Detection of Anomalous Grapevine Berries Using Variational Autoencoders - Miranda, Miro; Zabawa, Laura; Kicherer, Anna; Strothmann, Laurenz; Rascher, Uwe; Roscher, Ribana (Frontiers in Plant Science, 2022)
Grapevine is one of the economically most important quality crops. The monitoring of the plant performance during the growth period is therefore important to ensure a high quality end-product. This includes the observation, detection and respective reduction of unhealthy berries (physically damaged or diseased). At harvest, it is not necessary to know the exact cause of the damage, but rather if a damage is apparent or not. Since a manual screening and selection before harvest is time-consuming and expensive, we propose an automatic, image-based machine learning approach, which can lead observers directly to anomalous areas without the need to monitor every plant manually. Specifically, we train a fully convolutional variational autoencoder with a feature perceptual loss on images with healthy berries only and consider image areas with deviations from this model as damaged berries. We use heatmaps which visualize the results of the trained neural network, and therefore support the decision making for farmers. We compare our method against a convolutional autoencoder which was successfully applied to a similar task and show that our approach outperforms it.