WeedNet-R: a sugar beet field weed detection algorithm based on enhanced RetinaNet and context semantic fusion.

ABSTRACT: Accurate and dependable weed detection technology is a prerequisite for weed control robots to do autonomous weeding. Due to the complexity of the farmland environment and the resemblance between crops and weeds, detecting weeds in the field under natural settings is a difficult task. Existing deep learning-based weed detection approaches often suffer from issues such as monotonous detection scene, lack of picture samples and location information for detected items, low detection accuracy, etc. as compared to conventional weed detection methods. To address these issues, WeedNet-R, a vision-based network for weed identification and localization in sugar beet fields, is proposed. WeedNet-R adds numerous context modules to RetinaNet's neck in order to combine context information from many fea

SUBMITTER: Guo Z 

PROVIDER: S-EPMC10408303 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature