Novel data augmentation strategies to boost supervised segmentation of plant disease

Annotation of images in supervised learning is notably costly and time-consuming. In order to reduce this cost, our objective was to generate images from a small dataset of annotated images, and then use those synthesized images to help the network's training process. In this article, we tackled for illustration with agricultural material the difficult segmentation task of apple scab on images of apple plant canopy by using convolutional neural networks. We devised two novel methods of generating data for this use case: one based on a plant canopy simulation and the other on Generative Adversatial Networks (GANs). As a result, we found that simulated data could provide an important increase in segmentation performance, up to a 17% increase of F1 score (a measure taking into account precision and recall), compared to segmenting with weights initialized on ImageNet. In this way, we managed to obtain, with small datasets, higher segmentation scores than the ones obtained with bigger datasets if using no such augmentations. Moreover, we left our annotated dataset of scab available for the plant science imaging community. The proposed method is of large applicability for plant diseases observed at a canopy scale.