A Frequency–Spatial Feature Fusion Framework for Mango Ripeness Recognition Under Extreme Class Imbalance

Ripeness recognition is a critical step in postharvest fruit handling. However, publicly available ripeness datasets are typically small and severely imbalanced, with a single dominant class accounting for more than 90% of the samples. Under such conditions, overall accuracy is dominated by the majority class and masks the deficiencies of the model on the minority classes. Accordingly, Macro-F1 is adopted as the primary evaluation metric. A spatial convolutional branch is combined with a wavelet-domain branch through feature-level fusion. Rather than introducing new architectural components, this study focuses on an evaluation protocol that tests whether the gain originates from frequency information. The protocol comprises a capacity-matched dual-spatial control, repetition over five random seeds, and bootstrap confidence intervals. Data-level image synthesis and algorithm-level Focal Loss are further compared for handling class imbalance. On the ripeness dataset, the fusion model achieved a slightly higher mean Macro-F1 (84.50%) than both the capacity-matched control and the single spatial branch. The same advantage was observed on a second mango dataset of surface defects. Data-level image synthesis improved Macro-F1 more than Focal Loss did, and the gains came mainly from backbones with weaker baselines. Because both datasets contain only mangoes, whether the framework generalizes to other fruits or domains remains to be tested.