Image Classification for Soybean and Weeds Based on ViT

Author:

Liang Jingxin,Wang Dong,Ling Xufeng

Abstract

Abstract Abstracts. In this paper, ViT deep neural network based on self-attention mechanism is used in classification for images of soybean and weeds. Firstly, the overall image is split into multiple tiles; with each tile regarded as a word, the whole image is regarded as a sentence, which can be used for image semantic recognition by natural language processing technology. We designed a ViT network with sequence length of 50, embedded dimension of 384, and self-attention module layers of 12. With soybean weed classification dataset, the network is trained, verified and tested. Experimental results showed that ViT network is superior in classification on dataset of soybean and weeds, with excellent generalization capability.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Reference9 articles.

1. Application of image processing technology in weed recognition in field;Ren;Journal of Chinese Agricultural Mechanization,2020

2. Weed recognition based on SVM-DS multi-feature fusion;He;Transactions of the Chinese Society for Agricultural Machinery,2013

3. Identification of weeds in rice seedling stage based on convolution neural network and transfer learning;Deng;Agricultural Mechanization Research Journal,2021

4. Recognition method for weeds in rapeseed field based on faster R-CNN deep network;Zhang;Laser & Optoelectronics Progress,2020

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