Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs

This paper proposes a modern system for recognizing sunflower diseases based on Bidirectional Encoder representation from Image Transformers (BEIT). The proposed system is capable of recognizing various sunflower diseases with high accuracy. The presented research results demonstrate the advantages...

Cijeli opis

Spremljeno u:

Bibliografski detalji
Glavni autori:	Baboshina, V. A., Бабошина, В. А., Lyakhov, P. A., Ляхов, П. А., Lyakhova, U. A., Ляхова, У. А., Pismennyy, V. A., Письменный, В. А.
Format:	Статья
Jezik:	English
Izdano:	Institution of Russian Academy of Sciences 2025
Teme:	Bidirectional encoder Image processing Image transformer Neural network recognition Sunflower diseases
Online pristup:	https://dspace.ncfu.ru/handle/123456789/30407
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!

id	ir-123456789-30407
record_format	dspace
spelling	ir-123456789-304072025-05-06T12:41:12Z Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs Baboshina, V. A. Бабошина, В. А. Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Pismennyy, V. A. Письменный, В. А. Bidirectional encoder Image processing Image transformer Neural network recognition Sunflower diseases This paper proposes a modern system for recognizing sunflower diseases based on Bidirectional Encoder representation from Image Transformers (BEIT). The proposed system is capable of recognizing various sunflower diseases with high accuracy. The presented research results demonstrate the advantages of the proposed system compared to known methods and contempo-rary neural networks. The proposed visual diagnostic system for sunflower diseases achieved 99.57 % accuracy on the sunflower disease dataset, which is higher than that of known methods. The approach described in the work can serve as an auxiliary tool for farmers, assisting them in promptly identifying diseases and pests and taking timely measures to treat plants. This, in turn, helps in preserving and enhancing the yield. This work can have a significant impact on the de-velopment of agriculture and the fight against the global food shortage problem. 2025-05-06T12:34:35Z 2025-05-06T12:34:35Z 2025 Статья Baboshina V.A., Lyakhov P.A., Lyakhova U.A., Pismennyy V.A. Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs // Computer Optics. - 2025. - 49 (3). - pp. 435 - 442. - DOI: 10.18287/2412-6179-CO-1514 https://dspace.ncfu.ru/handle/123456789/30407 en Computer Optics application/pdf application/pdf Institution of Russian Academy of Sciences
institution	СКФУ
collection	Репозиторий
language	English
topic	Bidirectional encoder Image processing Image transformer Neural network recognition Sunflower diseases
spellingShingle	Bidirectional encoder Image processing Image transformer Neural network recognition Sunflower diseases Baboshina, V. A. Бабошина, В. А. Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Pismennyy, V. A. Письменный, В. А. Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
description	This paper proposes a modern system for recognizing sunflower diseases based on Bidirectional Encoder representation from Image Transformers (BEIT). The proposed system is capable of recognizing various sunflower diseases with high accuracy. The presented research results demonstrate the advantages of the proposed system compared to known methods and contempo-rary neural networks. The proposed visual diagnostic system for sunflower diseases achieved 99.57 % accuracy on the sunflower disease dataset, which is higher than that of known methods. The approach described in the work can serve as an auxiliary tool for farmers, assisting them in promptly identifying diseases and pests and taking timely measures to treat plants. This, in turn, helps in preserving and enhancing the yield. This work can have a significant impact on the de-velopment of agriculture and the fight against the global food shortage problem.
format	Статья
author	Baboshina, V. A. Бабошина, В. А. Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Pismennyy, V. A. Письменный, В. А.
author_facet	Baboshina, V. A. Бабошина, В. А. Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Pismennyy, V. A. Письменный, В. А.
author_sort	Baboshina, V. A.
title	Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
title_short	Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
title_full	Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
title_fullStr	Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
title_full_unstemmed	Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
title_sort	bidirectional encoder representation from image transformers for recognizing sunflower diseases from photographs
publisher	Institution of Russian Academy of Sciences
publishDate	2025
url	https://dspace.ncfu.ru/handle/123456789/30407
work_keys_str_mv	AT baboshinava bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT babošinava bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT lyakhovpa bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT lâhovpa bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT lyakhovaua bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT lâhovaua bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT pismennyyva bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs AT pisʹmennyjva bidirectionalencoderrepresentationfromimagetransformersforrecognizingsunflowerdiseasesfromphotographs
_version_	1842245415147143168

Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs

Similar Items