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September 26, 2025

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

Key Points

FCDRNet achieves an accuracy of 96.60% on the Peanut Leaf Disease Dataset, showing strong performance on crop disease identification.
The model employs three innovations, notably the frequency-channel mixing attention module that captures local and global features effectively.
This study utilizes a compression strategy that reduces parameters by 70.6%, significantly improving efficiency for field deployment.
The findings indicate potential for scalable, real-time inference in smart agriculture, with accuracy maintained at minimal degradation during compression.

Abstract

Abstract BACKGROUND 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. RESULTS FCDRNet introduces three key innovations: 1) a frequency‐channel mixing attention (FCMA) module that integrates median‐enhanced channel pooling with wavelet‐based frequency attention to effectively capture both local and global features; 2) a cross‐scale semantic fusion (CSF) module that facilitates adaptive multiscale lesion recognition; and 3) a DepGraph‐RKD compression strategy that reduces parameters by 70.6% (from 4.32 M to 1.27 M) and FLOPs by 56.98% (from 256.73 M to 110.43 M). Evaluations on the Peanut Leaf Disease Dataset (PLDD) and PlantVillage Dataset (PD) datasets demonstrate that FCDRNet achieves accuracies of 96.60% and 99.67%, respectively, surpassing baseline models by 3.31% and 2.23%. Notably, the compression method maintains robustness with an accuracy degradation of ≤0.14%, enabling real‐time inference at 14.84 ms on embedded devices. CONCLUSION FCDRNet offers scalable solutions for smart agriculture by synergistically integrating attention mechanisms, semantic fusion and dependency‐aware compression. It achieves a balanced performance in terms of accuracy and efficiency, with accuracy rates of 96.60%, 99.67% and 97.77% on three datasets: the PLDD, PD and PlantDoc, respectively. This performance has propelled the development of practical, field‐deployable crop disease monitoring systems, effectively addressing critical gaps in identification accuracy and the limitations associated with edge deployment. © 2025 Society of Chemical Industry.

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