FSCA-EUNet: Lightweight Classification of Stacked Jasmine Bloom-Stages via Frequency–Spatial Cross-Attention for Industrial Scenting Automation

TL;DR

This paper introduces a lightweight AI model to classify the bloom stages of stacked jasmine flowers in tea scenting processes, enabling real-time automation.

Contribution

A novel U-Net model with frequency-spatial cross-attention is proposed for efficient and accurate classification of jasmine bloom stages.

Findings

The model achieved 95.52% precision and 97.24% mean average precision on a custom dataset.

It runs at 22.33 FPS on edge devices with only 878.851 K parameters and 15.445 G FLOPs.

Ablation studies confirmed the effectiveness of each module in improving classification accuracy.

Abstract

To address the challenge of monitoring the postharvest jasmine bloom stages during industrial tea scenting processes, this study proposes an efficient U-shaped Network (U-Net) model with frequency–spatial cross-attention (FSCA-EUNet) to resolve critical bottlenecks, including repetitive backgrounds and small interclass differences, caused by stacked jasmine flowers during factory production. High-resolution images of stacked jasmine flowers were first preprocessed and input into FSCA-EUNet, where the encoder extracted multi-scale spatial features and the FSCA module incorporated frequency-domain textures. The decoder then fused and refined these features, and the final classification layer output the predicted bloom stage for each image. The proposed model was designed as a “U-Net”-like structure to preserve multiscale details and employed a frequency–spatial cross-attention module to extract high-frequency texture features via a discrete cosine transform. Long-range dependencies were established by NonLocalBlook, located after the encoders in the model. Finally, a momentum-updated center loss function was introduced to constrain the feature space distribution and enhance intraclass compactness. According to the experimental results, the proposed model achieved the best metrics, including 95.52% precision, 95.42% recall, 95.40% F1-score, and 97.24% mean average precision, on our constructed dataset with only 878.851 K parameters and 15.445 G Floating Point Operations (FLOPs), and enabled real-time deployment at 22.33 FPS on Jetson Orin NX edge devices. The ablation experiments validated the improvements contributed by each module, which significantly improved the fine-grained classification capability of the proposed network. In conclusion, FSCA-EUNet effectively addresses the challenges of stacked flower backgrounds and subtle interclass differences, offering a lightweight yet accurate framework that enables real-time deployment for industrial jasmine tea scenting automation.

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