Fine-grained weed recognition using Swin Transformer and two-stage transfer learning.

ABSTRACT: Weeding is very critical for agriculture due to its importance for reducing crop yield loss. Accurate recognition of weed species is one of the major challenges for achieving automatic and precise weeding. To improve the recognition performance of weeds and crops with similar visual characteristics, a fine-grained weed recognition method based on Swin Transformer and two-stage transfer learning is proposed in this study. First, the Swin Transformer network is introduced to learn the discriminative features that can distinguish subtle differences between visually similar weeds and crops. Second, a contrastive loss is applied to further enlarge the feature differences between different categories of weeds and crops. Finally, a two-stage transfer learning strategy is proposed to address the p

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10040655 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature