Canada, University of Waterloo
With the rise of Machine Learning and Image Recognition technologies, the market has seen useful agricultural tools such as Banna Freshness Analyzer, a Cucumber Sorter and so on. However, there lacks a tool for egg farmers and consumers for smart egg checking. Therefore, Hach aims to: • reduce the skills requirement for consumers to shop egg by providing a easy way to identify the development status of the egg. • Reduce the time for homestead farmers who raise egg themselves. By: • Use Image Recognition Service provided by Azure to determine the development of an egg • Use Web & Mobile service to store data on the cloud such that the egg stats is readily avaliable whenever the user need them. Sourcecode: https://github.com/hachyEgg/hachy-android app download: https://play.google.com/store/apps/details?id=ms.imagine.foodiemate web app: hachy.azurewebsites.net facebooklogin: email@example.com Password: hachEgg
Team Hachy is composed of Eugene Wang as the sole developer from the university of Waterloo. However, he is not alone. During the project, he has recieved help from numerous talented egg embryology students, UI experts, and machine learning enthusiasts. The eventual vision is to build an autonomous egg incubating tool that allows no human involvement in the incubating, candling and hatching of eggs. Hachy is considered as the first stage that enables autonomous egg development identification and tracking using Machine Learning.