on 13 Dec 2019 12:00 AM

Oliveex is one of the 6 (six) companies that participated in the AGINFRAplus Data Science Challenge ( It is a company based in Greece that has started its business activity during the last three (3) years.

Oliveex applies IoT technologies in food storage and specifically in fermented foods, such as olive products, dairy products, beer, and wine. The difference in fermented foods is that the product continues to grow during storage. Their system ensures the best storage conditions with real time remote monitoring and daily suggestions for the actions that must be done for an optimal storage process. Using state-of-the art sensing devices they can guarantee the best product quality and minimize the profit losses due to spoilage.

The decisions about quality control on fermented foods are taken from a sampling process with questionable precision that requires many working hours and investment in analyzing equipment. The suboptimal precision leads to product spoilage of up to 20%, food waste, and significant revenue losses. The reason for that it’s because the current technology isn’t available to these industries yet. All the sampling data are used only on-site for the necessary checkings without saving them for future use.

The solution that they offer is an IoT-enabled sensing device that is 24/7 attached at the storage tanks, collecting product data. Data is pushed to our cloud service in real-time. There they store and analyze them, define the fermentation stage, make the right predictions for possible production threats and suggest corrective actions that must be taken to ensure the best possible product quality and avoid spoilage. For the first time ever, data from all production stages can be saved and analyzed to help producers validate their quality control, production methods and also improve product traceability. Based on data analytics, different production methods can be compared using reports about each method results in product quality. Also, analytics are available for the separate stages of each production method including suggestions on optimizing them and their critical points, those who can affect the quality of the products. This is their way of improving food quality and reducing food waste by using valuable data that has not been used so far.

Oliveex, in order to power its innovation, executes some computationally demanding software algorithms and uses public or commercial cloud services to store and manage its data.

You can visit Oliveex website here.

As was the case with all six companies that participated in the competition, it was decided that Oliveex receives the AGINFRAplus Food Industry Lookout  distinction.

This means that this company will be announced as one of the AGINFRAplus Lookouts and will be promoted through AGINFRAplus extensive international network. A digital badge will also be provided to Oliveex, which they may use to list this distinction at their web site, pitch deck or other promotional material.




The selected AGINFRA+ Use Cases will illustrate the benefits of applying the Science as a Service approach to pressing research questions from the corresponding research communities.


This community focuses on use cases aim to support the workflow of researchers, intermediaries and business analysts working on crop modelling, crop phenology estimation and yield forecasting, as well as related activities in the area of policy and decision support in food security, farm management advice and related activities.



This community focuses on use cases to support scientists in the multidisciplinary field of risk assessment and emerging risk identification as there is currently a strong need to create new technology-supported solutions that facilitate the knowledge integration processes relevant for these tasks.



This community focuses on use cases related to the high-throughput phenotyping large amount of data which need to be analyzed immediately for decision making. This aims to support phenomics researchers to select plant species and varieties which are the most adapted to specific environments and to global changes.


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