YOLO-MSPM: A Precise and Lightweight Cotton Verticillium Wilt Detection Network

Xinbo Zhao, Jianan Chi, Fei Wang, Xuan Li, Xingcan Yuwen, Tong Li, Yi Shi () and Liujun Xiao ()
Additional contact information
Xinbo Zhao: Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alar 843300, China
Jianan Chi: Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, Alar 843300, China
Fei Wang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Xuan Li: College of Information Engineering, Tarim University, Alar 843300, China
Xingcan Yuwen: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Tong Li: College of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471000, China
Yi Shi: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
Liujun Xiao: College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China

Agriculture, 2025, vol. 15, issue 19, 1-18

Abstract: Cotton is one of the world’s most important economic crops, and its yield and quality have a significant impact on the agricultural economy. However, Verticillium wilt of cotton, as a widely spread disease, severely affects the growth and yield of cotton. Due to the typically small and densely distributed characteristics of this disease, its identification poses considerable challenges. In this study, we introduce YOLO-MSPM, a lightweight and accurate detection framework, designed on the YOLOv11 architecture to efficiently identify cotton Verticillium wilt. In order to achieve a lightweight model, MobileNetV4 is introduced into the backbone network. Moreover, a single-head self-attention (SHSA) mechanism is integrated into the C2PSA block, allowing the network to emphasize critical areas of the feature maps and thus enhance its ability to represent features effectively. Furthermore, the PC3k2 module combines pinwheel-shaped convolution (PConv) with C3k2, and the mobile inverted bottleneck convolution (MBConv) module is incorporated into the detection head of YOLOv11. Such adjustments improve multi-scale information integration, enhance small-target recognition, and effectively reduce computation costs. According to the evaluation, YOLO-MSPM achieves precision (0.933), recall (0.920), mAP50 (0.970), and mAP50-95 (0.797), each exceeding the corresponding performance of YOLOv11n. In terms of model lightweighting, the YOLO-MSPM model has 1.773 M parameters, which is a 31.332% reduction compared to YOLOv11n. Its GFLOPs and model size are 5.4 and 4.0 MB, respectively, representing reductions of 14.286% and 27.273%. The study delivers a lightweight yet accurate solution to support the identification and monitoring of cotton Verticillium wilt in environments with limited resources.

Keywords: cotton; YOLOv11; MobileNetV4; disease detection; small objects; deep learning (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/19/2013/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/19/2013/ (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:15:y:2025:i:19:p:2013-:d:1758770

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 ().