DIAGNOSING MEDICINAL PLANTS AND THEIR FUNGAL DISEASESUSING DEEP LEARNING MODELS

1. DIAGNOSING MEDICINAL PLANTS AND
THEIR FUNGAL DISEASES
USING DEEP LEARNING MODELS
Guided by Mathinakani M B.E.,M.E.,(Ph.D)
Presented by
Mahalakshmi R
(921722203009)

2. AGENDA - PRESENTATION
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 Introduction
 Abstract
 Objectives
 Literature Survey
 Existing system
 Proposed system
 Overall Design Of Proposed System
 Module Description
 Technologies
 Result And Discussion
 References

3. INTRODUCTION
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 Over 19,000 fungi are known to cause diseases in crop plants
worldwide.
 Fungal spores are readily dispersed by wind, water, soil, insects, and
other invertebrates. In this way, they may infest an entire crop.
 Traditional medicine has been practiced in Ethiopia for a long time
ago. Even today, plants remain the source for the majority of people
in developing countries to alleviate health problems.

4. ABSTRACT
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 Today, with the development of technology, most manual methods are
replaced by automated computer systems for the easiness of human beings.
 Plant identification and disease classification are two major agricultural
research areas, focusing on introducing computerized systems rather than
manual methods.
 Millions of plant species are in the world and play a significant role in
human life. Among all the types of plants, medicinal plants play an essential
role in the traditional medical field because herbal plants can heal humans.

5. OBJECTIVES
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 To achieve this goal, we evaluated the performance of two common pretrained
deep learning models (VGG19 and ResNet50) and compared their accuracy
levels.
 Finally, the system can estimate some performance metrics such as accuracy
and error rate for both algorithms and compare the algorithms based on
accuracy in the form of graph.

6. LITERATURE REVIEW
Title Year Author Methodology
Plant Disease Prediction
using Machine Learning
Algorithms
2022 G. Prem Rishi Kranth, M.
Hema Lalitha, Laharika
Basava and Anjali Mathur
In the above information about
prediction of plant disease different
techniques like decision tree, Naive
Bayes, Neural network and bar plot
are performed.
Leaf disease Detection
and Climatic Parameter
Monitoring of Plants
Using IoT
2021 Dr. G. H. Agrawal, Prof. S. G.
Galande, Shalaka R. Londhe
We have actualized a framework
which indicates usage for Internet of
Things utilized for observing
general climatic parameters
conditions by method for minimal
effort, effectively accessible
detecting framework.
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7. LITERATURE REVIEW
Title Year Author Methodology
The boosting approach
to machine learning:
An overview" in
Nonlinear estimation
and classification.
2022 R.E.Scahpire Boosting is a general method for
improving the accuracy of any
given learning algorithm.
Focusing primarily on the
AdaBoost algorithm.
Support vector
clustering
2020 A. Ben-Hur, D. Horn, H. T.
Siegelmann, V. Vapnik
Data points are mapped by
means of a Gaussian kernel to a
high dimensional feature space,
where we search for the minimal
enclosing sphere..
7

8. LITERATURE REVIEW
Title Year Author Methodology
Land cover change
assessment using
decision trees support
vector machines and
maximum likelihood
classification
algorithms
2022 J. R. Otukei, T. Blaschke Land cover change assessment is
one of the main applications of
remote sensed data. A number of
pixel based classification
algorithms have been developed
over the past years for the analysis
of remotely sensed data.
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9. EXISTING SYSTEM
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 In existing system propose a Long Short Term Memory neural network
algorithm to accomplish the leaf disease classification task.
 Plant disease recognition is an interesting and practical topic. However, this
problem has not been sufficiently explored due to the lack of systematically
investigation and large-scale dataset.
 The most challenging step in constructing such a dataset is providing a
reasonable structure from both the agriculture and image processing
perspective.

10. PROPOSED SYSTEM
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 In proposed system, the input image is taken from dataset repository. In pre-processing, we can resize
the original image and gray scale conversion, means convert RGB to B/W image.
 After that, we can extract the features from pre-processed image such as Local Binary Pattern (LBP)
and Mean Median Variance.
 We can split the images into test image is used for prediction and train image is used for evaluation or
training the model.
 Then, we can implement the different transfer learning algorithms such as Resnet- 50 and VGG -19 for
classifying the input image is affected by or not.
 Finally, the system can estimate some performance metrics such as accuracy and error rate. The
effectiveness of the proposed method was confirmed by comparing accuracy improvement.

11. OVERALL DESIGN OF PROPOSED SYSTEM
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12. MODULE DESCRIPTION
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 Image selection
 Image preprocessing
 Feature extraction
 Image Splitting
 Classification
 Prediction
 Performance Analysis.

