=Paper=
{{Paper
|id=Vol-3682/Paper4
|storemode=property
|title=FloraCheck: Pioneering for Leaf Disease Recognition
|pdfUrl=https://ceur-ws.org/Vol-3682/Paper4.pdf
|volume=Vol-3682
|authors=Alongbar Wary,Navya Gupta,Pratha Garg,Anushka Rajoria
|dblpUrl=https://dblp.org/rec/conf/sci2/WaryGGR24
}}
==FloraCheck: Pioneering for Leaf Disease Recognition==
https://ceur-ws.org/Vol-3682/Paper4.pdf
FloraCheck: Pioneering for Leaf Disease Recognition
Alongbar Wary1, *, Navya Gupta1, Pratha Garg1 and Anushka Rajoria1
1 Indira Gandhi Delhi Technical University for Women, Delhi, India, 110006
Abstract
The major source of our economy, which is the agricultural sector, is
seriously threatened by plant diseases. They negatively impact crops
and the means of livelihood for farming communities. The need for
automated solutions becomes evident when we consider how labor-
intensive and error-prone traditional manual methods are for
identifying illnesses. Our study FloraCheck investigates plant disease
identification using the EfficientNetB3 model by using deep learning.
We have chosen the PlantVillage dataset and implemented advanced
image preprocessing techniques for effective model training. Existing
approaches show differences in the data and models they use and
often struggle with limitations such as dataset specificity and a lack
of comprehensive generalization. FloraCheck is bridging these gaps
by harnessing the power of EfficientNetB3 through transfer learning,
ensuring adaptability to a diverse range of plant diseases. The model
is refined through strategic construction involving batch
normalization, regularization, dropout and a final classification layer.
This ensures the development of a robust and adaptive framework
for accurately detecting plant diseases. Our project has achieved an
accuracy rate of 98.93%, signifying a considerable advancement in
the automated detection of plant diseases.
Keywords
Plant disease recognition, CNN, transfer learning, plant village1
1. Introduction
In India, agriculture remains the backbone of the economy, with a substantial percentage
of
Symposium on Computing & Intelligent Systems (SCI), May 10, 2024, New Delhi, INDIA
∗ Corresponding author.
† These authors contributed equally.
alongbarwary@igdtuw.ac.in (A. Wary); navya153btit20@igdtuw.ac.in (N. Gupta);
prathagarg2410@gmail.com (P. Garg); anushka102btit20@igdtuw.ac.in (A. Rajoria)
© 2024 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
the population engaged in farming activities. Especially in rural areas, agriculture is not
merely
an occupation but a way of life. However, the agricultural sector stands as the major pillar
of many economies worldwide, it provides sustenance, employment, and economic stability
to millions of people out there. Unfortunately, plant disease poses a daunting challenge to
this vital sector. These diseases not only threaten crop yields but also jeopardize food
security and the livelihoods of farming communities. The urgency for efficient and
automated solutions to detect and combat these diseases has never been more apparent.
Our research is propelled by the profound impact that plant diseases impose on
agricultural productivity and food security. Traditional manual methods for disease
identification are not only labor-intensive and time-consuming but also prone to errors. The
need for automated systems that can precisely identify and diagnose plant diseases is
evident given the scope and complexity of today's agricultural challenges. In an ever-
changing global landscape, these kinds of systems are essential for maintaining crop health
and maximizing agricultural output.
In response to these pressing challenges, our study sets to achieve several
objectives. First and foremost, our goal is to create a reliable and effective automated system
for the identification and treatment of plant diseases by employing cutting-edge deep
learning techniques. Secondly, we seek to explore the effectiveness of transfer learning and
advanced image preprocessing techniques in enhancing the accuracy and adaptability of the
proposed system. Finally, we will evaluate the developed model's practical suitability for
deployment in agricultural settings by assessing its performance using real-world datasets.
Our study presents several significant contributions to the field of plant disease
detection and agricultural technology:
1. We present an innovative method for automated plant disease identification
that makes use of deep learning, specifically the EfficientNetB3 model.
