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adike mitra10

areca nut disease detection and classification using convolutional neural network

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adike mitra10

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  1. Srishyla Educational Trust(S) Bheemasamudra®GM INSTITUTE OF TECHNOLOGY, DAVANGERE Approved by AICTE | Affiliated to VTU Belagavi | Recognized by Govt. of Karnataka Department of Information Science & Engineering Synopsis Presentation on “ADIKE MITRA” Project Coordinator: Dr.Neelambike S B.E.,M.Tech.,Ph.D.,M.I.S.T.E Project Guide: Dr. Sunil Kumar B S B.E.,M.Tech.,Ph.D.,M.I.S.T.E Head of Department: Dr. Sunil Kumar B S B.E.,M.Tech.,Ph.D.,M.I.S.T.E Project Associates: Ananya Patel G P 4GM20IS004 Channabasavanana S Pawate 4GM20IS023 Radhika S B 4GM20IS035 Shreyas M Vali 4GM20IS049

  2. VISION: “To evolve as an Excellent Technological Education Center of Information Science to create competitive & responsible engineering professionals for the advancement of society.” MISSION: 1) To provide application specific training for developing quality engineers in Information Science. To develop creativity in students to become competent in the field of Information Science. To inculcate social and ethical values in students to perform better in diverse environment.

  3. PROGRAM EDUCATIONAL OBJECTIVES: To develop graduates who are proficient to solve wide range of computing related problems. To prepare graduates who have the necessary skills required for Higher Education and Entrepreneurship. To prepare graduates who have the ability to engage in Lifelong Learning. PROGRAM SPECIFIC OUTCOMES: Capable to design, develop & test the IT Solutions in real time. Competent to apply knowledge to manage & monitor IT resources.

  4. INTRODUCTION In developing countries like India, the economy mainly depends on agriculture. Farmers in India grow a diverse range of crops in that one of them is areca nut.

  5. Growth of Areca nut in world Growth of Areca nut in Karnataka

  6. Existing Technology Existing technology for detecting diseases in areca nut plants often relies on visual inspection by human experts. This approach is problematic for several reasons. 1.Firstly, it is labor-intensive and time-consuming, requiring skilled personnel. 2.Secondly, it may not detect diseases at an early stage when they are most treatable, leading to delayed interventions. 3.Lastly, as the disease spreads, it becomes challenging to monitor large plantations effectively, making it potential economic losses.

  7. PROBLEM STATEMENT To develop an efficient and automated system for the early detection of diseases in areca nut plants using machine learning, particularly Convolutional Neural Networks (CNN).

  8. OBJECTIVES 1.To collect datasets that contain healthy and diseased images of arecanut and their leaves. 2. Design and develop an algorithm for early detection of disease in arecanut that can avoid the spreading of diseases. 3. To detect the diseases with higher accuracy.

  9. SCOPE OF THE PROJECT • Method involves developing a comprehensive system for disease detection in areca nut plants. • This includes collecting and curating a diverse dataset of areca nut images, training a CNN model to accurately identify various diseases and implementing the system for real-time monitoring in areca nut plantations. • The project aims to provide a user-friendly system that can assist farmers in early disease detection.

  10. METHODOLOGY

  11. Mahali (Koleroga) Stem Bleeding Yellow leaf spot

  12. 1. Min. Intel Core i3 2. 4GB Ram. 3. 10GB Hard disk space. 4. Camera HARDWARE REQUIREMENTS SOFTWARE REQUIREMENTS • 1.Python programming language for coding the machine learning algorithms. • 2.OpenCV library for image processing and feature extraction. • 3.Jupyter & VS Code for IDE. • 3.TensorFlow or PyTorch for building and training deep learning models. • 4.Scikit-learn library for machine learning algorithms and evaluation metrics. • 5.Framework for developing the web application for the arecanut classification system like flask

  13. Project Schedule

  14. Conclusion • This project focuses on the early detection of diseases in Arecanut, leaves, and trunk using Convolutional Neural Networks. Experimentation is conducted using diseased and healthy arecanut image dataset of images. • The input image is first pre-processed, followed by feature extraction, training, and classification. The proposed System detects diseases of arecanut such as Mahalikoleroga, Stem bleeding, and yellow leaf spot. • Depending on the quality of the input image and the stage of the disease, the experimental results show varying levels of disease detection accuracy.

  15. Detection of Diseases in Arecanut Using Convolutional Neural Networks Authors : Anilkumar M G1, Karibasaveshwara TG1, Pavan HK1, Sainath Urankar1, Dr. Abhay Deshpande. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT),2019, Volume 08, Issue 05. • 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. • Mallaiah, Suresha & Danti, Ajit & Narasimha Murthy, S. Classification of Diseased Arecanut based on Texture Features. International Journal of Computer Applications. • Dhanuja K C.et.al. Areca Nut Disease Detection using Image Processing Technology. International Journal of Engineering Research 2020 V9. 10.17577/IJERTV9IS080352. • Vijai Singh ,A.K. Misra Detection of plant leaf diseases using image segmentation and soft computing techniques INFORMATION PROCESSING IN AGRICULTURE 4 (2017) 41–49 References

  16. THANK YOU

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