aquaSmart combines descriptive statistics and machine learning technologies that you can use to
- Explore your data
- Create independent models and use these models as a recommendation engine
- Benchmark fish performance
- Spot outliers
- Predict performance (FCR, growth, harvest result, etc.)
- Optimize harvest outcome to match customer’s needs
- Improve monitoring of new species performance and brand planning.
- Reveal how the environment, fish feeding, production management and practices affects the production
- Optimize feeding strategies
Compare your results with the results of other companies, with full respect to your sensitive corporate data. Using aquaSmart you are able to
- generate descriptive statistics on global datasets, position the performance of your company with regards to industry average and identify any gaps
- compare the findings of the machine learning analysis to similar findings of other companies (for example, which parameters have a positive or negative effect on fish growth)
No need for expensive hardware, infrastructure or IT people. Machine learning and data analytics are applications that demand powerful computers and serious infrastructure. With aquaSmart you can have all these without being concerned with the details of how it is done and without high costs. You only need a browser! aquaSmart offers you flexibility, the ability to work from anywhere and advanced security. Our platform is always up, automatically upgraded, and there are no extra IT maintenance costs.
The aquaSmart project has developed a process for standardising the use of Open Data in the Aquaculture field. It also aims to standardize the types of analytics that can be sought from Big Data platforms. Through this approach it has developed state of the art services in the aquaculture sector through access to global data integrated from heterogeneous sources. Working with CEN, the European Committee for Standardization, the aquaSmart Consortium has created a draft CEN Workshop Agreement for Big Data in Aquaculture which is in the approval process and will be available in the first quarter 2017.