An international computer science competition to count wheat ears more effectively, using image analysis
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. One example of this is the
manual counting of wheat ears from digital images – a long and tedious
job. Factors that make it difficult to manually count wheat ears from
digital images include the possibility of overlapping ears, variations
in appearance according to maturity and genotype, the presence or
absence of barbs, head orientation and even wind.
There is the 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 the curious re invited to join forces with us to solve
- The competition will run on the Kaggle platform from 4th May to 4th August 2020.
- International consortium, Global Wheat Dataset, has made more than 190,000 wheat ears available for this competition. Participants are invited to submit software models, based on this dataset, for counting wheat ears effectively.
The first prize is 8,000 USD, second is 4,000 USD and third 3000 USD, sponsored by the Global Institute for Food Security at the University of Saskatchewan, Canada.
For full details on the competition and on how to participate, visit www.kaggle.com/c/global-wheat-detection on the 4th of May !