b'ENFORCING PLANT BREEDERS RIGHTS WITH THE HELP OF SATELLITESBREEDERS TRUST INVESTS IN REMOTE SENSING TECHNIQUES. BY: MARCEL BRUINS S uppose one day it is possible to distinguish the most impor-tant Plant Breeders Rights protected potato varieties (or a variety of any other species) anywhere in the world by using remote sensing technology, and that it even is possible to distinguish seed potato fields from ware potato fields of a cer-tain variety. If it turns out to be possiblethat remote sensing techniques can recognize potato varietiesthen this could be a very helpful enforcement instrument in the fight against illegal multiplication and to enforce Plant Breeders Rights, for instance, by monitoring the farm saved seed acreages in a cer-tain part of the world. That is the dream of Geert Staring, general director of Breeders Trust (www.breederstrust.eu), seated with his head office in Brussels and representing the PBR interests of the 11 most prominent West European seed potato breeders and nine grass seed breeders.THE REMOTE SENSING PROJECTThe project aims to support Breeders Trust to enforce plantA photo from space of the North-East Polder in the Netherlands. The red breeders rights and eliminate illicit propagation and marketingcoloured blocks are potato fields and in yellow, other field crops.of seed potatoes. The project is performed by GEO4A, situated with its headquarters in Emmeloord in the Netherlands with a specialty in potatoes. In June 2020, Breeders Trust signed a contract with GEO4A to conduct a feasibility check in order to find an answer if potato variety recognition by using remote sensing technology, anywhere in the world, is possible. Breeders Trust and GEO4A jointly started the feasibility pilot project with five potato varieties. The North-East Polder in the Netherlands was chosen because of the high number of potato fields of many different varieties situated close to each other. Guido Mangnus, managing director GEO4A, mentions that due to the technical challenges, a step-by-step approach was chosen in close coordination with Breeders Trust. The first phase focusses specifically on the feasibility of variety classifica-tion by means of Earth Observation (EO) data in general. For the development of the potato variety recognition, Breeders TrustGeert Staring Guido Mangnusprovided sample locations and crop variety at those locations as in-situ data. These sample points are supplemented by GeoVille (Austria) with earth observation data (Sentinel 2) as a basis for any further actions on a potato variety methodology, Mangnuspotato variety recognition, using an in-house developed feature explains. Now, one year later, the results of this feasibility checkengineered dataset called HyperF-Tensors. Given the underlying are known and look very promising.dataset, these HyperF-Tensors represent unique fingerprints for the five potato varieties and are used as an input for the machine METHODOLOGY learning model classification.In the first step, data exploration is used to analyse the data pro-vided by Breeders Trust. Therefore, the spectral characteristicsRESULTS AND PERFORMANCE METRICSof each of the five selected varieties are visually checked. TheseFor each number of observations included into the training of in-situ data and Sentinel-2 observations are combined for thethe machine learning model, a random split of the train and test 48IEUROPEAN SEEDIEUROPEAN-SEED.COM'