TY - GEN

T1 - Application of neural networks to image recognition of wheat rust diseases

AU - Genaev, Mikhail

AU - Ekaterina, Skolotneva

AU - Afonnikov, Dmitry

PY - 2020/7

Y1 - 2020/7

N2 - Rust diseases of cereals are caused by pathogenic fungi and can significantly reduce plant productivity. Many cultures are subject to them. The disease is difficult to control on a large scale, so one of the most relevant approaches is crop monitoring, which helps to identify the disease at an early stage and make efforts to prevent its spread. One of the most effective methods of control is the identification of the disease from digital images that obtained by a smartphone camera. In this paper, we present a deep learning algorithm that uses a digital image of wheat plants to determine whether they are affected by a disease and, if so, what type: leaf rust or stem rust. The algorithm based on the convolution neural network of the densenet architecture. The resulting model demonstrates high accuracy of classification: the measure of accuracy F1 on the validation sample is 0.9, the AUC averaged over 3 classes is 0.98.

AB - Rust diseases of cereals are caused by pathogenic fungi and can significantly reduce plant productivity. Many cultures are subject to them. The disease is difficult to control on a large scale, so one of the most relevant approaches is crop monitoring, which helps to identify the disease at an early stage and make efforts to prevent its spread. One of the most effective methods of control is the identification of the disease from digital images that obtained by a smartphone camera. In this paper, we present a deep learning algorithm that uses a digital image of wheat plants to determine whether they are affected by a disease and, if so, what type: leaf rust or stem rust. The algorithm based on the convolution neural network of the densenet architecture. The resulting model demonstrates high accuracy of classification: the measure of accuracy F1 on the validation sample is 0.9, the AUC averaged over 3 classes is 0.98.

KW - CNN

KW - deep learning

KW - image analysis

KW - leaf rust

KW - phenotyping

KW - stem rust

KW - wheat

UR - http://www.scopus.com/inward/record.url?scp=85094832890&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=45183075

U2 - 10.1109/CSGB51356.2020.9214703

DO - 10.1109/CSGB51356.2020.9214703

M3 - Conference contribution

AN - SCOPUS:85094832890

T3 - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

SP - 40

EP - 42

BT - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

Y2 - 6 July 2020 through 10 July 2020

ER -