-bejaia.dz (M. Kihal); lamia.hamza@univ-bejaia.dz (L. Hamza); mc.kihal@univ-jijel.dz (M. C. Kihal)
� 0000-0002-6675-7087 (M. Kihal); 0000-0002-5436-3099 (L. Hamza); 0000-0002-2075-2491 (M. C. Kihal)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
8
The rest of this paper is organized as follows: Section 2 reviews related works. Section 3 introduces
our proposed approach for detecting diseased trees. In Section 4, we evaluate our results. Finally,
Section 5 concludes the paper and suggests potential directions for future research.
2. Related works
Numerous studies have focused on detecting diseases from the leaves of various plants. For tomato,
Prajwala et al.[1] proposed a variation of the convolutional neural network model, LeNet, to detect and
identify diseases in tomato leaves. For rice, Kawcher et al.[2] introduced a rice leaf disease detection
system utilizing machine learning techniques. Additionally, a study on potatoes [3] suggests a model
that employs pre-trained models for fine-tuning to extract relevant features from the dataset, followed
by a logistic regression classifier. For lemon tree, Banni and Sksvmacet [4] proposed a model that
utilises GLCM (Grey Level Co-Occurrence Matrix) algorithms for the detection of citrus leaf disease.
However, this study was unable to obtain appropriate outcomes in order to classify the image data.
This study yielded an accuracy rate of approximately 85.71%. Recently, more work has been based on
machine learning and deep learning algorithms. Pramanik et al. [5] used Transfer Learning-based Deep
Learning models, specifically DenseNet-201, ResNet-50, ResNet-152V2, and Xception, to classify lemon
leaf diseases. Xception outperformed all other models in terms of accuracy, with 94.34%. Khattak et al.
[6] suggested the use of a CNN model to distinguish between healthy fruits and leaves and those that
have prevalent citrus diseases, including black spot, canker, scab, greening, and Melanose. This CNN
model has a test accuracy of 94.55%. Hassam et al. [7] proposed a single-stream convolutional neural
network architecture to identify illnesses in citrus fruits. The expanded citrus dataset (Citrus Fruits,
Leaves, and Hybrid Datasets) were employed in the experiment, and the accuracy was 99.4%, 99.5%,
and 99.7% respectively. However, the study reveals little redundant information in the collected deep
features. Yuan [8] evaluated and compared two deep learning models, DenseNet and MobileNet, for the
case study of lemon leaf image classification. This study indicated that MobileNet is more promising
in practice. Islam et al. [9] used InceptionV3 and VGG16 deep learning models to classify diseases in
citrus leaves, including melanoses, canker, scab, and black spot. InceptionV3 outperforms VGG16 in
terms of accuracy. Despite numerous studies in the field of agronomy, no previous work has employed
a comprehensive set of ML algorithms, including DL, to optimize the detection of tree diseases.
3. Our approach
In this Section, we propose the use of nine ML algorithms and DL techniques to enhance the detection
of tree diseases from leaf images. Various machine learning techniques that we employed for this task,
including:
1. Ada Boost : A technique of grouping a number of individual weak classifiers all together in a
single powerful classifier.
2. Logistic Regression : A model that maximises the probability for a binary dependent variable.
3. Decision Trees : A technique that divides the data into subsets according to specific values of
the input dimensions, it can reveal the patterns correlated with plant diseases.
4. Random Forests : A learning algorithm that builds many decision trees in the training process.
5. Support Vector Machines : A method aims to select the hyperplane that provides the maximum
distance between classes of healthy and diseased leaves in the space of features.
6. k-Nearest Neighbors : Categorizes a leaf based on the results of a majority vote on the k nearest
neighbors using distance.
7. Naive Bayes : Utilize the Bayesian model with strong (naive) hypothesis of feature’s indepen-
dence.
8. Linear Discriminant Analysis : A method aims to determine the best way of dividing the
different classes.
