Detection of Wheat Lodging by Binocular Cameras during Harvesting Operation

Jingqian Wen, Yanxin Yin (), Yawei Zhang, Zhenglin Pan and Yindong Fan
Additional contact information
Jingqian Wen: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Yanxin Yin: Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Yawei Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Zhenglin Pan: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Yindong Fan: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Agriculture, 2022, vol. 13, issue 1, 1-14

Abstract: Wheat lodging provides important reference information for self-adaptive header control of a combine harvester. Aimed at real-time detection of wheat lodging, this paper proposed a detection method of wheat lodging location and area based on binocular vision. In this method, the angle relationship between the stem and vertical direction when wheat is upright, inclined, and lodging was determined by mechanical analysis. The discrimination condition of the wheat lodging degree was proposed based on the height of the visual point cloud on the surface of wheat crops. The binocular camera was used to obtain the image parallax of wheat within the harvesting region. The binocular camera optical axis parallel model was used to calculate the three-dimensional coordinate of wheat. Then, the height of the wheat stem was obtained by further analysis and calculation. According to the wheat stem height detected by vision, the location and area of wheat lodging within the combine harvester’s harvesting region were analyzed. A field experiment showed that the detection error of the wheat stem height was 5.5 cm and the algorithm speed was under 2000 milliseconds, which enabled the analysis and calculation of the wheat lodging location, contour, and area within the combine harvester’s harvesting region. This study provides key information for adaptive header control of combine harvesters.

Keywords: binocular vision; point cloud; three-dimensional reconstruction; wheat lodging; combine harvester; SGBM algorithm (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 references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/1/120/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/1/120/ (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:2022:i:1:p:120-:d:1021861

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 ().