2. Through the integration of sophisticated image preprocessing techniques and
transfer learning, we enhance the model's ability to precisely identify a wide
range of plant diseases.
3. Through extensive experimentation and evaluation using the PlantVillage
dataset, we demonstrate the efficacy and real-world applicability of the
developed model in diverse agricultural scenarios.
Finally, the structure of our paper is as follows:
In Section 2, we have mentioned a comprehensive overview of relevant research work in
the field of automated plant disease detection. Further in Section 3, focus has been laid on
outlining the methodology implemented and technical approach adopted in our study,
which includes data acquisition, data preprocessing, model construction, model training,
and classification. Section 4, we present the results of our experiments, including
classification model, performance metrics, comparative analysis and the limitations. It also
does the analysis and discussion of the findings of our study, interprets the results, and
highlights implications for agricultural practice. In Section 5, of the paper concludes with an
overview encompassing our contributions, limitations, and prospects for future research
endeavors. Finally, in Section 6, the References provide a concise list of all sources cited
throughout the paper, facilitating further exploration of the topic and validation of the
study's findings.
2. Literature Review
In [1], the key elements of CNN architecture, such as convolutional layers, ReLU activation,
pooling layers, and dropout layers, were employed in the Caffe framework. In [2], CNN
architecture was used with layers such as Convo2D, flatten, max, pooling etc. on the Plant
Village dataset with accuracy of 88%. In [3], the five different architectures are compared
which include VGG16, ResNet50, InceptionV3, InceptionResNet, and DenseNet169,
achieving the best result from ResNet50.
[4] evaluates multiple deep learning models such as GoogleNet, ResNet101,
ResNet50, InceptionV3, AlexNet, InceptionResNetV2, SqueezeNet, VGG16, VGG19.
For object recognition, conventional techniques like LBP, HOG, colour features and GLCM
for are also assessed. Bi-CNN employs pre-trained VGG and ResNet models for feature
extraction followed by ADAM optimizer on Plant Village Dataset [5].
In [6], CNN is achieved through a re-parametrization method and a dynamic pruning
gate to manage computational complexity, optimizing the feature extraction network. In [7],
three classifiers LeafNet, SVM and MLP are evaluated to detect diseases in tea plants.
LeafNet performed best with accuracy of 90.16%. In [8], the recognition of diseases in
tomato leaves is done by S-CNN in which the model is trained using segmented images. In
[9], on tomato, potato and pepper crops in the Plant Village dataset, CNN with image
preprocessing is done and it achieves 98.029% of accuracy.
In [10], CNN incorporated a fully connected layer for classification, convolutional
and pooling layer for feature extraction on approx. 35,000 images of Plant Village dataset.
In [11], max pooling layers come after the convolutional layers, and the final layer includes
an Adam optimizer and softmax activation to lower the loss function. In [12], using CNN
with Raspberry Pi kit to anticipate crop diseases in advance. With a suggested activation
function and two convolutional layers, it achieves 95% of system accuracy. Fertilizer
optimization is aided by K-means clustering-based image segmentation.
In [13], convolution is used to identify patterns and edges, while pooling serves to
reduce the image dimensions. CNN architectures which are applied simple CNN, VGG and
InceptionV3. In [14], a diverse dataset is captured through various sensors. Subsequently,
transfer learning is employed to leverage a pre-trained GoogLeNet CNN, facilitating
detection and classification tasks. The dataset is expanded (XDB) through manual
subdivision of images into smaller regions for optimal results.
In [15], The synthesis of three different CNN models (VGG-16, Google Net, ResNet
50) used with the application of two different classifiers (SVM and KNN). In [16], CNN is
being used where the feature extraction is done by DWT, GLCM giving an accuracy of
98.12%. In [17], three approaches are used, a customized CNN, transfer learning with
INCEPTIONv3, and visual transformers (small and large). Training involves Adam and RM-
Sprop optimization, categorical cross-entropy loss, and callbacks and appropriate learning
rates.
