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Sentinell image over grazed land
Photo: Jordbruksverket

AI and Sentinell images for agricultural area monitoring - a pilot

Satellit images and Artificiell Intelligens can facilitate monitoring and classification of changes in agricultural land use. This project aims to automatically decide if grasslands have been harvested or if pastures have been grazed during the season. It is a collaboration between the Swedish Board of Agriculture and RISE.

Each year, the Swedish Board of Agriculture (SJV) and the county administrative boards make thousands of field visits to pastures with the aim of determining whether the land is cultivated or abandoned. This is very costly and therefore, in the coming period of the Common Agricultural Policy (CAP), the EU proposes that field visits will be replaced by automatic analysis of satellite images.

This project aims to initiate SJV's work to improve quality and efficiency, by developing algorithms and best practices for agriculture monitoring relevant for the management of the CAP. The goal of this project is to develop algorithms that make it possible, in a limited area, to automatically detect whether pastures have been harvested or grazed during the season. By analyzing satellite images using AI / machine learning, changes can be detected and classified to automatically determine the land use over time. In the future, the method can be scaled up to automatically monitor all agricultural land in Sweden.

 

 


Summary

Project name

AI for agricultural monitoring

Status

Active

RISE role in project

Project leader

Project start

Duration

2021-08-31

Total budget

550.000 kr

Partner

Swedish Board of Agriculture (Bastian Berlin, Nils Fernquist, Viktoria Björnström)

Funders

Vinnova, Swedish Board of Agriculture, Swedish Space Data Lab

Project members

Supports the UN sustainability goals

8. Decent work and economic growth
10. Reduced inequalities
15. Life on land

Contact person

Anna Rydberg

Senior forskare

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Contact person

Johan Kristiansson

Senior Forskare

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