Automated generation of ground truth images of greenhouse-grown plant shoots using a GAN approach.

ABSTRACT: The generation of a large amount of ground truth data is an essential bottleneck for the application of deep learning-based approaches to plant image analysis. In particular, the generation of accurately labeled images of various plant types at different developmental stages from multiple renderings is a laborious task that substantially extends the time required for AI model development and adaptation to new data. Here, generative adversarial networks (GANs) can potentially offer a solution by enabling widely automated synthesis of realistic images of plant and background structures. In this study, we present a two-stage GAN-based approach to generation of pairs of RGB and binary-segmented images of greenhouse-grown plant shoots. In the first stage, FastGAN is applied to augment original

SUBMITTER: Ullah S 

PROVIDER: S-EPMC12495773 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature