Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Plant Research Using Aerial Imagery

Themes: Feedstock Production

Keywords: AI/ML, Miscanthus, Modeling

Citation

Varela, S., Zheng, X., Njuguna, J., Sacks, E., Allen, D., Ruhter, J., Leakey, A.D.B. April 23, 2025. “Dataset: Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery.” University of Illinois. DOI: 10.13012/B2IDB-8462244_V2.

Overview

ESGAN and data workflow including the generator (G) and discriminator (D) submodels utilized to assess flowering status.

This dataset supports the implementation described in the manuscript “Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery.”  It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper.  For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.

Data

Illinois Data Bank: Includes aerial plot images and plot IDs.

GitHub: Includes Python script and workflow

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