13. INPUT IMAGE
 The dataset, Medicinal fungal disease Image disease dataset is
implemented as input. The dataset is taken from dataset repository. The
input dataset is in the format ‘.png, ‘.jpg.
 In this step, we have to read or load the input image by using the imread
() function.
 In our process, we are used the tkinter file dialogue box for selecting the
input image.
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14. PREPROCESSING
 In our process, we have to resize the image and convert the image into gray scale.To
resize an image, you call the resize () method on it, passing in a two-integer tuple
argument representing the width and height of the resized image.
 The function doesn't modify the used image; it instead returns another Image with the
new dimensions.
 Convert an Image to Grayscale in Python Using the Conversion Formula and the
matplotlib Library.
 We can also convert an image to grayscale using the standard RGB to grayscale
conversion formula that is imgGray = 0.2989 * R + 0.5870 * G + 0.1140 * B.
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15. FEATURE EXTRACTION
 In our process, we have to extract the features from pre-processed image.
 Standard deviation is the spread of a group of numbers from the mean.
 The variance measures the average degree to which each point differs from the mean.
 Local Binary Pattern (LBP) is an effective texture descriptor for images which
thresholds the neighboring pixels based on the value of the current pixel .
 LBP descriptors efficiently capture the local spatial patterns and the gray scale
contrast in an image.
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16. IMAGE SPLITTING
 During the machine learning process, data are needed so that learning can take
place.
 In addition to the data required for training, test data are needed to evaluate the
performance of the algorithm in order to see how well it works.
 In our process, we considered 70% of the input dataset to be the training data
and the remaining 30% to be the testing data.
 Data splitting is the act of partitioning available data into two portions, usually
for cross-validator purposes.
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17. CLASSIFICATION
 In our process, we can implement the deep learning algorithm such as VGG-19 and Resnet-
50.
 VGG stands for Visual Geometry Group; it is a standard deep Convolutional Neural Network
(CNN) architecture with multiple layers.
 The “deep” refers to the number of layers with VGG-16 or VGG-19 consisting of 16 and 19
convolutional layers. The VGG architecture is the basis of ground-breaking object
recognition models.
 ResNet-50 is a convolutional neural network that is 50 layers deep. You can load a pretrained
version of the neural network trained on more than a million images from the ImageNet
database
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18. PERFORMANCE METRICS
 The Final Result will get generated based on the overall classification and prediction.
The performance of this proposed approach is evaluated using some measures like,
Accuracy
 Accuracy of classifier refers to the ability of classifier. It predicts the class label
correctly and the accuracy of the predictor refers to how well a given predictor can
guess the value of predicted attribute for a new data.
AC= (TP+TN)/ (TP+TN+FP+FN)
 Then, we can detect or to classify the input image is affected by disease or not.
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19. TECHNOLOGIES
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Software Requirements:
 O/S : Windows 7.
 Language : Python
 Tool : python Kivy
 Front End : Anaconda Navigator – Spyder
Hardware Requirements:
 System : Pentium IV 2.4 GHz
 Hard Disk : 200 GB
 Ram : 4GB

20. RESULT AND DISCUSSION
 We conclude that, the fungal medicinal plant disease dataset was collected
from dataset repository as input. The input dataset was mentioned in our
research paper.
 We are implemented the different classification algorithms (i.e.) deep learning
algorithms.
 Then, deep learning algorithms such as VGG-19 and Resnet-50. Finally, the
result shows that the accuracy and error rate for above mentioned algorithm
and predict the input image is affected or not.
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21. SAMPLE SCREENSHOT(Cont..)
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DISPLAYING SOME OF THE TRAINING IMAGES

22. SAMPLE SCREENSHOT(Cont..)
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DISPLAYING SOME OF THE TRAINING IMAGES

23. SAMPLE SCREENSHOT(Cont..)
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FUNGAL DISEASE IMAGES

24. SAMPLE SCREENSHOT(Cont..)
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FUNGAL DISEASE DETECTION

25. REFERENCES
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[1] M. A. Khan, "Introduction and Importance of Medicinal Plants and Herbs," 20 May 2016. [Online]. [Accessed 16
July 2021].
[2] "Agriculture of Sri Lanka," 25 April 2018. [Online]. [Accessed 18 July 2021].
[3] P. B. Weragoda, "The Traditional System of Medicine in Sri Lanka," Journal of Ethnopharmacology, Vol. 2, No.
1, Pp. 71-73, 1980.
[4] F. Bulathsinghala, "Ayurveda and Indigenous Medical Expertise to Overcome Covid-19," Lake House, 01
November 2020. [Online]. Available: Https://Www.Sundayobserver.Lk/2020/11/01/Impact/Ayurveda-And-
Indigenous-Medical-Expertise-Overcomecovid- 19. [Accessed 16 July 2021].
[5] "Convention on Biological Diversity," [Online]. Available: Https://Www.Cbd.Int/. [Accessed 17 July 2021].

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