Next Article in Journal
Comparing DInSAR and PSI Techniques Employed to Sentinel-1 Data to Monitor Highway Stability: A Case Study of a Massive Dobkovičky Landslide, Czech Republic
Previous Article in Journal
Self-Adjusting Thresholding for Burnt Area Detection Based on Optical Images
Open AccessArticle

Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN

1
Department of Engineering, Aarhus University, Finlandsgade 22, DK-8200 Aarhus N, Denmark
2
Department of Agroecology, Aarhus University, Forsøgsvej 1, DK-4200 Slagelse, Denmark
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2671; https://doi.org/10.3390/rs11222671
Received: 7 October 2019 / Revised: 6 November 2019 / Accepted: 13 November 2019 / Published: 15 November 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Lack of annotated data for training of deep learning systems is a challenge for many visual recognition tasks. This is especially true for domain-specific applications, such as plant detection and recognition, where the annotation process can be both time-consuming and error-prone. Generative models can be used to alleviate this issue by producing artificial data that mimic properties of real data. This work presents a semi-supervised generative adversarial network (GAN) model to produce artificial samples of plant seedlings. By applying the semi-supervised approach, we are able to produce visually distinct samples for nine unique plant species using a single GAN model, while still maintaining a relatively high visual variance in the produced samples for each species. Additionally, we are able to control the appearance of the generated samples with respect to rotation and size through a set of latent variables, despite these not being annotated features in the training data. The generated samples resemble the intended species with an average recognition accuracy of ∼64.3%, evaluated using an external state-of-the-art plant seedling classification model. Additionally, we explore the potential of using the GAN model’s discriminator as a quality assessment tool to remove poor representations of plant seedlings from the artificial samples. View Full-Text
Keywords: generative model; generative adversarial networks; supervised learning; unsupervised learning generative model; generative adversarial networks; supervised learning; unsupervised learning
Show Figures

Graphical abstract

MDPI and ACS Style

Madsen, S.L.; Mortensen, A.K.; Jørgensen, R.N.; Karstoft, H. Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN. Remote Sens. 2019, 11, 2671.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop