Mr. Hanlin Wang Profile

Hanlin Wang

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 October 2023 Paper

Area localization of leaf diseases under complex environments

Hanlin Wang, Keming Zhang, Yan Zou, Fenmei Fenme, Chenrui Kang, Hongbo Chen, Qingshan Xu

Proceedings Volume 12799, 1279923 (2023) https://doi.org/10.1117/12.3006213

KEYWORDS: Diseases and disorders, Object detection, Image segmentation, Agriculture, Feature extraction, Target detection, Computer vision technology, Spatial resolution, Reliability, Performance modeling

Read Abstract +

Regional location of plant diseases helps to develop targeted prevention and control strategies to improve crop production efficiency and reduce agricultural losses. Recently, some important advances have been made in computer vision-based methods. However, most of the current mainstream methods are based on target detection or rotation detection, which always include environmental noise in the results of locating disease areas. In this paper, we revisit the task from the perspective of segmentation tasks and propose a feature reinforcement module. Specifically, we effectively focus on the shortcomings of previous methods where neighbourhood features cannot interact by looking at the shortcomings of the Feature Pyramid Network (FPN) in the feature extraction process through multi-scale features repetitive sampling. In addition, we compared the dataset annotation methods under different annotations such as target detection, rotation detection, and segmentation, and the visualisation well demonstrates the need to use segmentation methods. In the final experimental results, our method is proved to improve mAP by 1.3% and mIOU by 1% over state-of-the-art methods. A large number of methods have demonstrated the superiority and reliability of our method.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years