9
Figure 1: General architecture of our proposed method
9. Extreme Gradient Boosting : An optimized gradient boosting.
As mentioned the result of the nine ML algorithms will be passed by a deep learning model to make
and optimize the finale decision as shown in Fig. 1.
4. Experimentation and results
In this Section, we will present the details and results of the experiments conducted on images of lemon
trees. The images used for this study were obtained from the Collection of Different Category of Leaf
Images[10]
4.1. Methodology
We employed the nine machine learning algorithms discussed in the previous section, followed by
implementing a deep learning model. Specifically, our DL model is a sequential model developed using
Keras[11] consisting of five dense layers. The first dense layer has 64 units with a ReLU activation
function, taking input data of dimension. The following layers have 128, 256, and 512 units, each also
using the ReLU activation. The last dense layer has a single unit with a sigmoid activation function,
suitable for binary classification tasks. The model is compiled with the ’adam’ optimizer, ’binary
crossentropy’ loss function. Finally, the model is trained on data for 50 epochs with a batch size of 32.
4.2. Results
The result of experimentation are shown in Table 1. This outcomes demonstrate a clear comparison of
various algorithms in terms of Accuracy, Precision, Recall, and F1-Score. Adaboost and SVM both achieve
an accuracy of 85%, with Adaboost showing a high recall of 99% and an F1-Score of 90%, indicating strong
performance in identifying true positives but a slightly lower precision of 82%. Logistic Regression and
XGBoost both achieve an accuracy of 87.5%, with high precision 87% and recall 96%, resulting in an
F1-Score of 92%, highlighting their balanced performance. Decision Trees and Random Forests show
lower accuracy at 82.5% and 80% respectively, k-NN achieves also 82.5% of accuracy. Linear Discriminant
Analysis achieves 85% accuracy, while Naive Bayes has the lowest accuracy at 75%, reflecting its limited
effectiveness in this context. Notably, our proposed ML-DL approach outperforms all other algorithms
with an accuracy of 93%, precision of 90%, recall of 99%, and an F1-Score of 95%, indicating superior
overall performance in terms of both identifying true positives and minimizing false positives. Moreover,
we trained the VGG16 model proposed by Islam et al. [9] on the same datasete used to train our model,
the VGG16 model obtained about 85% in all evaluation metrics. Furthermore, the loss and accuracy
curves show that the loss steadily decreases while the accuracy consistently increases until reaching
93%, indicating good model learning, as shown in Fig. 2.
10
Table 1
Performance of Different Algorithms
Algorithm Accuracy Precision Recall F1-Score
Adaptative boosting 85% 82% 99% 90%
Logistic Regression 87,5% 87% 96% 92%
Decision Trees 82,5% 86% 89% 88%
Random Forests 80% 78% 99% 88%
Support Vector Machines 85% 82% 99% 90%
k-Nearest Neighbor 82,5% 80% 99% 89%
Linear Discriminant Analysis 85% 87% 93% 90%
Naive Bayes 75% 76% 93% 84%
eXtreme Gradient Boosting 87,5% 87% 96% 92%
Islam et al. [9] 85% 85% 85% 85%
Our approach 93% 90% 99% 95%
Figure 2: Loss and accuracy model curves
5. Conclusion
In this paper, we have proposed a new approach based on machine learning followed by deep learning
to efficiently detect the health status of tree leaves, using nine powerful machine learning algorithms,
namely adaboost, logistic regression, decision tree, random forest, support vector machines, k-nearest
neighbors, naive bayes, linear discriminant analysis, and extreme gradient boosting. The results
presented in the experiment demonstrate that the proposed model outperformed the individual machine
learning algorithms on four evaluation measures, achieving accuracy of 93%, precision of 90%, recall of
99%, and an F1 measure of 95%. These results indicate the effectiveness and robustness of the proposed
approach, which can be used as an effective solution for tree disease control. Our future research will
explore the generalization of this approach to other domains and datasets.
Acknowledgments
This work has been sponsored by the General Directorate for Scientific Research and Technological
Development, Ministry of Higher Education and Scientific Research (DGRSDT), Algeria.
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