Mr. Aaron Etienne Profile

Aaron Etienne

at Purdue Univ

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 | 14 May 2019 Presentation + Paper

Machine learning approaches to automate weed detection by UAV based sensors

Aaron Etienne, Dharmendra Saraswat

Proceedings Volume 11008, 110080R (2019) https://doi.org/10.1117/12.2520536

KEYWORDS: Sensors, RGB color model, Unmanned aerial vehicles, Agriculture, Machine learning, Image processing, Image segmentation, Multispectral imaging, Neural networks, Calibration

Read Abstract +

Applying machine learning methods and analysis on remotely sensed color, multispectral, and thermal imagery has been recognized as a potentially cost-effective approach for detecting the location of various weed species in-field. This detection approach has the potential to be an important first step for broader Site-Specific Weed Management procedures (SSWM). The objective of this research was to create a method for automating the detection of weeds in corn and soybean fields, at different stages of the growing season. Sensors based on an unmanned aerial vehicle were used to capture imagery used for this research. We focused on identifying four common weed types present in Midwestern fields. This research involved: 1) collecting color, multispectral, and thermal imagery from UAV based sensors in corn and soybean fields throughout the 2018 growing season, 2) creating individual normalized differential vegetation index (NDVI) images from the near-infrared (NIR) and red multispectral bands 3) applying image thresholding and smoothing techniques on the NDVI imagery , 4) manually drawing bounding boxes and hand labelling vegetation blobs from the processed imagery using color images as the ground truth, 5) developing a training set of these processed, labeled images that represent weeds at different crop growth stages. Preliminary results of these methods show promise in creating an affordable first step to target herbicide application.

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