Dr. Jack Brown Profile

Dr. Jack Brown

at Univ. of Idaho

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 | 21 May 2018 Paper

Detection of canola flowering using proximal and aerial remote sensing techniques

Chongyuan Zhang, Wilson Craine, James Davis, Lav Khot, Afef Marzougui, Jack Brown, Scott Hulbert, Sindhuja Sankaran

Proceedings Volume 10664, 1066409 (2018) https://doi.org/10.1117/12.2304054

KEYWORDS: Visualization, RGB color model, Remote sensing, Data acquisition, Image processing, Agriculture, Unmanned aerial vehicles, Feature extraction, Cameras, Image analysis

Read Abstract +

In plant breeding, the time and length of flowering are important phenotypes that determine the seed yield potential in plants. Currently, flowering traits are visually assessed, which can be time-consuming, less accurate and subjective. To address this challenge, in this study, proximal and remote sensing with an unmanned aerial vehicle (UAV) were applied to monitor the canola flowers in a breeding trial with 35 varieties. Visible digital images (RGB) acquired were processed to extract the flowering features. The results indicated that flowering features extracted from both proximal and aerial images were significantly and positively correlated (P < 0.0001) with each other and with visual ratings. In general, aerial imaging overestimated canola flowering rates, which could be resulting from lower resolution at measured altitude (30 m), and rendered lower correlation coefficients (r = 0.53 – 0.62) with visual ratings. Proximal sensing resulted in better estimation of canola flowering with r ranging from 0.65 to 0.91. This study indicated that remote sensing can be used for high-throughput phenotyping of canola flowers with confidence. High-throughput phenotyping techniques will potentially improve the throughput and objectivity of detecting flowers in canola and other crops, and contribute to the development of new cultivars in breeding programs and yield estimation in precision agriculture.

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