Dr. Volkan Senyurek Profile

Dr. Volkan Senyurek

at Mississippi State Univ

SPIE Involvement:

Author

Publications (2)

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 | 7 June 2024 Presentation + Paper

Performance assessment of crop line detection in corn field from unmanned aerial vehicle video

Mohammad Abdus Shahid Rafi, Volkan Senyurek, Ali Gurbuz

Proceedings Volume 13053, 130530B (2024) https://doi.org/10.1117/12.3013501

KEYWORDS: Vegetation, Hough transforms, Video, Unmanned aerial vehicles, RGB color model, Image segmentation, Agriculture, Image processing, Cameras, Tunable filters

Read Abstract +

In recent times, precision agriculture, an approach that utilizes scientific and technological advancements and techniques for the enhancement of agricultural production, usually starts with the crop line detection procedure. Crop line detection helps precision agriculture with the mapping of the crop fields, which is useful for agricultural resources (water, fertilizer, pesticides, etc.) management, crop yield estimation, autonomous harvesting and irrigation management, disease and pest control, weed detection, controlled monitoring by autonomous machines and so forth. Although the aim of crop line detection in this inquiry is weed detection, which can aid the farmers regarding the optimum usage of herbicides in the field, it can be extended to any precision agriculture study. In this study, two different methods are employed for crop line detection: Hough transformation and Pixel/Frequency counting. The study was conducted on a 1.2-ha corn field through 2020 - 2023 that covers the crop period of corn (April ∼ August). More than 7000 high-spatial-resolution RGB images are collected using a GoPro camera attached to a custom-made unmanned aerial vehicle. Around 10% of these images are randomly selected for this analysis. RGB image frames were extracted from the video files and organized according to their weekly growth timeline. Normalized Excess Green Vegetation Index is calculated to convert them into two-level binary images. 2D Fourier transform is used to find the average crop line angle. Comparing the crop lines detected from both procedures with the actual crop lines present in the respective image frame, confusion matrix information is constructed for the performance evaluation. The average accuracy of crop line detection found for Hough transformation is 87.79%, and for Pixel counting, it is 95.71%, which can be promising choices to be employed for crop line detection.

Proceedings Article | 12 April 2021 Presentation + Paper

A UAS-based RF testbed for water utilization in agroecosystems

Mehmet Kurum, Ali Gurbuz, Spencer Barnes, Dylan Boyd, Matthew Duck, Md. Mehedi Farhad, Austin Flynt, Nathan Goyette, Preston Peranich, Mia Scheider, Volkan Senyurek

Proceedings Volume 11747, 117470J (2021) https://doi.org/10.1117/12.2591895

Read Abstract +

Agroecosystems compose large economic sectors in dominantly agriculture-based societies. Availability and management of water resources have a huge influence on the sustainability of agroecosystems. Low soil moisture is a major constraint on crop growth due to its vital role in providing crops with sufficient nutrition for root uptake. Current methodologies in precision agriculture are insufficient for direct soil moisture sensing since reflected shortwave solar radiation and infrared long-wave emission can only provide information about surface characteristics. While microwave signals are known to be highly sensitive to water within plants and soil, its implementation from small Unmanned Aircraft Systems (UAS) platforms are at relatively low technological readiness level compared to the use of shortwave / longwave optical sensors. In this paper, we summarize our efforts to apply radio frequency (RF) / microwave remote sensing from UAS for water utilization in agroecosystems. Recently, we developed a comprehensive UAS-based RF testbed, including a microwave radiometer, a scatterometer, wideband ground penetrating radar system as well as Signals of Opportunity (SoOp) receivers. These instruments operate from UAS platforms and use the microwave / radio wave portions of the spectrum. The testbed is accompanied with proximal sensing via autonomous unmanned ground vehicles that acquire in- situ soil moisture and vegetation geophysical parameters to provide appropriate datasets for training and testing physics aware, machine learning-based models. In this paper, we introduce the RF sensing framework that can enable non-intrusive high-resolution soil moisture estimates at multiple depths of soil via UAS-based active / passive / SoOp RF instruments.

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