A wavelet-based frequency-domain approach for accurate multi-crop disease detection.

Timely localization and diagnosis of crop lesions are critical for disease control and reducing pesticide use. However, in-field lesions often resemble leaf textures, vary widely in scale, and suffer from lighting and shadow interference-making simultaneous high accuracy and lightweight inference challenging. We propose WGA-YOLO, a lightweight YOLO variant for crop disease recognition. Central to our design is Wavelet Channel Recalibration (WCR), a DWT-based downsampling module: discrete wavelet transform naturally provides multi-resolution, time-frequency localized representations that explicitly separate low-frequency approximations from high-frequency edge/texture details. WCR fuses high- and low-frequency components and enhances feature representation through their frequency-domain complementarity, thereby preserving semantic and fine texture information during resolution reduction with negligible extra cost. We also introduce PS-C2f, which integrates Pinwheel-shaped convolutions into C2f to better capture tiny lesion details via multi-directional, irregular kernels, and replace SPPF with Dynamic Group Attention Pooling (DGAP) for efficient multi-scale context aggregation. On our PlantDoc_boost dataset, WGA-YOLO improves over YOLOv8n by 3.02 and 2.85% points, while reducing parameters and FLOPs by ~ 0.18 M and ~ 0.3G, demonstrating improved inference efficiency and deployment friendliness while maintaining strong detection performance in field scenarios.