Study on the Detection of Defoliation Effect of an Improved YOLOv5x Cotton

Xingwang Wang, Xufeng Wang, Can Hu (), Fei Dai, Jianfei Xing, Enyuan Wang, Zhenhao Du, Long Wang and Wensong Guo
Additional contact information
Xingwang Wang: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Xufeng Wang: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Can Hu: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Fei Dai: Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alar 843300, China
Jianfei Xing: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Enyuan Wang: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Zhenhao Du: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Long Wang: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China
Wensong Guo: College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China

Agriculture, 2022, vol. 12, issue 10, 1-15

Abstract: In order to study the detection effect of cotton boll opening after spraying defoliant, and to solve the problem of low efficiency of traditional manual detection methods for the use effect of cotton defoliant, this study proposed a cotton detection method improved YOLOv5x+ algorithm. Convolution Attention Module (CBAM) was embedded after Conv to enhance the network’s feature extraction ability, suppress background information interference, and enable the network to focus better on cotton targets in the detection process. At the same time, the depth separable convolution (DWConv) was used to replace the ordinary convolution (Conv) in the YOLOv5x model, reducing the convolution kernel parameters in the algorithm, reducing the amount of calculation, and improving the detection speed of the algorithm. Finally, the detection layer was added to make the algorithm have higher accuracy in detecting small size cotton. The test results show that the accuracy rate P (%), recall rate R (%), and mAP value (%) of the improved algorithm reach 90.95, 89.16, and 78.47 respectively, which are 8.58, 8.84, and 5.15 higher than YOLOv5x algorithm respectively, and the convergence speed is faster, the error is smaller, and the resolution of cotton background and small target cotton is improved, which can meet the detection of cotton boll opening effect after spraying defoliant.

Keywords: defoliating agents; cotton detection; deep learning; defoliation effect (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: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/10/1583/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/10/1583/ (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:12:y:2022:i:10:p:1583-:d:930951

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 ().