[18] explores ML and DL techniques, like random forest, SVM, and CNN like VGG-16,
VGG-19, and Inception-V3, to accurately detect and classify citrus leaf diseases based on a
manually curated dataset. (Evaluation involves area under the curve (AUC), precision, F1-
score, recall and accuracy, comparing the performance of ML and DL methods, with DL
demonstrating higher overall effectiveness). [19] Utilizing the Plant Village dataset for
supervised learning, applying pixel-based operations, and employing CNNs for image
classification.
In [20], utilization of advanced deep learning meta-architectures including RFCN,
SSD and Faster RCNN, SSD with Inception-v2 and the highest mean average precision
(73.07%) was achieved and optimization with Adam significantly improves accuracy,
particularly for specific disease classes. In [21], while the model is trained, its process has
included 160 images of the papaya leaves. There are numerous machine learning
algorithms, such as KNN, Naive Bayes, Random Forest, Support Vector Machine, CART, and
Logistic Regression, these all have been applied. Out of which, random forest performed
the best with an accuracy of 70.14%.
In [22], detection of plant infections relies on K Means clustering and GLCM
technique. Accuracy achieved was 98.27%. In [23], through the introduction of a rice plant
disease recognition system, the ML algorithms such as KNN, Logistic Regression, Naive
Bayes and Decision Tree are introduced. The Decision Tree algorithm gave best results by
achieving an accuracy of a perfect 97.9167%. This dataset consisted of three different
disease classes wherein each class has 40 images.
In [24], this paper's primary objective was to suggest enhancements to the existing
machine-learning based classification methods which are for plant disease detection,
supported by a comparison of the KNN classifier and SVM classifier. The outcomes
demonstrated that the suggested algorithm has achieved a good accuracy of 98.56%, which
also surpassed the 97.6% accuracy of the old/existing system. In [25], the suggested
approach detects the plant diseases with an average accuracy of 93% by using the Random
Forest Classifier as well as the digital Image processing technique.
In [26], Transfer learning is implemented with five pre-trained deep neural network
architectures: VGG16, DenseNet169, InceptionV3, ResNet50, and Xception. Following
model training, images representing different corn diseases from various datasets are
employed as test data to evaluate the models' generalization capabilities. The DenseNet169
model demonstrated superior performance. The highest generalization accuracy of 81.60%
was achieved when training the DenseNet169 model using (RGBA) images from the CD&S
corn disease dataset, with backgrounds removed. In [27], the study compares 4 deep neural
models such as fasterRCNN, EfficientDET, YoloV5 and YoloV6. Amongst all, YoloV5 model,
which was trained with 93% accuracy on pre-trained hyper parameters, produced the best
result. [28] achieves a detection accuracy of 98.26% by using the EfficientNetV2 model for
cardamom plant disease detection and the U2-Net for background removal.
[29] employs transfer learning with six different CNN architectures, including
VGG16, InceptionV3, Xception, Resnet50, MobileNet, and DenseNet121, for multi-class
classification of plant diseases using 11,333 images from the PlantVillage dataset, with
DenseNet121 achieving the highest accuracy at 95.48%. [30] proposes a rice plant disease
diagnosis method using DenseNet169-MLP, combining DenseNet169 as a feature extractor
and a multilayer perceptron for classification along with fuzzy c-means (FCM) based
segmentation for identifying diseased portions, achieving an accuracy of 97.68%. [31] uses
a hyperparameter-optimized Deep Convolutional Neural Network with data augmentation
to achieve an accuracy of 98.41%.
3. Methodology
Figure 1: Schematic overview of proposed methodology.
3.1. Data Acquisition
We have opted for the PlantVillage dataset, a compilation of images encompassing
diverse plant species and diseases. Originally comprising 38 labelled classes, we refined
the dataset to 25 classes. This curation, focused on specific plant species and diseases,
establishes a controlled framework for the purpose of recognizing and classifying plant
diseases. The resulting dataset, presented in Table 1, optimizes precision by
concentrating on classes crucial to our research.
