Evolutionary conditional GANs for supervised data augmentation: The case of assessing berry number per cluster in grapevine

Abstract

In viticulture, training a deep learning model to assess the number of berries in a cluster image requires labeling by manually counting the berries on each grape cluster. This repeating, demanding task directly opposes the need for large amounts of data of deep learning methods. The objective of this work is the development of evolutionary conditional generative adversarial networks (GANs) for supervised data augmentation in the task of assessing berry number per cluster in grapevine. Ninety-seven grape cluster images were collected and labeled by manually counting the berries, making up the original dataset, and it was used for the training of a conditional GAN using evolutionary schemes involving a population of generators competing within a common and dynamic environment: the discriminator. After generative networks training, the best performing generator was used for supervised data augmentation: generation of labeled images conditioned to a berry number, making up the augmented dataset. Two models were trained with one dataset each, original and augmented, both having approximately the same number of samples, around 400. The original dataset was enriched with traditional image transforms, while the augmented dataset had incorporated images from the trained GAN generator. The two models were trained using the same convolutional regression network architecture, and then tested on an external image dataset, with more than 1300 images not used in any training, to compare the performance of GAN data augmentation. Results showed that the model augmented with GANs yielded lower error values, with a validation error of 43 berries, than those from the original model, with a validation error of 65 berries. In the test dataset, the augmented model obtained an error of 39 berries (R2 = 0.75), surpassing again the original model, that obtained an error of 57 berries (R2 = 0.65). These results evidence that evolutionary conditional GANs generate synthetic labeled images that lead to higher performance deep learning regression models for assessing berry number from cluster images.