HIGHLIGHTS
- who: Shenglian Lu and collaborators from the Normal University, Guilin, China have published the article: Swin-Transformer-YOLOv5 for Real-Time Wine GrapeBunch Detection, in the Journal: (JOURNAL) of 18/Nov/2022
- what: Some of the major reasons, which made object detection challenging in agricultural environments, include severe occlusions from non-target objects (e_g, leaves, branches, trellis-wires, and densely clustered fruits) to target objects (e_g, fruit) . Combined with all these advantages, this study attempted to detect grape bunches in dense canopies using the improved YOLOv5. This research proposed the combination of architectures from a . . .
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