Attention-aided semantic segmentation network for weed identification in pineapple field

Abstract

Based on Unmanned aerial vehicle (UAV) platforms, targeted weed management has become the mainstream method to address the harm caused by weeds in farmland, and accurate weed area identification is an important prerequisite for this approach. In this paper, we conducted research on the existing issues in weed recognition based on PSPNet. To enhance the feature extraction ability of the model and improve its performance in complex field environments, we explored the effects of three types of attention modules when inserted into different positions in the network. The experimental results showed that the best performance was achieved when the ECA module was inserted into the SPP layer of the network. Additionally, to improve the utilization of limited data and enhance model performance, we designed a semi-supervised semantic segmentation method and conducted ablation experiments to determine the optimal network parameters and verify their effectiveness. Finally, we combined these two comprehensive optimization strategies and tested them on a weed dataset. The results show that the mean pixel accuracy (MPA) and F-score of the improved weed identification model are 88.66 % and 87.79 %, 3.82 % and 2.74 % higher than that of the original network while effectively increasing prediction accuracy. This study can provide a practical reference for precise weed distribution mapping and subsequent implementation of adaptive spraying on UAV platforms.