DOI: 10.20350/digitalcsic/17916
title: DeepfakeWeedSet

description: [Description of methods used for collection/generation of data] The artificial weed imagery contained in the DeepfakeWeedSet (_1 and _2) was generated using Stable Diffusion, a modern generative AI model that was conditioned with a set of real field images from Moreno et al. (2025). These real images depicted weed species naturally occurring in tomato crops, specifically Solanum nigrum, Portulaca oleracea, and Setaria verticillata. They served as the visual foundation from which Stable Diffusion learned the characteristic shapes, colors, and textures of each weed species. Once this reference material was provided, Stable Diffusion was used to synthesize thousands of new, highly realistic images, resulting in an artificial dataset comprising 10,800 synthetic images that include a total of 12,592 annotated weed instances (i.e., bounding boxes). References Hugo Moreno, Gabriel Rivera, Dionisio Andújar. Ground-based imagery dataset for early weed classification in tomato crops,Data in Brief. Volume 63,2025,112249,ISSN 2352-3409,https://doi.org/10.1016/j.dib.2025.112249.
description: [Methods for processing the data] The artificial images produced by Stable Diffusion are automatically labeled so they can be used to train object‑detection models. The process begins by converting each artificial image into an ExG‑R representation, a vegetation‑enhancement transformation that highlights green plant tissue and suppresses soil and background information. These ExG‑R images are then thresholded to create binary masks in which vegetation appears in white and the background in black. From these binary masks, an automated labeling system detects individual connected vegetation regions and places bounding boxes around them, producing initial annotations for each weed instance in the artificial image. Due to the fact that automated labeling is not perfect, especially when plants overlap or when leaf shapes produce irregular contours, a final manual correction step was applied. In this step, a human annotator reviewed the automatically generated bounding boxes and refined them as needed to ensure accurate placement and sizing. This semi‑automated workflow significantly accelerated annotation while maintaining high labeling quality. The annotation is under the PascalVOC format employed for exporting the corresponding XML files. This format records the coordinates of bounding boxes that delineate the objects within each image. All bounding boxes associated with a single image were stored in one XML file. Each object entry includes the coordinates of the upper-left and lower-right corners—denoted as xmin, ymin, xmax, and ymax—along with the class name of the labeled object. Three weed species were categorized with the assistance of weed science experts and labeled according to the EPPO (European and Mediterranean Plant Protection Organization) code system. The assigned labels are as follows: 1. SOLNI: Solanum nigrum L. 2. POROL: Portulaca oleracea L. 3. SETVE: Setaria verticillata L. These annotations provide a structured foundation for training and validating object detection models, facilitating accurate weed identification in early growth stages.