MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field

Qiangli Yang, Yong Ye (), Lichuan Gu and Yuting Wu
Additional contact information
Qiangli Yang: School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
Yong Ye: School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
Lichuan Gu: School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
Yuting Wu: School of Information and Computer, Anhui Agricultural University, Hefei 230036, China

Agriculture, 2023, vol. 13, issue 6, 1-20

Abstract: Weed control has always been one of the most important issues in agriculture. The research based on deep learning methods for weed identification and segmentation in the field provides necessary conditions for intelligent point-to-point spraying and intelligent weeding. However, due to limited and difficult-to-obtain agricultural weed datasets, complex changes in field lighting intensity, mutual occlusion between crops and weeds, and uneven size and quantity of crops and weeds, the existing weed segmentation methods are unable to perform effectively. In order to address these issues in weed segmentation, this study proposes a multi-scale convolutional attention network for crop and weed segmentation. In this work, we designed a multi-scale feature convolutional attention network for segmenting crops and weeds in the field called MSFCA-Net using various sizes of strip convolutions. A hybrid loss designed based on the Dice loss and focal loss is used to enhance the model’s sensitivity towards different classes and improve the model’s ability to learn from hard samples, thereby enhancing the segmentation performance of crops and weeds. The proposed method is trained and tested on soybean, sugar beet, carrot, and rice weed datasets. Comparisons with popular semantic segmentation methods show that the proposed MSFCA-Net has higher mean intersection over union (MIoU) on these datasets, with values of 92.64%, 89.58%, 79.34%, and 78.12%, respectively. The results show that under the same experimental conditions and parameter configurations, the proposed method outperforms other methods and has strong robustness and generalization ability.

Keywords: semantic segmentation; weed segmentation; agricultural weed dataset; convolutional attention (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:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/6/1176/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/6/1176/ (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:1176-:d:1160933

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 ().