SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction
This addresses a domain-specific issue for the agricultural and food industries by providing a rapid and reliable method to evaluate browning, though it is incremental as it builds on existing transformer architectures.
The paper tackles the problem of predicting lettuce browning, which reduces product quality, by developing a deep learning model called SQ-Swin that outperforms traditional methods and other deep learning backbones on fresh-cut lettuce datasets.
Packaged fresh-cut lettuce is widely consumed as a major component of vegetable salad owing to its high nutrition, freshness, and convenience. However, enzymatic browning discoloration on lettuce cut edges significantly reduces product quality and shelf life. While there are many research and breeding efforts underway to minimize browning, the progress is hindered by the lack of a rapid and reliable methodology to evaluate browning. Current methods to identify and quantify browning are either too subjective, labor intensive, or inaccurate. In this paper, we report a deep learning model for lettuce browning prediction. To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights. First, our model includes quadratic features in the transformer model which is more powerful to incorporate real-world representations than the linear transformer. Second, a multi-scale training strategy is proposed to augment the data and explore more of the inherent self-similarity of the lettuce images. Third, the proposed model uses a siamese architecture which learns the inter-relations among the limited training samples. Fourth, the model is pretrained on the ImageNet and then trained with the reptile meta-learning algorithm to learn higher-order gradients than a regular one. Experiment results on the fresh-cut lettuce datasets show that the proposed SQ-Swin outperforms the traditional methods and other deep learning-based backbones.