Effects of Foreground Augmentations in Synthetic Training Data on the Use of UAVs for Weed Detection

Simon Hallösta, Mats Ingemar Pettersson, Mattias Dahl

Keywords: Foreground augmentations, Synthetic training data, UAVs (Unmanned Aerial Vehicles), Weed detection, Precision agriculture, Black-grass, Alopecurus myosuroides, Mask R-CNN mode

Abstract:

This study addresses the issue of black-grass, a herbicide-resistant weed
that threatens wheat yields in Western Europe, through the use of high-
resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation
in precision agriculture. We mitigate challenges such as the need for large
labeled datasets and environmental variability by employing synthetic data
augmentations in training a Mask R-CNN model. Using a minimal dataset of 43
black-grass and 12 wheat field images, we achieved a 37\% increase in Area
Under the Curve (AUC) over the non-augmented baseline, with scaling as the
most effective augmentation. The best model attained a recall of 53\% at a
precision of 64\%, offering a promising approach for future precision
agriculture applications.

Submission Number: 50