ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

Rice is a staple food for over half of the global population, but it faces significant yield losses: up to 52% due to leaf blast disease and brown spot diseases, respectively. This study aimed at proposing a hybrid architecture, namely ResViT-Rice, by taking advantage of both CNN and transformer for...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Yujia Zhang, Luteng Zhong, Yu Ding, Hongfeng Yu, Zhaoyu Zhai
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: MDPI AG 2023-06-01
Sarja:Agriculture
Aiheet:
leaf blast disease
brown spot disease
hybrid architecture
transformer encoder
convolutional neural network
Linkit:https://www.mdpi.com/2077-0472/13/6/1264

Internet

https://www.mdpi.com/2077-0472/13/6/1264

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