[Cyverse.Nappn] [webinar] Ali Moghimi Sep 24 8:30am PDT (UTC -7): Artificial intelligence and hyperspectral imaging for high-throughput plant phenotyping

[Amy Tabb]

** Apologies for duplicate postings **

Ali Moghimi from the University of California, Davis will present a 
webinar  "Artificial intelligence and hyperspectral imaging for 
high-throughput plant phenotyping" on Tuesday, Tuesday, September 24, 
2019, at 8:30 am PDT (UTC -7) as part of the IEEE RAS Technical 
Committee on Agricultural Robotics and Automation's (AgRA) webinar series.

Please register for this webinar here using Zoom: 
https://zoom.us/webinar/register/5915672879968/WN_W7ACPFscTxubzslZGs1nrg


This webinar will also be broadcast with YouTube Live, which is an 
option from Zoom (registration not necessary if you are joining via 
YouTube Live).

Commenting and asking questions is still possible via YouTube Live.  The 
link is not available beforehand, so check ~15 minutes before the 
webinar on Twitter, where I will post it publicly: 
https://twitter.com/AgRoboticsTC

All details, including a time zone converter, technical details, and 
presenter biography available here: 
http://ieeeagra.com/events/webinar-september-24-2019/

Title: Artificial intelligence and hyperspectral imaging for 
high-throughput plant phenotyping.

Abstract: Artificial intelligence (AI) is becoming an increasingly 
imperative tool for sustainable crop production in the era of digital 
agriculture. In this talk, I present my Ph.D. research work in which I 
utilized AI to leverage the unique advantages of hyperspectral imaging 
for investigating desired phenotyping traits in wheat with both indoor 
and field setups.

For our indoor setup, we developed a sensor-based framework for analysis 
of hyperspectral images to assess the difference between the salt 
tolerance of four wheat lines. We were able to attain a quantitative 
ranking as early as one day after applying salt treatment. In addition, 
we developed an ensemble feature selection pipeline to identify the most 
informative spectral bands associated with the desired trait in plant 
phenotyping. I present the results of testing the developed feature 
selection pipeline in finding the most prominent bands for salt stress 
assessment and Fusarium head blight detection in wheat.

For our field setup, we mounted the hyperspectral camera on an unmanned 
aerial vehicle to collect aerial imagery in two consecutive growing 
seasons from three experimental yield fields composed of hundreds of 
experimental wheat lines. We trained a deep neural network with fully 
connected layers for yield prediction. While conventional harvesting of 
plots for yield measurement relies on demanding, extremely laborious, 
and time-consuming tasks, our automated framework could predict the 
yield of wheat plots in a fast, cost-effective manner. In addition, our 
framework offers a unique insight for breeders to investigate the yield 
variation at sub-plot scale - a valuable new index in breeding programs 
to nominate high-yielding cultivars that are capable of producing a 
uniform yield across the plot. The results revealed that the proposed 
framework can also serve as a valuable tool for remote visual inspection 
of the plots and optimizing the plot size to investigate more lines in a 
dedicated field each year.

Our webinars are free and not limited to individuals on mailing lists - 
feel free to forward.

-- 

Amy Tabb, Ph.D.
amytabb at ieee.org

This email address is used in my role as:
a co-chair of the IEEE RAS technical committee on Agricultural Robotics and Automation
http://ieeeagra.com/
Twitter: @AgRoboticsTC
Joining AgRA: http://ieeeagra.com/join/

she/her
preferred address: Amy
if you really want to use a title and family name: Dr. Tabb

usually at UTC-4 / EDT (New York) time zone