Challenge close                       : Mid-March 2020
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Artificial intelligence has invaded the agriculture field during the last few years. From automatic crop monitoring via drones, smart agricultural equipment, food security and camera-powered apps assisting farmers to satellite imagery based global crop disease prediction and tracking, computer vision has been a ubiquitous tool. This workshop aims to expose the fascinating progress and unsolved problems of computational agriculture to the AI research community. It is jointly organized by AI and computational agriculture researchers and has the support of CGIAR, a global partnership that unites international organizations engaged in agricultural research for a food-secure future.
Computer Vision for Agriculture (CV4A) is the second workshop of the Computer Vision for Global Challenges initiative and will focus on agriculture. It will be held in April 2020, in conjunction with the International Conference on Representation Learning (ICLR), in Addis Ababa, Ethiopia. It will be a full-day event and will feature invited speakers, poster and spotlight presentations, a panel discussion and (tentatively) a mentoring/networking dinner.

CHALLENGES AND COMPETITIONS
CV4A will feature two open challenges, both hosted on the African platform Zindi:
* The CGIAR Wheat Rust Detection Challenge
* The Radiant Earth Foundation Crop Classification using Earth Observations Challenge
Both challenges will feature cash prizes and travel grants for the top performing submission, as well as the top performing submission from an African researcher and the top performing submission from a female-identified African researcher. More details coming soon!

CALL FOR PAPERS
We invite researchers to submit their recent work on Computer Vision applications, tasks and challenges inspired by and applied to agriculture, with a special focus on developing regions. Topic include but are not limited to:

 – Crop health (pests, diseases, plant nutrient deficiencies) and crop yield estimation.
 – Crop type recognition from ground and/or satellite imagery. Such data can be used to target interven- tions, assess risk, evaluate the impact of programs.
 – Artificial intelligence for bottom-up, farmer-led crop improvement.
 – Hyper-spectral (e.g. NIR, MIR, x-ray fluorescence) imaging for detecting early-stage or physiological issues in crops (e.g. photosynthetic activity, water stress, nutrient stress).
 – Whole-field, multi-view crop diagnostics.
 – Computer vision methods for food security, index insurance, drought/flood early warning systems.
 – Multi-modal integration of data from diverse sensors.
 – Crowdsourcing agricultural data.
 – Papers will be presented as poster and oral presentations. There will be some travel support to presenters.

Researchers based in developing regions are strongly encouraged to submit

PAPER SUBMISSION
Up to four pages papers in PDF format, with unlimited pages for references. To prepare your submission to the CV4A workshop, please use the ICLR 2020 LaTex style files. The review process is double-blind.

Submissions will be handled via CMT: https://cmt3.research.microsoft.com/CV4A2020

PEOPLE
 – Workshop organizers
Yannis Kalantidis (Naver Labs Europe)
Laura Sevilla-Lara (University of Edinburgh)
Ernest Mwebaze (Google AI Ghana)
Dina Machuve (Nelson Mandela African Institution of Science and Technology)

 – Challenge Organizers
Hamed Alemohammad (Radiant Earth Foundation)
David Guerena (CIMMYT)

– Essential Collaborators
Drew Westbury (Facebook)
Celina Lee (Zindi)
Jamie Yang (Facebook)
Brian King (CGIAR Platform for Big Data in Agriculture)
Lorenzo Torresani (Facebook AI and Dartmouth)
Timnit Gebru (Google AI)
John Quinn (Makerere University & Google AI Ghana)
Larry Zitnick (University of Washington & Facebook AI)
Jitendra Malik (UC Berkeley & Facebook AI)