presentation

1. An Improved Convolutional Neural
Network Model for Detection of
Arecanut Disease and Remedies
Under the Guidance of:
Project Team

2. AGENDA
• Abstract
• Introduction
• Literature Survey
• Aim and Objectives
• Existing System
• Proposed System
• System requirement Specifications
• References

3. Need Solution

4. ABSTRACT
• Arecanut, commonly referred to as betel nut, is a tropical crop.
• India holds the second position globally both in its production and consumption.
• Throughout its lifecycle, the crop is susceptible to various ailments, spanning from root to fruit. Presently,
disease detection primarily relies on visual inspection, requiring farmers to meticulously examine each
crop at regular intervals.
• In this study, we have introduced a system utilizing Convolutional Neural Networks to identify diseases
affecting the leaves, trunk, and overall health of the arecanut plant.
• Additionally, the system offers recommended remedies for these identified diseases.
• We developed an improved CNN model to distinguish between healthy and diseased arecanut plants.
• To facilitate training and testing, we curated a dataset comprising more than 700 images of both healthy and diseased
arecanut plants, split into an 80:20 ratio for training and testing, respectively.

5. INTRODUCTION
• The arecanut palm is the source of common chewing nut, popularly known
as betel nut or Supari. In India it is extensively used by large sections of
people and is very much linked with religious practices.
• India is the largest producer of arecanut and at the same time largest
consumer also. Major states cultivating this crop are Karnataka (40%),
Kerala (25%), Assam (20%), Tamil Nadu, Meghalaya and West Bengal.

6. 'Blast Disease' caused by an air-borne fungus can wipe out 70 percent of
arecanut plantations in Karnataka. Published: 24 Nov 2022

8. LITERATURE SURVEY
SL
NO
AUTHOR Year Title METHODOLOGY DRAWBACKS
1 Dhanuja K
C1 , Mohan
Kumar H P
August
2020
Areca Nut Disease
Detection using
Image Processing
Technology
Applications of Wavelet, Gabor,
Local binary (LBP), Gray Level
Difference Matrix (GLDM) and
Gray Level Co-Occurrence
Matrix (GLCM) are used to
extract various texture features
from areca nut.
The automatic configuration of the
system is made possible in the
future for the classification of
areca nuts.
2
Rajashree
Krishna,1,*
Prema K
V2 and
Rajat
Gaonkar3
2022 Areca Nut Disease
Dataset Creation
and Validation
using Machine
Learning
Techniques based
on Weather
Parameters
Historical weather data i.e.
temperature, rainfall, relative
humidity, sunshine, wind
direction, and wind speed are
collected from the Udupi
weather station.
Concentrated only on the Dataset
creation , No model
implementation is done

9. SL
NO
AUTHOR Year Title METHODOLOGY Drawbacks
3 Manpreet
Sandhu,
Pratik
Hadawale
2020 Plant Disease Detection using
ML and UAV
In this paper the
author has developed
an automated system
for detecting disease
using leaf image
classification.
Cost is more for implementation.
4 Ashish
Nage, V.R.
Raut
2019 Detection and Identification of
Plant Leaf Diseases based on
Python
the author developed
an Android application
that helps farmers in
identifying plant
disease by uploading a
leaf image to the
system which uses a
convolution neural
network algorithm to
identify the disease in
the leaf.
person having less knowledge
cannot use the mobile application
effectively.
LITERATURE SURVEY

10. SL NO Author Year Title METHODOLOGY Drawbacks
5 Anandhakris
hnan MG,
Joel Hanson,
Annette Joy
March
2017 Plant Leaf Disease
Detection using Deep
Learning and
Convolutional Neural
Network
In this paper to get better
feature extraction, image
reprocessing was done,
which includes noise
removal, intensity
normalization, removing
reflections, and masking
portions of the image.
Feature extraction can be
done only for few features .
6
Manisha
Bhange,
H.A.
Hingoliwala
2015
Smart Farming:
Pomegranate Disease
Detection Using Image
Processing
In this paper detect
pomegranate diseases and
also suggest the solution on
diseases.
Image Augmentation takes
longer time than
classification
7 Meghana D
R and
Prabhudeva
S
June
2022
Image Processing
based Arecanut
Diseases Detection
Using CNN Model
In this paper the authors
have used different
algorithms (SVM, KNN,
Decision tree, CNN) for
detection of diseases in
leaves.
If we increase the epochs ,
Loss will increase to 10%.
LITERATURE SURVEY

11. PROBLEM STATEMENT
The aim of the project is to detect the disease in arecanut fruit, Stem and leaves using improved
Convolutional Neural network and Image processing techniques and finding the remedies to the
diseases.
• Objectives
 To collect datasets that contain healthy and diseased images of arecanut and their leaves.
 Design and develop an algorithm for early detection of disease in arecanut that can avoid the
spreading of diseases.
 Develop a deep learning based algorithm that would suggest solutions for the detected
diseases.
 Developing a web application for early detection of disease, prediction and providing
remedies to the above solution.

12. APPLICATIONS
 Arecanut disease detection has numerous applications across agriculture, environmental monitoring, and
research.
 Early Disease Detection: Rapid identification of diseases allows for early intervention, preventing
widespread damage to crops. This early detection can help in the timely application of treatments or
management strategies.
 Early Disease Detection: Rapid identification of diseases allows for early intervention, preventing
widespread damage to crops. This early detection can help in the timely application of treatments or
management strategies.
 Quarantine and Disease Control: Plant disease detection is crucial in quarantine procedures, preventing
the spread of diseases to new areas or regions.