Table 1. Details of our dataset
Details Count
Number of images 31407
Number of unique plant species 5
Number of distinct plant diseases represented 25
Figure 2: Sample images from Plant Village dataset.
3.2. Data Preprocessing
Before model construction, thorough data pre-processing procedures were conducted to
guarantee the quality and relevance of the dataset.
• Dataset Stratification: A stratified split was implemented to guarantee that classes
were fairly represented in the test, validation, and training sets
• Image Processing and Augmentation: We implemented image augmentation
techniques of horizontal flipping, rotation, zooming, brightness adjustments, and
shifts to enhance robustness of our dataset and a scaling function to normalize pixel
values. To balance efficiency and information preservation, we resize the images to
(224, 224) pixels, aligning with the EfficientNetB3 architecture, which utilizes three
color channels (RGB).
• Batch Size Selection: For both training and testing, we chose a batch of 40. This
action aimed to achieve equilibrium between computational efficiency and model
convergence.
3.3. Model Construction
Once the data was pre-processed, we constructed the model architecture.
• Transfer Learning with EfficientNetB3: We have utilized transfer learning with the
EfficientNetB3 architecture as our base model for the construction of the image
classification model. The model’s pre-training on the ImageNet dataset motivated
this choice, enabling it to capture complex image features. To tailor the base model
to our specific classification task, we added these supplementary layers.
• Incorporating Batch Normalization: For the purpose of stabilizing and accelerating
the training process, we incorporated batch normalization. This enabled us to
normalize the input of each layer preventing internal covariate shift and promoting
more streamlined model learning.
• Dense layer with regularization: A dense layer with Rectifier activation was
introduced to capture complex patterns in the data. In order to reduce overfitting,
L2 weight regularization was applied to the layer to promote resilient learning.
• Dropout for generalization: To enhance model generalization, a dropout layer with
rate of 0.45 was implemented. This layer randomly deactivated neurons during
training, to avoid overdependence of models on specific nodes and improving
overall performance.
• Final Classification Layer: The model’s concluding layer consists of a dense layer
employing softmax activation, providing probabilities for each class in the
classification task. This layer is crucial for generating predictions and determining
the likelihood for each class.
• Model Compilation: The model that has been compiled makes use of the Adamax
optimizer with a step size of 0.001. Choosing categorical cross-entropy as the loss
function was in line with our goal of training for accuracy.
3.4. Model Training
With the model architecture in place, we trained the model using specific parameters
and evaluated its performance.
1. Training parameters:
• Epochs: The model underwent training for a total of five epochs.
• Verbose Setting (Verbosity): The training progress was displayed with
verbosity set to 1, for real time updates on metrics like loss and accuracy.
2. Validation for performance evaluation: To gauge the performance of our model and
confirm that it can be extended to unfamiliar data, a validation dataset was
employed during the training process. After each epoch, the model was evaluated
on this independent dataset, providing insights into its capacity to extrapolate
beyond the provided training dataset.
3.5. Classification
The classification process determines whether a plant leaf from the Plant Village Dataset
is contaminated or not. It further distinguishes the class of plant infection and recognizes
the specific plant variety.
4. Result and Analysis
• Classification Model
We have distributed our dataset into 80% training set, 10% validation set and 10%
testing set. We prepared a classification report that provides a detailed assessment
of the plant disease recognition system’s performance across various diseases
affecting plants.
Table 2. Classification Report
• Performance Metrics
The training process of the model unfolded over five epochs, revealing significant progress
in both training and validation accuracies. Commencing with a notably high accuracy of
89.82% and loss of 4.2516, the model exhibited a remarkable learning curve,
culminating in an accuracy of 99.26% and a substantially reduced loss of 0.2842 by the
final epoch. Such progression signifies the model's adeptness at capturing intricate
patterns within the dataset.
Finally, our plant disease recognition model achieves an accuracy of 98.93% on the
test dataset following the model's performance evaluation.
