Join the agtech community set to improve wheat cultivar breeding selection – the plant phenotyping Empire needs you!
In the last round of online knowledge sharing sessions we organised for you during the April 2020 global lockdown period, we insisted on 3 key messages:
1: Hiphen is an expert in turning the latest scientific advancements in operational solutions for our clients. This is the reason why we care deeply about introducing bullet proof solutions that can work across different field conditions. We do not want to change the data processing engine everytime we jump on a different journey;
2: We care about being transparent with you so that you understand the methodology we use and you can engage with it to customize it to your needs. With all the knowledge shared last April, we do hope that this message came across – please please please, work with a plant phenotyping partner from whom you understand the methodology, do not opt for a ‘black box’ approach and feeling, you will not far with that;
3: We care about being agile to allow you to scale. We believe that there is little vamue in investing in a method that will only work on a specific domain and that you cannot deploy to your regional, national or global footprint.
With the Global Wheat Challenge, here is a chance to develop an AI-powered solution that can deliver on all three missions. You can replay our webinar on this topic HERE to get more detailed information.
The Global Wheat Challenge is an international computer science competition to count wheat ears more effectively, using AI-powered image analysis. All the details about this kaggle competition can be found HERE. The competition will run from May 4th to August 4th 2020 and is made possible thanks to the collaboration of an International consortium of research institutions that compiled over 190 000 annotated images of wheat heads across 3 continents. A cash prize of 15 000 Dollars awaits the data science teams that will develop the best AI-powered wheat heads counting model.
Global WHEAT Dataset is the first large-scale dataset for wheat head detection from field optical images. It includes a very large range of cultivars from differents continents. Wheat is a staple crop grown all over the world and, consequently, interest in wheat phenotyping spans across the globe. Therefore, it is important that AI-powered models developed for wheat phenotyping, such as wheat head detection networks, can be applied across different growing environments around the world.
Remember, the plant phenotyping Empire needs you!