13. ADVANTAGES
 Early Detection: Identifying diseases in their early stages allows for prompt action.
 Preventative Measures: Early detection enables farmers to implement timely using
specific pesticides.
 Reduced Crop Loss: By promptly diagnosing diseases, farmers can take appropriate
steps.
 Optimized Resource Use: Disease detection helps optimize resource utilization
minimizing Optimized Resource Use: Disease detection helps optimize resource
utilization.

14. EXISTING SYSTEM
• Visual Inspection: The traditional method involves manual examination by agricultural experts or
farmers to identify visual symptoms of diseases, such as leaf discoloration, lesions, spots, or unusual
patterns.
• Laboratory Techniques: These methods involve taking samples from plants showing symptoms,
culturing pathogens in a lab, and using techniques like microscopy, PCR (Polymerase Chain Reaction),
ELISA (Enzyme-Linked Immunosorbent Assay), or DNA sequencing to identify specific pathogens
causing diseases.
• Image Processing: Utilizing cameras and image processing algorithms to capture images of plants and
then employing computer vision techniques, machine learning, or deep learning to analyze these images
for disease symptoms. This method allows for automated and rapid analysis of large-scale crops.
• Machine Learning: Creating machine learning platforms that integrate various data sources (images,
environmental factors, and historical data) to diagnose plant diseases.

15. PROPOSED SYSTEM
Web Application

16. SYSTEM REQUIREMENT SPECIFICATIONS
• Hardware Requirements
 Processor: Intel CORE i3
 RAM: 4 GB
 Disk space: minimum 256 GB
• Software Requirements
 Operating System (Windows, MacOS).
 Python, HTML, CSS, JavaScript.
 Java / Python Framework and SQL database
 An Internet Browser (Google Chrome, Microsoft Edge etc).
 Code Editor (Visual Studio code/PyCharm).
 The package manager PIP (pip is a python package-management system written in Python
used to install and manage software packages).

17. Architecture Diagram
Input Image
Dataset
Pre-Processing
Training
Data
Deep Learning Model
(Improved CNN)
Feature Selection
and Segmentation
Test
Data
Model Evaluation
Splitting Dataset
Recognition
Recognition of
activity using web
Application
Healthy Unhealthy

18. DATA FLOW DIAGRAM –L0
Deep
Learning
Model
Data Pre-processing
Feature Extraction
Input
ArecaNut
Dataset
Train Data Test Data
Recognition
of Arecanut
Disease

19. SYSTEM DESIGN – Dataflow Diagram-LEVEL1
USER
CAPTURE
IMAGE
Arecanut
Disease
DETECTION
DATASET
SYSTEM
RECOGNITION

20. COLLECT IMAGES
of Arecanut Fruit,
Stem and Leaves
PREPROCESSED
&EXTRACT
FEATURES
DESIGN &
CONSTRUCT
MODEL
TEST THE
MODEL IN RAW
IMAGE
TRAIN MODEL
USER ADMIN
Use Case Diagram
Disease
Detection

21. REFERENCES
1. Dhanuja K C. Areca Nut Disease Detection using Image Processing Technology. International Journal of Engineering Research
2020 V9. 10.17577/IJERTV9IS080352.
2. Mallaiah, Suresha & Danti, Ajit & Narasimhamurthy, S. Classification of Diseased Arecanut based on Texture Features.
International Journal of Computer Applications. 2014.
3. Manpreet Sandhu, Pratik Hadawale, Saumaan Momin, Prof. Ajitkumar Khachane. Plant Disease Detection using ML and UAV.
International Research Journal of Engineering and Technology 2020 V7.
4. Mr. Ashish Nage, Prof. V. R. Raut, Detection and Identification of Plant Leaf Diseases based on Python, INTERNATIONAL
JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT),2019, Volume 08, Issue 05.
5. Anandhakrishnan MG Joel Hanson, Annette Joy, Jeri Francis, Plant Leaf Disease Detection using Deep Learning and
Convolutional Neural Network, International Journal of Engineering Science and Computing, Volume 7, Issue No.3, March
2017
6. Manisha Bhange, H.A. Hingoliwala, Smart Farming: Pomegranate Disease Detection Using Image Processing, Procedia
Computer Science, Volume 58,2015, Pages 280-288, ISSN 1877-0509.

22. REFERENCES
7. Swathy Ann Sam, Siya Elizebeth Varghese, Pooja Murali, Sonu Joseph John, Dr. Anju
Pratap. Time saving malady expert system in plant leaf using CNN, 2020, Volume 13,
Issue No 3.
8.Detection and classification of areca nuts with machine vision Kuo-Yi Huang 2012
9.Classification of Diseased Areca nut based on Texture Features International Journal of
Computer Applications (0975 – 8887) Recent Advances in Information Technology,
2014
10.Segmentation and Classification of Raw Arecanuts Based on Three Sigma Control
Limits December 2012.
11.C.L. Hsieh, S.F. Cheng, T.T. Lin, Application of image texture analysis and neural
network on the growth stage recognition for head cabbage seedlings, Journal of
Agricultural Machinery 6 (2) (1997) 1–13 (in Chinese).

23. THANK YOU