Challenge

There are several vineyard monitoring solutions currently in the market, but winemakers are not investing in such solutions because their return on investment is unclear. On the other hand, winemakers are increasingly interested in the digital marketing of their products.

StoryWine aims to provide an innovative platform for precision viticulture that also works as a marketing tool for winemakers, increasing their return on investment in smart sensing solutions. Through StoryWine, winemakers will be able to transmit their passion for winemaking to wine lovers! Our ambition is to move beyond the emerging Internet of Wines wave, dierentiating our oer by fitting the emerging needs of the winemaking
sector, which is increasingly investing in establishing new ways of wine promotion.

Diatomic Support

The dierent experts from DIATOMIC provided us with valuable advice on both technical and market-related issues. Access to the dierent competencies oered by DIATOMIC helped us better understand the needs for our MVP, as well as define our go-to-market strategy. The access to training sessions and webinars increased our know-how on IoT technologies and business models in the IoT arena, helping us to better shape our experiment.

Key Results

Our experiment covered an area of around 25 hectares in 2 countries. This allowed us to learn about dierent perspectives on smart vineyard solutions in Portugal and Italy, and to compare the needs of small and large producers. We engaged 24 winemakers and 105 wine lovers. Reaching out to so many interested producers and consumers allowed us to fine-tune our concept in the best way possible and dedicate our eorts on developing the
proper features. StoryWine tested 2 dierent providers on a total of 8 monitoring stations.

This allowed us to understand in more detail the challenges in the integration of devices for monitoring crops, as well as to fine tune our business model of w.r.t. device provisioning. We have logged more than 30,000 records from the sensors since April 2019. In combination with other data sources, these records allowed us to train and test our algorithms for
irrigation forecast and pest prevention.

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