  Optimizing deep learning models requires large amounts of annotated images, a
process that is both time-intensive and costly. Especially for semantic
segmentation models in which every pixel must be annotated. A potential
strategy to mitigate annotation effort is active learning. Active learning
facilitates the identification and selection of the most informative images
from a large unlabelled pool. The underlying premise is that these selected
images can improve the model's performance faster than random selection to
reduce annotation effort. While active learning has demonstrated promising
results on benchmark datasets like Cityscapes, its performance in the
agricultural domain remains largely unexplored. This study addresses this
research gap by conducting a comparative study of three active learning-based
acquisition functions: Bayesian Active Learning by Disagreement (BALD),
stochastic-based BALD (PowerBALD), and Random. The acquisition functions were
tested on two agricultural datasets: Sugarbeet and Corn-Weed, both containing
three semantic classes: background, crop and weed. Our results indicated that
active learning, especially PowerBALD, yields a higher performance than Random
sampling on both datasets. But due to the relatively large standard deviations,
the differences observed were minimal; this was partly caused by high image
redundancy and imbalanced classes. Specifically, more than 89\% of the pixels
belonged to the background class on both datasets. The absence of significant
results on both datasets indicates that further research is required for
applying active learning on agricultural datasets, especially if they contain a
high-class imbalance and redundant images. Recommendations and insights are
provided in this paper to potentially resolve such issues.
