A segmentation network for smart weed management in wheat fields

Abstract

Precision mechanical weed control is important for wheat cultivation. Accurate segmentation of weeds and wheat in images is a critical step in precision weeding. A modified U-net for segmenting wheat and weeds on images was presented in this paper. A image classification task was used to select the backbone network for encoding part. A image segmentation task on similar datasets was used to select and pre-training the decoding network. The training process applied the transfer learning. Experiment results show that the IoU of segmentation reached 88.98%, and the average speed on the embedded devices was 52 FPS. Results demonstrated that the modified neural network was able to effectively segment wheat and weed in the image. It can be used to guide precision weeding.