FAF-Net: A Lightweight Frequency-Auxiliary Fusion Network for Plant Disease Classification in Natural-Scene Images

Plant disease classification in natural scenes remains challenging because disease symptoms are often localized and imaging conditions are complex, including cluttered backgrounds, illumination variations, scale changes, and fine-grained inter-class similarities. To address these challenges, this study proposes FAF-Net, a frequency-aware fusion network with auxiliary supervision for plant disease classification in natural scenes. The proposed framework is built on EfficientNet-B3 and integrates three complementary strategies: CutMix augmentation, an FFT-based frequency branch, and a healthy/diseased auxiliary supervision branch. The RGB branch extracts spatial semantic features from natural-scene images, whereas the frequency branch converts the input image into a log-normalized Fourier magnitude spectrum and learns complementary texture representations. The auxiliary branch provides coarse-grained health-status supervision during training, encouraging the shared representation to capture disease-relevant features. Experiments were conducted on the PlantDoc dataset, which contains 2598 images from 27 healthy and diseased categories. Compared with the EfficientNet-B3 baseline, FAF-Net improved the classification accuracy from 69.49% to 74.58%, corresponding to a gain of 5.09 percentage points. Ablation results further indicate that CutMix, frequency-domain features, and auxiliary supervision provide complementary improvements. These results suggest that frequency-aware feature fusion and coarse-grained auxiliary supervision can enhance plant disease classification under natural-scene conditions.