Detection and monitoring wheat diseases using unmanned aerial vehicles (UAVs)

Joshi, Pabitra; Sandhu, Karansher S.; Singh Dhillon, Guriqbal; Chen, Jianli; Bohara, Kailash

doi:10.1016/j.compag.2024.109158

Computers and Electronics in Agriculture

2024

DOI: 10.1016/j.compag.2024.109158

|View full text |Cite

Detection and monitoring wheat diseases using unmanned aerial vehicles (UAVs)

Pabitra Joshi,

Karansher S. Sandhu,

Guriqbal Singh Dhillon

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 150 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Improved monitoring of southern corn rust using UAV-based multi-view imagery and an attention-based deep learning method

Lv,

Xu,

Zhong

et al. 2024

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Improved monitoring of southern corn rust using UAV-based multi-view imagery and an attention-based deep learning method

Lv,

Xu,

Zhong

et al. 2024

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments

Zhang,

Liu

et al. 2024

Plant Methods

View full text Add to dashboard Cite

Verticillium wilt greatly hampers Chinese cabbage growth, causing significant yield limitations. Rapid and accurate detection of Verticillium wilt in the Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) can provide significant agronomic benefits. Here, we propose a detection model, DSConv-GAN, which is based on images acquired by an unmanned aerial vehicle (UAV). Based on YOLOv8, with the addition of the dynamic snake convolution (DSConv) module and the improved loss function maximum possible distance intersection-over-union (MPDIoU), we acquired enhanced complex structures and global characteristics in Chinese cabbage images under different growth conditions. To reduce the difficulty of acquiring diseased Chinese cabbage data, a cycle-consistent generative adversarial network (CycleGAN) was used to simulate and generate images of the Verticillium wilt characteristics for multiple fields. The detection of lightly infected plants achieved precision, recall, mean average precision (mAP), and F1-score of 81.3, 86.6, 87.7, and 83.9%, respectively. DSConv-GAN outperforms other models in terms of precision, detection speed, robustness, and generalization. The model is combined with software to improve the practicability of the proposed method. Our results demonstrate DSConv-GAN to be an effective intelligent farming tool that provides early, rapid, and accurate detection of Chinese cabbage Verticillium wilt in complex growing environments.

show abstract

Applications of Computer Vision, 2nd Edition

Cernadas

2024

Electronics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Detection and monitoring wheat diseases using unmanned aerial vehicles (UAVs)

Cited by 4 publications

References 150 publications

Improved monitoring of southern corn rust using UAV-based multi-view imagery and an attention-based deep learning method

Improved monitoring of southern corn rust using UAV-based multi-view imagery and an attention-based deep learning method

DSCONV-GAN: a UAV-BASED model for Verticillium Wilt disease detection in Chinese cabbage in complex growing environments

Applications of Computer Vision, 2nd Edition

Contact Info

Product

Resources

About