基于频域和信道混合关注和跨尺度语义融合的作物病害轻量级识别网络

IF 3.8 1区农林科学 Q1 AGRONOMY

Pest Management Science Pub Date : 2025-09-24 DOI:10.1002/ps.70170

Tiancan Jian, Haixia Qi, Riyao Chen, Yu Liang, Guangsheng Liang, Xiwen Luo

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引用次数: 0

摘要

准确识别作物病害对提高农业生产力至关重要；然而，由于复杂的现场条件和在资源有限的设备上部署的限制，它遇到了挑战。本研究旨在开发一种轻量级但精确的框架，称为FCDRNet，它集成了功能增强和压缩技术，以促进在现场的实际部署。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Lightweight crop disease identification network based on frequency domain and channel mixing attention and cross-scale semantic fusion

查看原文本刊更多论文

Lightweight crop disease identification network based on frequency domain and channel mixing attention and cross-scale semantic fusion

Accurate identification of crop diseases is essential for enhancing agricultural productivity; however, it encounters challenges arising from complex field conditions and the constraints of deploying on resource-limited devices. This study aims to develop a lightweight yet accurate framework, referred to as FCDRNet, which integrates feature enhancement and compression techniques to facilitate practical deployment in the field.

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来源期刊

Pest Management Science 农林科学-昆虫学

CiteScore

7.90

自引率

9.80%

发文量

553

审稿时长

4.8 months

期刊介绍： Pest Management Science is the international journal of research and development in crop protection and pest control. Since its launch in 1970, the journal has become the premier forum for papers on the discovery, application, and impact on the environment of products and strategies designed for pest management. Published for SCI by John Wiley & Sons Ltd.