Millet classification remains challenging due to high inter-class similarity and subtle intra-class variations, particularly in fine-grained varieties. This research presents a hybrid deep learning framework that combines deep visual features extracted from VGG19 with handcrafted statistical and texture-based descriptors to enhance classification performance across three millet types: Barnyard, Little Millet, and Proso. The proposed dual-stream architecture integrates Global Average Pooled VGG19 features with handcrafted descriptors in one branch, while a parallel VGG19 pipeline forms the second branch; both are fused using a bilinear fusion mechanism, followed by L2 normalization and softmax classification. Evaluated on a curated dataset under few-shot conditions with limited training samples per class, the model achieved \(100\%\) accuracy across all millet classes, as confirmed by the confusion matrix and classification report. These results highlight the robustness and accuracy gained by integrating handcrafted domain-specific features with deep neural representations, providing a significant step toward intelligent, automated millet classification with potential applications in large-scale grain sorting, quality control, and digital agricultural analytics using lightweight deep learning solutions.