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 . . .
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