dots-horizontal

Agriculture-Vision Consortium Opens Call for Papers, Prize Challenge

Researchers interested in publishing materials, prize money encouraged to apply

Indianapolis – February 3, 2022 – The 3rd International Agriculture-Vision Workshop and Prize Challenge has opened its call for papers for 2022. The workshop, held annually at CVPR, features an international lineup of speakers and panelists, but also provides two visibility opportunities through a call for papers and a prize challenge. All accepted full-length papers will be published as part of the IEEE CVPR 2022 Workshop Proceedings.

 

“Paper submissions undergo a double-blind review by researchers in the field, providing top papers with the opportunity to publish and present their research via poster format or orally,” says Jennifer Hobbs, PhD, Intelinair’s Director of Machine Learning and organizer of the Agriculture-Vision Consortium for 2022. “In addition to the call for papers, we also have a two-track challenge opportunity for participants to take home prize money.”

 

Call for Papers
Paper submissions are open to a wide range of topics in the computer vision space, from pattern recognition to transfer learning and domain adaptation. Those interested in submitting full-length papers must do so by March 9, 2022. The call for short, non-proceedings papers will have a deadline in May.

 

Prize Challenge
This year’s prize challenge offers two tracks for interested participants to earn prize money:

  • Agriculture-Vision: a multi-class semantic segmentation model using high-resolution aerial imagery to identify key agronomic patterns or interest. AND,
  • CropHarvest: a global remote sensing dataset from a variety of agricultural land use datasets and remote sensing products.

 

The prize challenge is currently open and runs through the beginning of June.

 

Additional information on paper guidelines and the prize challenge can be found on agriculture-vision.com.

 

“Technology can provide useful tools to help address the world’s food problem,” says Ranveer Chandra, CTO in Agri-Food for Microsoft and member of the workshop organizing committee. “This workshop showcases innovations in Computer Vision and AI and brings together the right stakeholders who can help us create the needed breakthroughs in this space.”

 

Other members of the 2022 organizing committee include Humphrey Shi (University of Oregon, Picsart); Naira Hovakimyan (Intelinair co-founder, University of Illinois Urbana-Champaign); Hanna Kerner (University of Maryland, NASA Harvest); Melba Crawford and Edward Delp (Purdue University); and Kai Wang and Najmul Hassan (University of Oregon).

 

To learn more about Agriculture-Visio, regularly check agriculture-vision.com for updates or connect with one of the organizers on LinkedIn.

 

Download the pdf of the release here.

 

Share This Article

RELATED BLOG POSTS

Yield Forecast

Understand where your corn yield is based on the current state of the crop. As the season unfolds, see how it is having an impact on your final yield.

Yield Loss

Powered by years of Nitrogen research at the University of Missouri, our corn Yield Loss analytic, powered by NVision Ag, gives insight into potential yield loss due to Nitrogen deficiency. Optional analytic for nitrogen management.

Variable Dry Down

variable dry down

Understand which fields and which areas of the field are drying down to help plan your harvest logistics.

Underperforming Area

low crop health

Not all areas of your fields perform the same and low NDVI doesn’t necessarily mean there is anything you can do to fix it this year. Underperforming Area alerts you to the fields and areas of the fields that are performing below their historical potential. This will allow you to quickly find those fields and areas and make adjustments to get them back on target and protect yield.

Nutrient Deficiency

nutrient deficiency

As the crop grows, it can tell us more of what is wrong with it. This analytic finds the fields and areas of the fields where there is a nutrient deficiency so that issues can be addressed before grain fill.

Disease Stress

disease risk

In conjunction with the Thermal Stress, Disease Stress alert takes into account weather information to more precisely indicate the type of stress impacting the crop.

Thermal Stress

thermal risk

Using our thermal imagery, AGMRI can detect elevated heat patterns of the crop that indicate crop stress.

Crop Health

Crop Health

Get a complete view of your farms and fields and identify where yield potential is ranked highest to the lowest.

Weed Map & Weed Escape

weed escape

Know what fields and areas of the fields have weeds. With machine integration or based on planting date, be alerted to what fields have weeds that may be impacting yield.

Historical Field Performance

AGMRI creates 5 performance zones in each field based on the historical average of those zones. This data is used to compare the current season to help understand where you are underperforming from the zone potential.

Low Emergence

low emergence

Notification of what fields and areas of the field have poor emergence.

Stand Assessment

strand assessment

AGMRI detects the established rows and uses computer vision and machine learning to determine the best segment of row and compares the rest of the field to that segment to give you a relative map. If machine data is integrated, a stand population map is returned.