A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Mingfeng Huang, Guoqin Xu, Junyu Li and Jianping Huang
Additional contact information
Mingfeng Huang: School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Guoqin Xu: School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Junyu Li: School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Jianping Huang: School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Agriculture, 2021, vol. 11, issue 12, 1-14

Abstract: Northern leaf blight (NLB) is a serious disease in maize which leads to significant yield losses. Automatic and accurate methods of quantifying disease are crucial for disease identification and quantitative assessment of severity. Leaf images collected with natural backgrounds pose a great challenge to the segmentation of disease lesions. To address these problems, we propose an image segmentation method based on YOLACT++ with an attention module for segmenting disease lesions of maize leaves in natural conditions in order to improve the accuracy and real-time ability of lesion segmentation. The attention module is equipped on the output of the ResNet-101 backbone and the output of the FPN. The experimental results demonstrate that the proposed method improves segmentation accuracy compared with the state-of-the-art disease lesion-segmentation methods. The proposed method achieved 98.71% maize leaf lesion segmentation precision, a comprehensive evaluation index of 98.36%, and a mean Intersection over Union of 84.91%; the average processing time of a single image was about 31.5 ms. The results show that the proposed method allows for the automatic and accurate quantitative assessment of crop disease severity in natural conditions.

Keywords: plant disease; northern maize leaf blight; lesions segmentation; attention mechanism; YOLACT++; instance segmentation; convolutional neural network (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/11/12/1216/pdf (application/pdf)
https://www.mdpi.com/2077-0472/11/12/1216/ (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:11:y:2021:i:12:p:1216-:d:693636

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 ().