Plant Disease Detection System
Fazle Hasan
, Pranay Bobde , Vedant Naikwade , Yash Thavkar , Prof. Vaishali Patil
CNN (convolution neural network), GDP (gross development Product), DL (Deep learning), OS (Operating system) and IO (Input Output)
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.
"Plant Disease Detection System", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 12, page no.695 - 698, December-2022, Available :https://ijsdr.org/papers/IJSDR2212107.pdf
Volume 7
Issue 12,
December-2022
Pages : 695 - 698
Paper Reg. ID: IJSDR_203007
Published Paper Id: IJSDR2212107
Downloads: 000347323
Research Area: Engineering
Country: Nagpur, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
