Plant disease detection and classification using machine learning algorithm - It gives you a glance of introduction on why do we have to detect and classify the diseases along with the IEEE papers as a reference to the titled project

1. Academic Project Phase-1 Presentation on
“PLANT DISEASE DETECTION AND CLASSIFICATION USING
MACHINE LEARNING ALGORITHM”
Under the guidance of:
Dr. Vijayashekhar S Sankannanavar
Associate Professor
Department of CS&E
Presented by:
Rumman Hajira (1AY16CS089)
ACHARYA INSTITUTE OF TECHNOLOGY
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
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(Affiliated to Visvesvarya Technological University, Belagavi, Approved by AICTE, New Delhi and Accredited by NBA & NAAC)
Acharya Dr. Sarvepalli Radhakrishnan Road, Achithnagar Post, Soladevanahalli, BENGALURU-560107
Department of CS&E, Acharya Institute of Technology 21-Oct-22
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2. AGENDA
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• Agriculture is the boon to country’s Economy
• Methods of detection
• Machine learning Algorithms
• Classification of various plant diseases
• Suggest Pesticide
Department of CS&E, Acharya Institute of Technology 22-Oct-22
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3. INTRODUCTION TO THE PROJECT

4. INTRODUCTION
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1.PROBLEM DEFINITION:
• Agriculture crops are threatened by wide variety of plant diseases.
• These can damage the crop , lower the vegetable and fruits quality and wipe out the harvest.
• About 42% of the world’s total agricultural crop is destroyed yearly by diseases.

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2. Overview Of Technical Area
• Vision method
• Laboratory methods – polymerase chain reaction ,thermography , etc.
• Machine learning and Deep learning – accuracy and recognition
• Algorithms – Random Forest , K-nearest neighbor
• CNN

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3. Overview of Existing System
• The most wanted crop, Paddy
• Diseases- Brown spot , Paddy Blast , Bacterial blight affected leaf
• Methods – Histograms, k-means clustering, Image Processing
• Drawbacks – Accuracy

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4.Overview Of Proposed System
• Innovation : Convolutional Neural Network
• Benefit : Increase in accuracy rate
• Classification of various plant diseases
• Advancement : Pesticide suggestion

8. LITERATURE SURVEY

9. LITERATURE SURVEY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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S.N
PAPER TITTLE
& PUBLICATION
DETAILS
NAME OF THE
AUTHORS
TECHNICAL
IDEAS /
ALGORITHMS
USED IN THE
PAPER &
ADVANTAGES
SHORTFALLS/DISA
D VANTAGES &
SOLUTION
PROVIDED BYTHE
PROPOSED
SYSTEM
1 F. Marzougui, M. Elleuch and M.
Kherallah, "A Deep CNN
Approach for Plant Disease
Detection," 2020 21st International
Arab Conference on Information
Technology (ACIT), 2020, pp. 1-6,
doi:
10.1109/ACIT50332.2020.930007
2.
1.Fatma Marzougui
2.Mohamed Elleuch
3.Monji kherallah
Uses the ResNet and
data argumentation on
data , ResNet has more
accuracy and less
training time than CNN
Without set background
the accuracy is less

10. LITERATURE SURVEY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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S.N
PAPER TITTLE
& PUBLICATION
DETAILS
NAME OF THE
AUTHORS
TECHNICAL
IDEAS /
ALGORITHMS
USED IN THE
PAPER &
ADVANTAGES
SHORTFALLS/DISA
D VANTAGES &
SOLUTION
PROVIDED BYTHE
PROPOSED
SYSTEM
2 A. KP and J. Anitha, "Plant
disease classification using deep
learning," 2021 3rd International
Conference on Signal Processing
and Communication (ICPSC),
2021, pp. 407-411, doi:
10.1109/ICSPC51351.2021.9451
696.
1.Akshai KP
2. J. Anitha
The literature study
reveals that pre-trained
models using transfer
learning are an efficient
strategy for plant
disease classification
The ResNet model adds
the output of one layer
to the next layer that’s
why the accuracy is less
than the densenet.

