  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.
