Standard

Potato Leaf Disease Detection Using the Convolutional Neural Network. / Haidari, Abdullah; Kumar, Avinash.
14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Haidari, A & Kumar, A 2023, Potato Leaf Disease Detection Using the Convolutional Neural Network. in 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings: book. Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), 30/11/2023. https://doi.org/10.1109/ELECO60389.2023.10416057

APA

Haidari, A., & Kumar, A. (2023). Potato Leaf Disease Detection Using the Convolutional Neural Network. In 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings: book (pp. 1-5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ELECO60389.2023.10416057

Vancouver

Haidari A, Kumar A. Potato Leaf Disease Detection Using the Convolutional Neural Network. In 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1-5 doi: 10.1109/ELECO60389.2023.10416057

Author

Haidari, Abdullah ; Kumar, Avinash. / Potato Leaf Disease Detection Using the Convolutional Neural Network. 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1-5

BibTeX

@inproceedings{5f53d9a36fe64bf4ab29b14be1cff70b,
title = "Potato Leaf Disease Detection Using the Convolutional Neural Network",
abstract = "Plant diseases, a momentous challenge which initial phase of identification diseases plays a prominent function in managing the prevalence of infection and improving the farming industry. The study is concerned about an approach for the development of a potatoes leaves disease's recolonization model by utilizing deep learning. the configuration of 80/20 is employed for training the model.The Adam is used as the optimizer, the augmentation techniques like flips, rotations are applied to avoid overfitting problem in order to improve the performance and robustness of the model. Our model obtained significant result with 97% accuracy and this study can be used to accurately assess potato leaf diseases detection. Our proposed model successfully performs classification on three types of potato leaves, including healthy, early blight, and late blight.",
author = "Abdullah Haidari and Avinash Kumar",
note = "The work was performed as part of research conducted in the Ural Mathematical Center with the financial support of the Ministry of Science and Higher Education of the Russian Federation (Agreement number 075-02-2023-913); 2023 14th International Conference on Electrical and Electronics Engineering (ELECO) ; Conference date: 30-11-2023 Through 02-12-2023",
year = "2023",
month = nov,
day = "30",
doi = "10.1109/ELECO60389.2023.10416057",
language = "English",
isbn = "979-835036049-3",
pages = "1--5",
booktitle = "14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Potato Leaf Disease Detection Using the Convolutional Neural Network

AU - Haidari, Abdullah

AU - Kumar, Avinash

N1 - The work was performed as part of research conducted in the Ural Mathematical Center with the financial support of the Ministry of Science and Higher Education of the Russian Federation (Agreement number 075-02-2023-913)

PY - 2023/11/30

Y1 - 2023/11/30

N2 - Plant diseases, a momentous challenge which initial phase of identification diseases plays a prominent function in managing the prevalence of infection and improving the farming industry. The study is concerned about an approach for the development of a potatoes leaves disease's recolonization model by utilizing deep learning. the configuration of 80/20 is employed for training the model.The Adam is used as the optimizer, the augmentation techniques like flips, rotations are applied to avoid overfitting problem in order to improve the performance and robustness of the model. Our model obtained significant result with 97% accuracy and this study can be used to accurately assess potato leaf diseases detection. Our proposed model successfully performs classification on three types of potato leaves, including healthy, early blight, and late blight.

AB - Plant diseases, a momentous challenge which initial phase of identification diseases plays a prominent function in managing the prevalence of infection and improving the farming industry. The study is concerned about an approach for the development of a potatoes leaves disease's recolonization model by utilizing deep learning. the configuration of 80/20 is employed for training the model.The Adam is used as the optimizer, the augmentation techniques like flips, rotations are applied to avoid overfitting problem in order to improve the performance and robustness of the model. Our model obtained significant result with 97% accuracy and this study can be used to accurately assess potato leaf diseases detection. Our proposed model successfully performs classification on three types of potato leaves, including healthy, early blight, and late blight.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85185843891

U2 - 10.1109/ELECO60389.2023.10416057

DO - 10.1109/ELECO60389.2023.10416057

M3 - Conference contribution

SN - 979-835036049-3

SP - 1

EP - 5

BT - 14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 14th International Conference on Electrical and Electronics Engineering (ELECO)

Y2 - 30 November 2023 through 2 December 2023

ER -

ID: 53741306