Table 3. Performance Metrix
Accuracy Loss
Train 0.9937 0.2407
Valid 0.9913 0.2649
Test 0.9893 0.2604
Figure 3: Graph of loss during training and validation across epochs.
Figure 4: Graph of accuracy during training and validation across epochs.
• Comparison Analysis of Performance with Related Studies
We compared our results with several existing research papers to contextualize the
success of our method. Following is the comparison evaluation of performance with
existing research papers.
Our approach, concentrating on 25 carefully selected classes, distinguishes itself
from studies like [14], which employed a more diverse dataset captured through
numerous different sensors widely available. The deliberate emphasis on specificity
enhances the precision of disease classification in our model. The stratified split, image
processing, and augmentation techniques contributed to the robustness of our dataset.
In comparison to [10], which utilized CNN with a fully connected layer for
classification and convolutional and pooling layers for feature extraction, our
preprocessing techniques align with the specific requirements of the EfficientNetB3
architecture, ensuring efficiency and preservation of information. Leveraging transfer
learning with EfficientNetB3, we introduced batch normalization, densely connected
layers with regularization, and dropout for generalization.
Compared to [17], which explored multiple approaches including a customized CNN
and transfer learning, our use of EfficientNetB3 with tailored modifications ensures an
effective balance between complexity and accuracy. Using categorical cross-entropy as
the loss function, five epochs were conducted during the training phase at a learning rate
of 0.001.
In comparison to [6], where a reparameterization method and dynamic
pruning gate were used to manage computational complexity. Our approach
achieves competitive accuracy without resorting to complex computational
optimization techniques.
Our model achieved an outstanding accuracy of 98.93% surpassing the
performance of [20], wherein deep learning meta-architectures with a mean
average precision of 73.07% attained the highest score. The specificity of our model
in detecting diverse plant diseases across various species is reflected in the
precision, recall, and F1-score metrics, as illustrated in our classification report.
So, our research showcases competitive performance when compared to
existing research papers. The specificity and efficiency of our approach position it
as a noteworthy contribution to the field of plant disease detection and
classification.
Figure 5: Comparison of performance with different studies
• Limitations
While our project has made significant strides, it is crucial to acknowledge areas
where can continue to grow and improve.
1. Larger dataset: The project acknowledges that the model's performance is
contingent on the size and diversity of the dataset. For the model to be even
more effective at generalizing to a wider variety of plant diseases, a bigger
dataset might be needed.
2. Additional factors: One aspect worth noting is our model's current focus on
visual cues in images, leaving out contextual information such as soil conditions
or weather patterns. Integrating these factors could provide a more
comprehensive analysis of plant health.
3. Real-time updates: The project does not explicitly address real-time or frequent
updates. Changes in the dataset or emerging diseases may necessitate periodic
model updates for sustained effectiveness. Periodic updates could be necessary
to keep the model relevant and effective.
5. Conclusion
Our plant disease recognition system has accomplished prominent results using the plant
village dataset when evaluated. The model has achieved test accuracy of 98.93% which
shows that the model is highly trained at classifying the different classes of plant diseases
accurately. We have also provided a comprehensive classification report containing
evaluation parameters such as positive prediction value, sensitivity, and F-measure of
different plant disease classes which again declares the efficiency of the model. In this
paperwork, transfer learning has been employed with EfficientNetB3, integrated image
preprocessing techniques, applied batch normalization, used ReLU activation and dropout
layers making sure the model remains efficient and robust in all kinds of situations. In order
to efficiently train and test the model, we have divided the dataset in strategic ways. Also,
image augmentation is incorporated to enhance model’s robustness. The data is fed into the
model by dividing it into batches. These techniques contribute to enhancing the efficacy of
the model and capability to identify the different plant diseases precisely.
