Detection of Famous Tea Buds Based on Improved YOLOv7 Network

Yongwei Wang, Maohua Xiao, Shu Wang, Qing Jiang, Xiaochan Wang and Yongnian Zhang ()
Additional contact information
Yongwei Wang: Engineering College, Nanjing Agricultural University, Nanjing 210031, China
Maohua Xiao: Engineering College, Nanjing Agricultural University, Nanjing 210031, China
Shu Wang: Engineering College, Nanjing Agricultural University, Nanjing 210031, China
Qing Jiang: Engineering College, Nanjing Agricultural University, Nanjing 210031, China
Xiaochan Wang: Engineering College, Nanjing Agricultural University, Nanjing 210031, China
Yongnian Zhang: Engineering College, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 6, 1-13

Abstract: Aiming at the problems of dense distribution, similar color and easy occlusion of famous and excellent tea tender leaves, an improved YOLOv7 (you only look once v7) model based on attention mechanism was proposed in this paper. The attention mechanism modules were added to the front and back positions of the enhanced feature extraction network (FPN), and the detection effects of YOLOv7+SE network, YOLOv7+ECA network, YOLOv7+CBAM network and YOLOv7+CA network were compared. It was found that the YOLOv7+CBAM Block model had the highest recognition accuracy with an accuracy of 93.71% and a recall rate of 89.23%. It was found that the model had the advantages of high accuracy and missing rate in small target detection, multi-target detection, occluded target detection and densely distributed target detection. Moreover, the model had good real-time performance and had a good application prospect in intelligent management and automatic harvesting of famous and excellent tea.

Keywords: famous and excellent green tea; bud detection; improved YOLOv7 algorithm; attention mechanics (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/6/1190/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/6/1190/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:6:p:1190-:d:1163162

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().