Predicting Cherry Quality Using Siamese Networks

The cherry industry is a rapidly growing sector of New Zealand's export merchandise and, as such, the accuracy with which pack-houses can grade cherries during processing is becoming increasingly critical. Conventional computer vision systems are usually employed in this process, yet they fall short in many respects, still requiring humans to manually verify the grading. In this work, we investigate the use of deep learning to improve upon the traditional approach. The nature of the industry means that the grade standards are influenced by a range of factors and can change on a daily basis. This makes conventional classification approaches infeasible (as there are no fixed classes) so we construct a model to overcome this. We convert the problem from classification to regression, using a Siamese network trained with pairwise comparison labels. We extract the model embedded within to predict continuous quality values for the fruit. Our model is able to predict which of two similar quality fruit is better with over 88% accuracy, only 5% below the self-agreement of a human expert.