Tia-yolov5: an improved yolov5 network for real-time detection of crop and weed in the field

HIGHLIGHTS

• who: Aichen Wang et al. from the Nanjing Agricultural University, China have published the Article: TIA-YOLOv5: An improved YOLOv5 network for real-time detection of crop and weed in the field, in the Journal: (JOURNAL)
• what: In this paper, a fast and accurate workflow including a pixel-level synthesization data augmentation method and a TIA-YOLOv5 network was proposed for real-time weed and crop detection in the field.
• how: Showed that the average weed recognition accuracy of the model was 94.1% which was 5.5% and 9.9% higher than . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.