TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 2; p. 547
• Main Authors: Fang, Wenhui, Chen, Weizhen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Accuracy; Agriculture; Algorithms; Analysis; Artificial intelligence; Classification; computer vision; Corn; Deep learning; Disease; distributed shift convolution; Efficiency; Harvest; intelligence; Leaves; Localization; Tea; tea buds; Tea industry; Unmanned aerial vehicles; YOLOv8n; China; distributed shift convolution; YOLOv8n; computer vision; tea buds; intelligence

Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we develo...