Agroknow tell us about their aspirations for Data Pitch and beyond.

The global population is estimated to increase to nearly 10 billion by 2050, and with some estimating that we will need to increase crop production by over 50%, the question is now how can we ensure that we feed the world sustainably? It is absolutely vital that farmers are supported to become as efficient as possible.

Greek startup Agroknow specialise in agritech solutions that empower farmers to save time and money, increasing productivity. We spoke to the Head of Projects, Panagiotis Zervas, about their plans for Data Pitch.

What do you hope to achieve on the Data Pitch accelerator?

Currently, getting different agricultural certifications to prove the quality of a product is a demanding and time-intensive process for farmers. While most certification schemes are based on the same type of data, the process for applying for each scheme varies, meaning the farmer has to collect the data multiple times to get different certifications.

While on the  Data Pitch accelerator, we plan to develop a Minimum Viable Product (MVP) to support both farmers and buyers. Our web application will allow farmers to collect their data just once, and then use it to apply for different agriculture certifications. Buyers will be able to search for products with certain certifications, and ‘data snapshots’ will help them to understand the different parameters of each certification scheme.

In addition, we’re planning to develop a data brokerage service that will allow farmers to share their certification-relevant data with existing or potential buyers in an open and trustworthy manner.

What shared data will you work with and how will you use it?

We are working with datasets provided by GROW Observatory as part of the Sustainable Food Supply Chain Challenge (creating farm-to-market linkages). These datasets include high-resolution, citizen-generated time series soil moisture data from across Europe, satellite imaging, data from IoT installations of farmers and survey data on land cover, land use and soil condition.

We will use these datasets to help us train our algorithms, which will be at the core of our product. We can also use the datasets to produce a data normalisation filter, which will be developed in order to detect and normalise outliers within the farm data.

Why do you think it is important for startups to work with large scale data providers?

It’s important for startups because we need access to trusted data sources to validate our emerging ideas, and test their feasibility alongside potential stakeholders.

What’s the best thing about working with data?

The agriculture and food sector is fragmented and has different data sources, which makes it quite challenging to create meaningful services. Having said that, the potential to add value to the different stages across the supply chain by solving this challenge, is the best thing about working with data!

If you could change one thing about the data ecosystem what would it be?

We would like to see more initiatives that promote sharing data in a secure and trustworthy manner.