@inproceedings{552796a674594fee8f7098c9f1c3b188,
title = "Detection of plant diseases in the images using Deep Neural Networks",
abstract = "Agriculture suffers from crop diseases, and losses yield every year. Early detection of crop diseases can effectively decrease the loss. Leaves from crops are affected by the disease and can help farmers to detect any changes. Our study uses crops labelled dataset to train the Faster-RCNN model to identify if leaves are affected by any means. Our study shows more than 97% accuracy to detect disease in early stages that framers were unable to do in the past.",
keywords = "Deep Neural Networks, Plants",
author = "Gul, {Malik Urfa} and Seungmin Rho and Anand Paul and Sanghyun Seo",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 ; Conference date: 16-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
doi = "10.1109/CSCI51800.2020.00137",
language = "English",
series = "Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "738--739",
booktitle = "Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020",
address = "United States",
}