Work

Early Plant Classification Model Based on Dual Attention Mechanism and Multi-Scale Module

Year: 2025

Type: article

Abstract: In agricultural planting, early plant classification is an indicator of crop health and growth. In order to accurately classify early plants, this paper proposes a classification method combining a dual attention mechanism and multi-scale module. Firstly, the ECA module (Efficient channel attention) is added to enhance the attention of the network to plants and suppress irrelevant background noise; secondly, the MSFN (Multi-scale Feedforward Network) module is embedded to improve the ability to capture complex data features. Finally, CA (Channel attention) is added to further emphasize the extracted features, thus enhancing the discrimination ability and improving the accuracy of the model. The experimental results show an accuracy of 93.20%, precision of 94.53%, recall of 93.27%, and an F1 score of 93.39%. This study can realize the classification of early plants, and effectively distinguish crops from weeds, which is helpful to identify and realize accurate weeding, thus promoting the intelligent and modern process of agricultural production.

Source: AgriEngineering

DOI: 10.3390/agriengineering7030066

Authors: Tonglai Liu, Xuanzhou Chen, Wanzhen Zhang, Xuekai Gao, Liqiong Lu, Shuangyin Liu

Institutions: Zhongkai University of Agriculture and Engineering, Lingnan Normal University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,930