Plants play a crucial role in preserving life and maintaining biodiversity on earth by facilitating air and water for living beings. Medicinal plants, one of the important classes of plants, serve as medicine for many diseases. The knowledge about medicinal plants carried by generations must be preserved and protected. Computer vision, pattern recognition, and image processing technologies provide promising results for the identification and classification of medicinal plants. Identifying a medicinal plant with required medicinal values is one of the major challenging tasks. In this system, we are developing an application that will help doctors to Identify the plants This is a desktop application. it is user-friendly/accessible and works fine just you need to upload the plant's image and click on predict it will give all information related to that plant and also give what is used of that plant's leaves. In this application, we are using 15000 images of different plants and there are 50 categories of plants in our custom dataset to train our model by using deep learning models. Our application also provides plant information in Sanskrit. To train our model we used Google Colab.

1. G.H.RAISONI COLLEGE OF ENGINEERING AND
MANAGEMENT,
CHAS-AHMEDNAGAR
Ayur-Vriksha
A Deep Learning Approach for Classification of Medicinal Plants
PRESENTED BY :
Pratiksha Zende.
Soham kadam.
Premkumar Varma.
GUIDED BY :
Prof Savita Adhav

2. CONTENTS:
1. INTRODUCTION
2. PROBLEM STATEMENT
3. LITERATURE SURVEY
4. MOTIVATION
5. GOALS AND OBJECTIVES
6. PROPOSED SYSTEM
7. SYSTEM ARCHITECTURE
8. SYSTEM REQUIREMENTS
9. DATA FLOW DAIGRAM 0
10. DATA FLOW DIAGRAM 1
11. USE CASE DIAGRAM
12. CLASS DIAGRAM
13. ADVANTAGES / DISADVANTAGES
14. CONCLUSION
15. FUTURE SCOPE
16. REFRENCE

3. INTRODUCTION:
• Plants play a crucial role in preserving life and maintaining biodiversity on earth
by facilitating air and water for living beings.
• Medicinal plants, one of the important class of plants, serve as medicine for
many diseases. The knowledge about medicinal plants carried by generations
must be preserved and protected.
• Computer vision, pattern recognition, and image processing technologies
provide promising results for identification and classification of medicinal plants.
• Identifying a medicinal plant with required medicinal values is one of the major
challenging tasks.

4. PROBLEM STATEMENT:
• Now a days many people use Modern Scientific Medicine for diagnosing their
Diseases.
• So side effects of using these medicines is very high, these patient can’t cure
their disease in depth.
• For small disease, infection, allergy many people majorly prefer modern
medicines.

5. LITERATURE SURVEY:
SR
NO
PAPER TITLE PUBLICATIO
N YEAR
ADVANTAGE DISADVANTAGE
1. AyurLeaf: A Deep
Learning Approach for
Classification of
Medicinal Plants
IEEE 2018 Good Dataset with
96.76% Accuracy
Fails to provide Interface and used
for only 40 categories.
2. Medicinal plants, also
called medicinal
herbs, have been
discovered and used
in traditional
medicine practices
since prehistoric times
IEEE 2016 Accuracy of this
model is 94.4%
PNN require more memory space to
store the model
3. Classification of
selected medicinal
plants leaf using image
processing
IEEE 2012 The efficiency of the
implementation of
the proposed
algorithms is found
to be 92%.
The system is found by testing in on
10 different plant species.
.

6. MOTIVATION:
• The motive of Ayurveda is complete health for all.
• Ayurveda first believes in maintenance of health. By making this application we
contribute for Ayurveda to maintain health. Identification of medicinal plants is
fundamental for their effective treatment.
• Ayurveda lets the healthy person be more healthy and helps one to be devoid of
any diseases. So, first protect one’s healthy state and if got with some diseases,
treat it.
• Ayurveda believes in the removal of the cause and avoidance of causative factors.
It is to remove the root cause and give permanent relief.

7. GOALS AND OBJECTIVE:
Goals : -
• To create the user friendly application .
• To detect and provide information of the ayurvedic plants.
Objective : -
• To provide the application which detect the medicinal plants
and provide the information.

8. PROPOSED SYSTEM:
• In this system we are developing an application which will help doctors to Identify
the plants
• This is desktop application.
• it is user-friendly/accessible and work fine just you need to upload the plants
image and click on predict it will give all information related to that plant and also
give what is use of that plant leaves.
• In this application we are using 15000 images of different plants and there are 50
categories of plants in our custom dataset and train our model by using deep
learning models.
• Our application also provide plant information in Sanskrit.
• To train our model we used Google Colab.