11. LITERATURE SURVEY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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S.N
PAPER TITTLE
& PUBLICATION
DETAILS
NAME OF THE
AUTHORS
TECHNICAL
IDEAS /
ALGORITHMS
USED IN THE
PAPER &
ADVANTAGES
SHORTFALLS/DISA
D VANTAGES &
SOLUTION
PROVIDED BYTHE
PROPOSED
SYSTEM
3 S. M. Hassan and A. K. Maji,
"Plant Disease Identification Using
a Novel Convolutional Neural
Network," in IEEE Access, vol. 10,
pp. 5390-5401, 2022, doi:
10.1109/ACCESS.2022.3141371.
1.SK Mahmudul
2. Hassan
3. Arnab Kumar Maji
Novel CNN model
based inception and
residual connection.
Number of parameters
used are reduced.
The imbalance cassava
dataset is used ,so
accuracy is less than
balanced dataset
accuracy.

12. LITERATURE SURVEY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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S.N
PAPER TITTLE
& PUBLICATION
DETAILS
NAME OF THE
AUTHORS
TECHNICAL
IDEAS /
ALGORITHMS
USED IN THE
PAPER &
ADVANTAGES
SHORTFALLS/DISA
D VANTAGES &
SOLUTION
PROVIDED BYTHE
PROPOSED
SYSTEM
4 M. N and K. J. Gowda , "Image
Processing System based
Identification and Classification of
Leaf Disease: A Case Study on
Paddy Leaf," 2020 International
Conference on Electronics and
Sustainable Communication
Systems (ICESC), 2020, pp. 451-
457, doi:
10.1109/ICESC48915.2020.91556
07.
1.Manohar N 2.Karuna
J Gowda
Image processing ,Ostu
,GLCM and KNN
Longer training for the
framework.

13. LITERATURE SURVEY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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S.N
PAPER TITTLE
& PUBLICATION
DETAILS
NAME OF THE
AUTHORS
TECHNICAL
IDEAS /
ALGORITHMS
USED IN THE
PAPER &
ADVANTAGES
SHORTFALLS/DISA
D VANTAGES &
SOLUTION
PROVIDED BYTHE
PROPOSED
SYSTEM
5 P. A. H. Vardhini, S. Asritha and Y.
S. Devi, "Efficient Disease
Detection of Paddy Crop using
CNN," 2020 International
Conference on Smart Technologies
in Computing, Electrical and
Electronics (ICSTCEE), 2020, pp.
116-119, doi:
10.1109/ICSTCEE49637.2020.927
6775.
1. P. A Harsha 2.
Vardini 3. S. Asritha 4.
Y. Sumitha Devi
Disease prediction ,
raspberry pi, CNN
,Artificial Intelligence .
Cost and User friendly.
Applicable on Paddy
crop plant.

14. REQUIREMENTS SPECIFICATION

15. FUNCTIONAL REQUIREMENTS
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• The Functional requirements define the internal workings of the
software
• The technical details, data manipulation and processing and other
specific functionality that show how the use cases are to be satisfied.
• They are supported by non-functional requirements, which impose
constraints on the design or implementation.
Department of CS&E, Acharya Institute of Technology 22-Oct-22

16. NON-FUNCTIONAL REQUIREMENTS
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• Dependability
• Availability
• Reliability
• Safety
• Security
Department of CS&E, Acharya Institute of Technology 22-Oct-22

17. SOFTWARE REQUIREMENTS
• Operating System : Windows 10
• IDE : python 3.7, MATLab , TensorFlow
• Language : Python
As of now, we are vigorously focusing on only software algorithms. We are using
python tool for developing algorithms. In future, we will be using TensorFlow and Python IDE
for transforming this algorithm into a complete product.
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Department of CS&E, Acharya Institute of Technology 22-Oct-22

18. HARDWARE REQUIREMENTS
• Processor : Intel i7
• Hard Disk :120 GB
• RAM : 16 GB
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Department of CS&E, Acharya Institute of Technology 22-Oct-22

19. PROPOSED METHODOLOGY

20. PROPOSED METHODOLOGY
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The main steps in the proposed work is summarized below:
• This work proposes a training image generation technology based on
image processing techniques, which can enhance the robustness and prevent
overfitting of the CNN-based model in the training process.
• A convolutional neural network is first employed to diagnose leaf
diseases; the end-to -end learning model can automatically discover the
discriminative features of the leaf images and identify the common types of leaf
diseases with high accuracy.

