An international computer science competition to count wheat ears more effectively, using image analysis
For several years, agricultural research has been using sensors to observe plants at key moments in their development. However, some important plant traits are still measured manually. Wheat ears are counted manually from digital images. It is a long and tedious process. Digital detection of wheat ears is challenging. The following factors contribute to the complexity.
Variations in appearance based on genotype and maturity
Presence or absence of barbs
There is a need for a robust and accurate computer model that is capable of counting wheat ears from digital images. This model will benefit phenotyping research and help producers around the world assess ear density, health and maturity more effectively. Some work has already been done in deep learning, though it has resulted in too little data to have a generic model.
The Global Wheat Head Challenge, an international data science competition, was created to address this need. The objective is to have a software model capable of locating ears on a wide variety of data, without bias. Data Scientists, hackers, scientists and researchers are invited to join forces with us to solve this challenge!
The competition will run on the AIcrowd’s platform from 4th May to 4th July 2021.
An international consortium, Global Wheat Dataset, has made more than 300,000 wheat ears on 6500 images available for this competition. Participants are invited to submit software models based on this dataset for counting wheat ears effectively.
This challenge features a prize pool of $4000 USD.
First place – $2000 USD
Second place – $1000 USD
Third place – $1000 USD
These prizes are sponsored by the Global Institute for Food Security at the University of Saskatchewan, Canada, Kubota, Japan, Hiphen Plant, France and DigitAg, France.
For more details on the competition and on how to participate, visit the challenge page on AIcrowd.