A Lightweight Cotton Verticillium Wilt Hazard Level Real-Time Assessment System Based on an Improved YOLOv10n Model

Juan Liao, Xinying He, Yexiong Liang, Hui Wang, Haoqiu Zeng, Xiwen Luo, Xiaomin Li, Lei Zhang, He Xing () and Ying Zang ()
Additional contact information
Juan Liao: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xinying He: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yexiong Liang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Hui Wang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Haoqiu Zeng: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xiwen Luo: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xiaomin Li: College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Lei Zhang: College of Agriculture, South China Agricultural University, Guangzhou 510642, China
He Xing: School of Information Technology & Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
Ying Zang: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 9, 1-22

Abstract: Compared to traditional manual methods for assessing the cotton verticillium wilt (CVW) hazard level, utilizing deep learning models for foliage segmentation can significantly improve the evaluation accuracy. However, instance segmentation methods for images with complex backgrounds often suffer from low accuracy and delayed segmentation. To address this issue, an improved model, YOLO-VW, with high accuracy, high efficiency, and a light weight, was proposed for CVW hazard level assessment based on the YOLOv10n model. (1) It replaced conventional convolutions with the lightweight GhostConv, reducing the computational time. (2) The STC module based on the Swin Transformer enhanced the expression of foliage and disease spot boundary features, further reducing the model size. (3) It integrated a squeeze-and-excitation (SE) attention mechanism to suppress irrelevant background information. (4) It employed the stochastic gradient descent (SGD) optimizer to enhance the performance and shorten the detection time. The improved CVW severity assessment model was then deployed on a server, and a real-time detection application (APP) for CVW severity assessment was developed based on this model. The results indicated the following. (1) The YOLO-VW model achieved a mean average precision (mAP) of 89.2% and a frame per second (FPS) rate of 157.98 f/s in assessing CVW, representing improvements of 2.4% and 21.37 f/s over the original model, respectively. (2) The YOLO-VW model’s parameters and floating point operations per second (FLOPs) were 1.59 M and 7.8 G, respectively, compressed by 44% and 33.9% compared to the original YOLOv10n model. (3) After deploying the YOLO-VW model on a smartphone, the processing time for each image was 2.42 s, and the evaluation accuracy under various environmental conditions reached 85.5%, representing a 15% improvement compared to the original YOLOv10n model. Based on these findings, YOLO-VW meets the requirements for real-time detection, offering greater robustness, efficiency, and portability in practical applications. This model provides technical support for controlling CVW and developing cotton varieties resistant to verticillium wilt.

Keywords: cotton verticillium wilt detection; hazard level assessment; lightweight network; improved YOLOv10 model (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/9/1617/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/9/1617/ (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:14:y:2024:i:9:p:1617-:d:1478573

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 ().