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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Published Paper Details
Paper Title: Plant Disease Detection System
Authors Name: Fazle Hasan , Pranay Bobde , Vedant Naikwade , Yash Thavkar , Prof. Vaishali Patil
Unique Id: IJSDR2212107
Published In: Volume 7 Issue 12, December-2022
Abstract: Plant diseases are considered one of the main factors influencing food production and minimize losses in production, and it is essential that plant diseases should be recover with the recommendation of specialist. The recent expansion of deep learning methods which has been used in this project for plant disease detection, offering a robust tool with highly accurate results. This project identify the state of the art, with the use of convolutional neural networks (CNN) in the process of identification and classification of plant diseases. With the help of CNN, we can able to diagnose the diseases of plants and recommend the best medicine for the infected plant. This model successfully achieved the average accuracy of 95.69%. And then connecting the model with localhost website with help of API.
Keywords: CNN (convolution neural network), GDP (gross development Product), DL (Deep learning), OS (Operating system) and IO (Input Output)
Cite Article: "Plant Disease Detection System", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 12, page no.695 - 698, December-2022, Available :http://www.ijsdr.org/papers/IJSDR2212107.pdf
Downloads: 000201541
Publication Details: Published Paper ID: IJSDR2212107
Registration ID:203007
Published In: Volume 7 Issue 12, December-2022
DOI (Digital Object Identifier):
Page No: 695 - 698
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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