9. DL Model
Img 300
Img N
Img 300
Img 300
Split 20 % Test
and
80 % Train
Save Model with
.H5 extension
Categories
like Tulsi
,Neem etc.
Deploy
Model with
Flask
All Image
operations
Prediction etc.
done here
By checking the
Accuracy we
select particular
Model
1.Flatten the input image dimensions to 1D (width pixels
x height pixels)
2.Normalize the image pixel values (divide by 255)
3.One-Hot Encode the categorical column
4.Build a model architecture (Sequential) with Dense
layers
5.Train the model and make predictions
SYSTEM ARHITECHTURE:

10. SYSTEM REQUIRNMENTS:
Hardware Requirements
– GPU : 4GB Nvidia Graphics Card
– HARD DISK : 40 GB
– MONITOR : 15 VGA colour
– CPU : i5 and above Processor
– RAM : 4GB +
Software Requirements
– Operating system : Windows 7 above
– Python Version : 3.8 above with including Deep Learning libraries
– Coding Language : Python , Node JS.
– Other Software : Anaconda Navigator , Google-Colab

11. Data Flow Diagram LEVEL 0:
DL Model
INPUT
IMAGE
Save Model with .H5
extension
PREDICTIONS
All Image operations
Prediction etc. done here
By checking the Accuracy we
select particular Model

12. Data Flow Diagram LEVEL 1:
INPUT
IMAGES
(DATAS
ET)
Arrange
Dataset
in folders
20% test
80% train
DL Model
Deploy Model
with Flask
Sanskrit
Words

13. USE-CASE DIAGRAM
MODEL
PREDICTION
INTERFACE
RESULT
https://ayur-vriksha-data-flow.netlify.app/

14. CLASS DIAGRAM

15. ADVANTAGES AND DISADVANTAGES:
ADVANTAGES:
1. It helps for Ayurvedic doctors for identifying various plants
2. User friendly interface.
3. Simple and easy to use.
DISADVANTAGES:
1. Not 100% accurate.
2. Sometimes provide inaccurate information.

16. APPLICATIONS:
• Our app is based on Image Detection with the help of that we can specify what is plant name and
what purpose they used for because all medical plants have different properties.
• There are 2500+ Ayurvedic plants so it is difficult to identify which plant is used for what purpose so
remembering all plants is quite difficult so our main aim is that to make our app user friendly for all
and they can be easily identify plants.
• The Ayurvedic practitioners also called as vaidyas possesses a thorough Ayurvedic knowledge and
their treatment involves the complete well being of a person physically, mentally and spiritually. With
the help of this app they can easily find and say which plant this is and how to use for this to cure
patients.

17. CONCLUSION:
• Main aim is that all can identify which plant is this and what its use.
• It helps for Ayurvedic doctors for identifying various plants
• Helps all by providing user friendly interface by uploading sample pic of plants.
• We provide Sanskrit names to our project because Ayurveda has invented in my
country India.

18. FUTURE SCOPE:
• In the future, we will be able to upgrade the AyurVriksh model to make it even more suitable for
classifying leaf images in which a single image contains leaves from more than one plant species
and leaves from a single plant species in different orientations.
• In future we will add more than 50 categories so we can classify all plants.
• In future we also have higher GPU so accuracy will be much better and also in we will apply latest
classification deep learning model which will recognize all plants very efficiently and correctly.

19. REFERANCES:
[1] J. W. Tan, S. Chang, S. Binti Abdul Kareem, H. J. Yap, and K. Yong. Deep learning for plant
species classification using leaf vein morphometric. pages 1–1, 2018.
[2] A. Gopal, S. Prudhveeswar Reddy, and V. Gayatri. Classification of selected medicinal plants
leaf using image processing. In 2012 International Conference on Machine Vision and Image
Processing (MVIP), pages 5–8, Dec 2012.
[3] R. Janani and A. Gopal. Identification of selected medicinal plant leaves using image features
and ann. In 2013 International Conference on Advanced Electronic Systems (ICAES), pages 238–
242, Sept 2013.
[4] D. Venkataraman and N. Mangayarkarasi. Computer vision based feature extraction of leaves
for identification of medicinal values of plants. In 2016 IEEE International Conference on
Computational Intelligence and Computing Research (ICCIC), pages 1–5, Dec 2016.
[5] S. Prasad and P. P. Singh. Medicinal plant leaf information extraction using deep features. In
TENCON 2017 - 2017 IEEE Region 10 Conference, pages 2722–2726, Nov 2017.

20. THANK YOU!
🙏