Image-Based Wheat Fungi Diseases Identification by Deep Learning

Plants

Elena I. Gultyaeva¹  Mikhail A. Genaev²  Ekaterina S. Skolotneva²  Dmitry A. Afonnikov²  Elena A. Orlova³  Nina P. Bechtold³ 

[1] All Russian Institute of Plant Protection, Pushkin, 196608 St. Petersburg, Russia;Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia;Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630501 Krasnoobsk, Russia;

关键词: wheat;    leaf rust;    powdery mildew;    septoria;    stem rust;    yellow rust;   

DOI  :  10.3390/plants10081500

来源: DOAJ

【 摘 要 】

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.

【 授权许可】

Unknown