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

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.