The aim of the study was to create a practical method for identifying potential locations for bilberry picking with help of remote sensing, local field data and phone application to support the development of the local berry value chain. Local field data w as collected 2021 and 2022 and consisted 503 and 525 plots from a study area of circa 25x45km in Västerbotten, Sweden. The potential for bilberry production was evaluated by measuring the shrub cover and amount of raw berries. Wall to wall remote sensing d ata included a Sentinel 2 image from same summer, airborne laser scanning data from 2020 and other map products. We created classification models for bilberry shrub and yield using both logistic regression (2 classes) and ordinal regression (3 classes) mod els using 2021 data, and validated and calibrated models with 2022 data. Predictor variables consisted of spectral metrics from satellite data; structural metrics from laser data; existing raster maps of tree species, stand attributes, site index, soil moi sture and land use classes. The 2 class models performed better than three class models, delivering the AUC 0.73, overall accuracy 0.83 and kappa value 0.51 for best bilberry shrub model and 0.75, 0.77 and 0.50 respectively for best bilberry yield model. T he best models included both laser based structural metrics describing e.g canopy closure and spectral metrics, but also e.g. volume of pine, soil moisture and site index were found significant predictor variables. Calibration of the models improved annual predictions and the validation of the 2021 raster maps with 2022 data produced similar AUC, OA, and kappa values for bilberry yield (0.73, 0.74 and 0.46), but lower for bilberry shrub (0.61, 0.68 and 0.24). A dedicated phone application was developed duri ng the project, which was used both for collecting the field data and for presenting the potential locations of berry yields. Local berry maps can help berry pickers easier to find the berries in forest landscape and therefore support local berry value cha in. This study is part of the FAIRCHAIN project, which has received funding from the European Union’s funding programme H2020 research and innovation programme under grand agreement 101000723.