FINAL_ ARECANUT_CNN_PPT PHASE 2_of.pptx

Arecanut disease detection

1. “ Detection of Arecanut Disease Using CNN ” Under the Guidance of : Miss.AFREEN BANU M.B Project Coordinator: Asst.prof.Mrs.RATNA Professor & HOD: Prof. NITYANANDA D M Presentation on Phase-2 Presented by AFSANABANU KALAS 2GO20CS001 GOURAMMA C 2GO20CS011 PRAMODA S 2GO20CS030 SANA MUDGAL 2GO21CS414

2. INTRODUCTION AIM & OBJECTIVES LITERATURE SURVEY EXISTING SYSTEM PROPOSED SYSTEM SYSTEM ANALYSIS SYSTEM IMPLEMENTATION ADVANTAGES APPLICATIONS SYSTEM REQUIREMENT SPECIFICATION CONCLUSION REFERENCES AGENDA

3. Areca nut is an economically important crop in many tropical regions .However , it is susceptible to various disease that significantly reduce the yield and quality of nuts. Problem statement

4. 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. INTRODUCTION

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

6. Aim The aim of the project is to detect the disease in arecanut fruit, Stem and leaves using Deep Learning and Image processing techniques and finding 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 like CNN, Rest Net, Efficient Net, 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. AIM AND OBJECTIVES

7. LITERATURE SURVEY

8. LITERATURE SURVEY

9. 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, ELISA, 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 to diagnose plant diseases.These systems continuously learn and improve their accuracy over time. EXISTING SYSTEM

10. PROPOSED SYSTEM

11. Architecture Diagram

12. SYSTEM DESIGN – Dataflow Diagram USER CAPTURE IMAGE ARECANUT DISEASE DETECTION DATASET SYSTEM RECOGNITION

13. Arecanut disease ( healthy and Unhealthy) Data Collection Pre-processing & Feature Extractions Training and Testing the Model Deploying Deep learning CNN Model Prediction of 9 types of healthy and unhealthy Diseases Web Interface to Predict the Disease class. MODULES

14. SYSTEM IMPLEMENTATION Importing the Package Download the Dataset

15. Visualize the Data

16. Arecanut disease Detection landing page SNAPSHOTS

17. 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. ADVANTAGES

18. APPLICATIONS Areca nut 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. Quarantine and Disease Control: Plant disease detection is crucial in quarantine procedures, preventing the spread of diseases to new areas or regions.

19. Hardware Requirements Processor: Ryzen 5 RAM: 8 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). SYSTEM REQUIREMENT SPECIFICATIONS

20. This project focuses on the detection of diseases in Arecanut, leaves, and trunk using Deep Neural Networks. Experimentation is conducted using diseased and healthy arecanut image dataset of 620 images. The input image is first pre-processed, followed by feature extraction, training, and classification. The proposed System detects diseases of arecanut such as Mahali, Stem bleeding, and yellow leaf spot and provides remedies for the same. Depending on the quality of the input image and the stage of the disease, the experimental results show varying levels of disease detection accuracy. In this project we used 4 deep learning models such as CNN, RestNet, EffecientNet and VGG16. Among these algorithm CNN gives 98.76% and Efficient Net gives 98.70% Accuracy. As a result, this system takes a step toward encouraging farmers to practice smart farming and allowing them to make better yield decisions by enabling them to take all the necessary preventive and corrective action on their arecanut crop with accuracy 98.76%. So, CNN model is used in final deployment of Web application for finding prediction. CONCLUSIONS

21. Dhanuja K C. Areca Nut Disease Detection using Image Processing Technology. International Journal of Engineering Research 2020 V9. 10.17577/IJERTV9IS080352. Mallaiah, Suresha & Danti, Ajit & Narasimhamurthy, S. Classification of Diseased Arecanut based on Texture Features. International Journal of Computer Applications. 2014. 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. 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. 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 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. 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. Detection and classification of areca nuts with machine vision Kuo-Yi Huang 2012 Classification of Diseased Areca nut based on Texture Features International Journal of Computer Applications (0975 – 8887) Recent Advances in Information Technology, 2014 Segmentation and Classification of Raw Arecanuts Based on Three Sigma Control Limits December 2012. REFERENCES

22. THANK YOU