6. References
[1] Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, Dubravko Culibrk, Darko
Stefanovic, "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image
Classification," Computational Intelligence and Neuroscience, vol. 2016, Article ID 3289801,
11 pages, 2016. https://doi.org/10.1155/2016/3289801
[2] Srivastava, Prakanshu & Mishra, Kritika & Awasthi, Vibhav & Sahu, Vivek & Pal, Pawan
Kumar. (2021). PLANT DISEASE DETECTION USING CONVOLUTIONAL NEURAL
NETWORK. International Journal of Advanced Research. 09. 691-698.
10.21474/IJAR01/12346.
[3] A. Sagar and D. Jacob, “On using transfer learning for plant disease detection,” BioRxiv,
pp. 1–8, 2021.
[4] TÜRKOĞLU, MUAMMER and HANBAY, DAVUT (2019) "Plant disease and pest detection
using deep learning-based features," Turkish Journal of Electrical Engineering and
Computer Sciences: Vol. 27: No. 3, Article 6. https://doi.org/10.3906/elk-1809-181
[5] D. Srinivasa Rao, R. Babu Ch, V. Sravan Kiran, N. Rajasekhar, K. Srinivas et al., "Plant
disease classification using deep bilinear cnn," Intelligent Automation & Soft Computing,
vol. 31, no.1, pp. 161–176, 2022.
[6] Liu, Y.; Liu, J.; Cheng, W.; Chen, Z.; Zhou, J.; Cheng, H.; Lv, C. A High-Precision Plant Disease
Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.
Plants 2023, 12, 2073. https://doi.org/10.3390/plants12112073
[7] Chen, J.; Liu, Q.; Gao, L. Visual Tea Leaf Disease Recognition Using a Convolutional Neural
Network Model. Symmetry 2019, 11, 343. https://doi.org/10.3390/sym11030343
[8] Sharma, P.; Berwal, Y.P.S.; Ghai, W. Performance analysis of deep learning CNN models
for disease detection in plants using image segmentation. Inf. Process. Agric. 2020, 7, 566–
574.
[9] M. A. Jasim and J. M. AL-Tuwaijari, "Plant Leaf Diseases Detection and Classification
Using Image Processing and Deep Learning Techniques," 2020 International Conference on
Computer Science and Software Engineering (CSASE), Duhok, Iraq, 2020, pp. 259-265, doi:
10.1109/CSASE48920.2020.9142097.
[10] S. V. Militante, B. D. Gerardo and N. V. Dionisio, "Plant Leaf Detection and Disease
Recognition using Deep Learning," 2019 IEEE Eurasia Conference on IOT, Communication
and Engineering (ECICE), Yunlin, Taiwan, 2019, pp. 579-582, doi:
10.1109/ECICE47484.2019.8942686.
[11] G. Shrestha, Deepsikha, M. Das and N. Dey, "Plant Disease Detection Using CNN," 2020
IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, 2020, pp. 109-113,
doi: 10.1109/ASPCON49795.2020.9276722.
[12] S. Y. Yadhav, T. Senthilkumar, S. Jayanthy and J. J. A. Kovilpillai, "Plant Disease Detection
and Classification using CNN Model with Optimized Activation Function," 2020
International Conference on Electronics and Sustainable Communication Systems (ICESC),
Coimbatore, India, 2020, pp. 564-569, doi: 10.1109/ICESC48915.2020.9155815.
[13] Chohan M, Khan A, Katper S, Mahar M (2020) Plant disease detection using deep
learning. Int J Recent Technol Eng 9(1):909–914
[14] Barbedo JG. Plant disease identification from individual lesions and spots using deep
learning. Biosyst Eng. 2019;180:96–107.
[15] Mohameth, F. , Bingcai, C. and Sada, K. (2020) Plant Disease Detection with Deep
Learning and Feature Extraction Using Plant Village. Journal of Computer and
Communications, 8, 10-22. doi: 10.4236/jcc.2020.86002.
[16] S. Ashok, G. Kishore, V. Rajesh, S. Suchitra, S. G. G. Sophia and B. Pavithra, "Tomato Leaf
Disease Detection Using Deep Learning Techniques," 2020 5th International Conference on
Communication and Electronics Systems (ICCES), Coimbatore, India, 2020, pp. 979-983,
doi: 10.1109/ICCES48766.2020.9137986.
