Sugar beet is a biennial herb with cold, drought, and salinity resistance and is one of the world’s major sugar crops. In addition to sugar, sugar beets are important raw materials for chemical and pharmaceutical products, and the residue after sugar extraction can be used to produce agricultural by-products, such as compound feed, which has a high comprehensive utilization value [1]. Field weeds, such as sugar beets, are harmful to crop growth and can compete with crops for sunlight and nutrients. If weeds are not removed in time during crop growth, they cause a decrease in crop yield and quality. Therefore, there is considerable interest in the development of automated machinery for selective weeding operations. The core component of this technology is a vision system that distinguishes between crops and weeds. To address the problems of difficult weed extraction, poor detection, and segmentation of region boundaries in traditional sugar beet detection, an end-to-end encoder–decoder model based on an improved UNet++ for segmentation is proposed in this paper and applied to sugar beet and weed detection. UNet++ can better fuse feature maps from different layers by skipping connections and can effectively preserve the details of sugar beet and weed images. The new model adds an attention mechanism to UNet++ by embedding the attention module into the upsampling process of UNet++ to suppress interference from extraneous noise. The improved model was evaluated on a sugar beet and weed dataset containing 1026 images. The image dataset in this study was obtained from sugar beet and weed images collected at the University of Bonn, Germany. According to the experimental results, the model can significantly eliminate noise and improve segmentation accuracy.

Keywords: ATT-NestedUNet, Deep learning, Weed detection, Semantic segmentation, Sugar beet

No potential conflict of interest relevant to this article was reported.