November 4, 2020

Smart food production and machine learning

In the framework of the series of webinars organised by theTraceability and Big Data S3 Partnership, a webinar on “Smart food productionand machine learning” was held in October, with over 40 participants from different countries of the EU. The speakers represented the partner region of Satakunta, in Finland.

Tiina Palonen, from the regional council, presented the main features of Satakunta, and introduced the first speaker, Teija Kirkkala, from the Pyhäjärvi Institute, who shared the main needs regarding traceability and big data in the regional agrifood value chain. She presented as well the circular model in which food production is operating in the region; and the regional research needs, which regard consumer demands, resources efficiency, and the security of supply as the main ones.

The second speaker, Petri Linna, from the Tampere University, made a presentation on field conditions challenges related to open data. He presented a project which is currently dealing with the identification of problems of fields in the region, mainly in topics related with water management, lack or organic matter, soil variations, soil compaction, or the nutrition level. He explained the process of identification of problems via open data in concrete areas of the region, via aerieal photographs and the use of Sentinel together with historical orthopictures.

Nathaniel Narra, also from the Tampere University, was the last speaker in the session. Narra explained the use of modern methods of machine learning for applications in open field farming in the region. Machine learning in agriculture uses methods to make correlations from the data obtained by the sensors in different areas (plants, animals, land…). In concrete, he presented, as an example, the numerous contributions that machine learning can make in the field of plants (crop classification, disease detection, fruit counting oryields predictions…). Finally, he concluded by saying that the next big wave in agritech will be better in supporting decision-making processes in relation with a variety of issues, mainly resource allocation based upon field performance; fertilizer applications; detection of pests and disease pressure; guidingirrigation decisions; forecasting field-level yields; or zones’ management.

 

The webinar video