Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation

Ranbing Yang, Yuming Zhai, Jian Zhang (), Huan Zhang, Guangbo Tian, Jian Zhang, Peichen Huang and Lin Li
Additional contact information
Ranbing Yang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Yuming Zhai: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Jian Zhang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Huan Zhang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Guangbo Tian: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Jian Zhang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Peichen Huang: College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Lin Li: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China

Agriculture, 2022, vol. 12, issue 9, 1-17

Abstract: Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method.

Keywords: crop row detection; potato; semantic segmentation; feature midpoint adaptation (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 (4)

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/9/1363/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/9/1363/ (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:9:p:1363-:d:904304

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 ().