Weighted skip-connection feature fusion: A method for augmenting UAV oriented rice panicle image segmentation

Unmanned aerial vehicles (UAVs) have the potential to reduce manual interventions in digitizing farmland and improve the accuracy and efficiency of data collection. Measuring crop yields with UAVs is a critical step in achieving precision agriculture on unmanned farms. The key to estimating rice yield is to distinguish the panicle region from the non-panicle region based on semantic segmentation. However, the traditional semantic seg-mentation model, such as the UNet, has an inferior segmentation performance on UAV images. In addition, UAVs are unable to segment the rice panicle region in real time due to the limited edge computing capabilities for some improved UNet models. To address this issue, this study proposes a new method for augmenting UAV rice panicle image segmentation called weighted skip-connection feature fusion (WSFF). Furthermore, a novel model WSUNet is constructed by combining WSFF and UNet model, which aims to enhance the performance of rice panicle segmentation without additional computational cost.Two datasets of rice panicle images taken with UAV are constructed. These two and cell nuclei dataset are used to compare the performance of UNet, WSUNet and UNet++. Mean intersection over union (mIOU) and mean pixel accuracy (mPA) are adopted as evaluation metrics. In terms of mIOU, the results indicate that WSUNet outperforms the UNet on all datasets, with a maximum increase of 2.84 on the rice panicle dataset. And the average inferencing speed (AIS) of WSUNet on CPU is 2.1 times that of UNet++. Additionally, in order to verify the role of WSFF, 1WSUNet and 2WSUNet are constructed based on WSFF with two different skip-connection modes. By observing the training scalars of 1WSUNet, 2WSUNet, WSUNet, and UNet, it can be seen that the model set with WSFF has a more competitive learning ability than UNet, and the segmentation performance of the model could be further improved with the increase of the amount of skip-connection.

Automated summary

Unmanned aerial vehicles (UAVs) have the potential to enhance precision agriculture in unmanned farms by reducing manual interventions and improving data collection efficiency. This study proposes a new method called weighted skip-connection feature fusion (WSFF) to augment UAV rice panicle image segmentation. The constructed model WSUNet, which combines WSFF and UNet, shows improved segmentation performance without additional computational cost.