Miranda Miro, Zabawa Laura, Kicherer Anna, Strothmann Laurenz, Rascher Uwe, Roscher Ribana
Remote Sensing Group, Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany.
Institute of Geodesy and Geoinformation, Professorship of Geodesy, University of Bonn, Bonn, Germany.
Front Plant Sci. 2022 Jun 1;13:729097. doi: 10.3389/fpls.2022.729097. eCollection 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 the 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 that was successfully applied to a similar task and show that our approach outperforms it.
葡萄是经济上最重要的优质作物之一。因此，在生长期间监测植株表现对于确保高品质的最终产品很重要。这包括观察、检测并相应减少不健康的浆果（物理损伤或患病的）。在收获时，不必知道损伤的确切原因，而是要知道损伤是否明显。由于收获前的人工筛选和挑选既耗时又昂贵，我们提出了一种基于图像的自动机器学习方法，该方法可以直接将观察者引导至异常区域，而无需人工监测每一株植物。具体来说，我们仅在有健康浆果的图像上训练一个带有特征感知损失的全卷积变分自编码器，并将与该模型存在偏差的图像区域视为受损浆果。我们使用热图来可视化训练后的神经网络的结果，从而为农民的决策提供支持。我们将我们的方法与一个成功应用于类似任务的卷积自编码器进行比较，并表明我们的方法优于它。
