Johan Kristiansson
Senior Forskare
Contact Johan08 January 2021, 10:08
AI for area monitoring - a feasibility study was granted by Vinnova.
The Swedish Board of Agriculture (SJV) and the county administrative boards make every year thousands of field visits to pastures with the aim of determining whether the land is being used for agricultural practices 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 automated satellite imagery analysis.
SJVs forthcoming action plan for digitization mentions AI's potential to achieve higher quality and efficiency in operations. The proposed AI project aims to begin the SJV´s work to improve quality and efficiency in operations by developing processes that reduce costs and provide greater customer value.
Today, images from the Sentinel satellites are used to analyse how agricultural parcels are used. This work is performed by experts at SJV and can be very time consuming, which makes it difficult to analyse larger parcel area.
The goal of the project is to develop algorithms based on a limite d area to automatically detect whether pastures have been mowed or grazed during the year. By analysing satellite images using AI / machine learning, changes can be detected and classified to automatically determine how an agricultural parcel has been used over time. At a later stage, the method can be scaled up to automatically monitor all agricultural land throughout Sweden.
There is already a great need to follow the development of pastures over time, partly through the updating of the Land Parcel Identification System (LPIS) that SJV performs annually and the requirements for future inspections within the new CAP. The increased knowledge in AI has the potential to solve other conditions and controls in a cheaper and more efficient way.
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