An international computer science competition to count wheat ears more effectively, using image analysis

 

The Problem 

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. 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.  
 

The Need 

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 Competition 

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 this challenge! 
 

Details: 

Prize 

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 !