[17] E. Hirani, V. Magotra, J. Jain and P. Bide, "Plant Disease Detection Using Deep Learning,"
2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra,
India, 2021, pp. 1-4, doi: 10.1109/I2CT51068.2021.9417910.
[18] R. Sujatha, J. M. Chatterjee, N. Jhanjhi, and S. N. Brohi, “Performance of deep learning vs
machine learning in plant leaf disease detection,” Microprocessors and Microsystems, vol.
80, p. 103615, 2021.
[19] Panchal, A.V.; Patel, S.C.; Bagyalakshmi, K.; Kumar, P.; Khan, I.R.; Soni, M. Image-based
Plant Diseases Detection using Deep Learning. Mater. Today Proc. 2021.
[20] Saleem, M.H.; Khanchi, S.; Potgieter, J.; Arif, K.M. Image-Based Plant Disease
Identification by Deep Learning Meta-Architectures. Plants 2020, 9, 1451.
https://doi.org/10.3390/plants9111451
[21] S. Ramesh et al., "Plant Disease Detection Using Machine Learning," 2018 International
Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C),
Bangalore, India, 2018, pp. 41-45, doi: 10.1109/ICDI3C.2018.00017.
[22] Dr. Sridhathan C"Plant Infection Detection Using Image Processing.“International
Journal Of Modern Engineering Research (IJMER), vol. 08, no. 07, 2018, pp.13-16.
[23] K. Ahmed, T. R. Shahidi, S. M. Irfanul Alam and S. Momen, "Rice Leaf Disease Detection
Using Machine Learning Techniques," 2019 International Conference on Sustainable
Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, 2019, pp. 1-5, doi:
10.1109/STI47673.2019.9068096.
[24] A. S. Tulshan and N. Raul, "Plant Leaf Disease Detection using Machine Learning," 2019
10th International Conference on Computing, Communication and Networking
Technologies (ICCCNT), Kanpur, India, 2019, pp. 1-6, doi:
10.1109/ICCCNT45670.2019.8944556.
[25] Kulkarni, P.; Karwande, A.; Kolhe, T.; Kamble, S.; Joshi, A.; Wyawahare, M. Plant disease
detection using image processing and machine learning. arXiv 2021, arXiv:2106.10698.
[26] A. Ahmad, A. E. Gamal and D. Saraswat, "Toward Generalization of Deep Learning-
Based Plant Disease Identification Under Controlled and Field Conditions," in IEEE Access,
vol. 11, pp. 9042-9057, 2023, doi: 10.1109/ACCESS.2023.3240100
[27] Khalid, M.; Sarfraz, M.S.; Iqbal, U.; Aftab, M.U.; Niedbała, G.; Rauf, H.T. Real-Time Plant
Health Detection Using Deep Convolutional Neural Networks. Agriculture 2023, 13, 510.
https://doi.org/10.3390/agriculture13020510
[28] S. C. K., J. C. D. and N. Patil, "Cardamom Plant Disease Detection Approach Using
EfficientNetV2," in IEEE Access, vol. 10, pp. 789-804, 2022, doi:
10.1109/ACCESS.2021.3138920.
[29] Sumalatha, G.; Rao, S.K.; Singothu, J.R. Transfer Learning-Based Plant Disease
Detection; IJIEMR: Lucknow, India, 2021; Volume 10.
[30] R. P. Narmadha, N. Sengottaiyan and R. J. Kavitha, "Deep transfer learning based rice
plant disease detection model," Intelligent Automation & Soft Computing, vol. 31, no.2, pp.
1257–1271, 2022.
[31] Pandian, J.A.; Kanchanadevi, K.; Kumar, V.D.; Jasińska, E.; Goňo, R.; Leonowicz, Z.;
Jasiński, M. A Five Convolutional Layer Deep Convolutional Neural Network for Plant Leaf
Disease Detection. Electronics 2022, 11, 1266.
https://doi.org/10.3390/electronics11081266