21. PROPOSED METHODOLOGY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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• Convolutional Neural Network (CNN)
A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning
algorithm which can take in an input image, assign importance (learnable weights and
biases) to various aspects/objects in the image and be able to differentiate one from
the other.

22. PROPOSED METHODOLOGY
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Department of CS&E, Acharya Institute of Technology
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• By analyzing the characteristics of leaf diseases, a novel deep convolutional
neural network model based on ResNet shall be proposed; the convolution
kernel size is adjusted, fully-connected layers are replaced by a convolutional
layer, and GoogLeNet’s Inception is applied to improve the feature extraction
ability.
• Residual network (ResNet) is a CNN architecture whose core building element is
a residual block.

23. PROPOSED METHODOLOGY
22-Oct-22
Department of CS&E, Acharya Institute of Technology
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• The strength of ResNet34 to solve the degradation problem to give higher
accuracies and the advantages of this pre-trained model is the motivation of
using it as the classification technique in our proposed work.
• Proposed work is planning to use a dataset of various images of many crops
diseased leaves as shown below.
Fig. 1 Image samples of leaf disease

24. CONCLUSION AND FUTURE ENHANCEMENT

25. CONCLUSION
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⚫This project shall propose a novel deep convolutional neural network model to
accurately identify and classify leaf diseases, which can automatically discover
the discriminative features of leaf diseases and enable an end-to-end learning
pipeline with high accuracy.
⚫ A novel structure of a deep convolutional neural network based on the ResNet
model shall be designed by removing partial full connected layers, adding
pooling layers, introducing the GoogLeNet Inception structure into the proposed
network model.
Department of CS&E, Acharya Institute of Technology 22-Oct-22

26. FUTURE ENHANCEMENT
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⚫The future work can also be dedicated to the automatic estimation of the severity
of these diseases.
⚫Usage of real-time images to identify the diseases which would increase time efficiency
of the project and it can be carried out to identify diseases on other crops such as wheat,
sugarcaneand others.
⚫The instant solutions can be made available to the farmers by designing mobile
based applications.
⚫Online solutions related to plant diseases can be provided by using web portals
Department of CS&E, Acharya Institute of Technology 22-Oct-22

27. 1 F. Marzougui, M. Elleuch and M. Kherallah, "A Deep CNN Approach for Plant Disease Detection," 2020 21st
International Arab Conference on Information Technology (ACIT), 2020, pp. 1-6, doi:
10.1109/ACIT50332.2020.9300072.
2 A. KP and J. Anitha, "Plant disease classification using deep learning," 2021 3rd International Conference on Signal
Processing and Communication (ICPSC), 2021, pp. 407-411, doi: 10.1109/ICSPC51351.2021.9451696.
3 S. M. Hassan and A. K. Maji, "Plant Disease Identification Using a Novel Convolutional Neural Network," in IEEE
Access, vol. 10, pp. 5390-5401, 2022, doi: 10.1109/ACCESS.2022.3141371.
4 M. N and K. J. Gowda, "Image Processing System based Identification and Classification of Leaf Disease: A Case Study
on Paddy Leaf," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC),
2020, pp. 451-457, doi: 10.1109/ICESC48915.2020.9155607.
5 P. A. H. Vardhini, S. Asritha and Y. S. Devi, "Efficient Disease Detection of Paddy Crop using CNN," 2020 International
Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 2020, pp. 116-119, doi:
10.1109/ICSTCEE49637.2020.9276775.
REFERENCES
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Department of CS&E, Acharya Institute of Technology 22-Oct-22

28. THANK YOU
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Department of CS&E, Acharya Institute of Technology 22-Oct-22