Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation

TL;DR

This paper investigates the effectiveness of active learning strategies, particularly PowerBALD, in reducing annotation effort for crop-weed semantic segmentation in agriculture, highlighting challenges like class imbalance and image redundancy.

Contribution

It provides a comparative analysis of active learning methods on agricultural datasets, revealing limited improvements due to dataset challenges, and offers insights for future research.

Findings

PowerBALD outperformed random sampling in model performance

High image redundancy and class imbalance affected results

Significant performance differences were not observed due to dataset issues

Abstract

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.