EggDetect Unit, the first non-invasive technology to detect egg fertility pre-incubation and the chicken's gender pre-hatching

Project Information

EDU

Grant agreement ID: 773801

DOI

10.3030/773801

Project closed

EC signature date 12 May 2017

Start date 1 June 2017

End date 30 September 2017

Funded under

• SOCIETAL CHALLENGES - Food security, sustainable agriculture and forestry, marine, maritime and inland water research, and the bioeconomy

Total cost

€ 71 429,00

EU contribution

€ 50 000,00

Coordinated by

EGGDETECT LTD
Israel

Periodic Reporting for period 1 - EDU (EggDetect Unit, the first non-invasive technology to detect egg fertility pre-incubation and thechicken's gender pre-hatching)

Reporting period: 2017-06-01 to 2017-09-30

Summary of the context and overall objectives of the project

The EggDetect Unit seeks to optimise the incubation process for laying poultry. Currently there is no method for fertility testing when the egg is laid and gender differentiation happens only once the chicks are hatched. Male chicks are not useful either as layers or to raise as meat for consumers. The birth ratio of male to female birds is roughly 50:50. Millions of male birds are culled via maceration or suffocation annually. This is a poor use of resources and creates unnecessary costs on top of the ethical implications.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Over the course of the feasibility study EggDetect has contacted stake holders and secured sites for pilot trial, contacted interested customers who have expressed intent once results are verified. We have investigated our mvp and created a road map to future value adding features. We have also completed the design for our pilot trial.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

EggDetect's EggDetect Unit (EDU) enables non-invasive fertility detection before the eggs are incubated and can predict gender of the egg while still in-ovo with 98% accuracy. Infertile eggs are no longer incubated reducing incubator contamination and eliminating food waste. The EDU removes the need for the mass culling of day old male chicks and instead returns them to the circular economy including the need for fertilised eggs in vaccine production. Further developments include deep learning from data accumulation will help managers with process flow and breeders to see the impact of genetic variation as breeders change.

EDU Schematic