project - EIP-AGRI Operational Group

Automatically identify pests on rapeseed and carrot crops with image analysis
Automatiskt identifiera skadeinsekter på raps och morotsodlingar med bildanalys

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Ongoing | 2022 - 2024 Sweden
Ongoing | 2022 - 2024 Sweden
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Objectives

In this project, we will a solution using image recognition and machine learning to help farmers to identify pests, detect patterns and incidents of growth and suggest better timing and use of pesticides. By digitally documenting field situations and suggesting more accurate control and timing thresholds, we can minimize the unnecessary use of pesticides and minimize the amount of time and resources that farmers need to dedicate to manually monitoring the status in their fields. Furthermore, the data we gather across wider geographical areas, introduces the potential to support research and broader monitoring of live field conditions.

Objectives

Vi vill utveckla stöd för att med hjälp av bildigenkänning och maskininlärning (AI) identifiera skadeinsekter, dokumentera och föreslå tidpunkt för bekämpning. Genom digital övervakning av fälten kan vi samla in information om både om skadeinsekter, men och också vilka insekter som normal vistas där. Vi vill kunna bygga en plattform där jordbrukaren på ett naturligt s tt kan utnyttja nya lösningar från forskning inom AI, men också var en plattform som kan samla in data från jordbruket som kan användas för forskning och utveckling och i förlängningen leda till ett mer evidensbaserat utnyttjande av bekämpningsmedel.

Additional comments

Tegbot are experts in the use of AI to deploy solutions supporting the agriculture sector. In this project, we will a solution using image recognition and machine learning to help farmers to identify pests, detect patterns and incidents of growth and suggest better timing and use of pesticides. We are currently focusing on carrot flies and rapeseed beetles however our technology has broad application to intrusion and damage by other animal or insect species. By digitally documenting field situations and suggesting more accurate control and timing thresholds, we can minimize the unnecessary use of pesticides and minimize the amount of time and resources that farmers need to dedicate to manually monitoring the status in their fields. Furthermore, the data we gather across wider geographical areas, introduces the potential to support research and broader monitoring of live field conditions.

Project details
Main funding source
Rural development 2014-2020 for Operational Groups
Rural Development Programme
2014SE06RDNP001 Sweden - Rural Development Programme (National)
Location
Main geographical location
Gotlands län

EUR 113 297.00

Total budget

Total contributions from EAFRD, national co-financing, additional national financing and other financing.

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Contacts

Project coordinator

  • Fredrik Gradelius

    Project coordinator

Project partners