Forests provide essential ecosystem services beyond timber, yet locating high-yield areas for non-wood forest products such as bilberries (<em>Vaccinium myrtillus</em>) remains a challenge for both recreational and commercial pickers. By integrating Airborne Laser Scanning (ALS), Geographical Information System (GIS) data, and crowdsourced terrestrial imagery analyzed via deep learning (YOLO), we developed a predictive system optimized for identifying high-yield hotspots. We demonstrate that YOLO detection remains highly accurate, but plant height significantly contributes to berry omission. However, this limitation can be mitigated by selecting the maximum berry count from multi-angle terrestrial images. Using a Random Forest classifier across a 36-km² study area in Nuuksio, Finland, we achieved a precision of 58% for the highest yield category. This represents a 20-fold increase in the probability of encountering a high-yield area compared to random searching. Extensive user testing over two years validated the practical utility of the system, showing a 22.5% increase in harvested yield and a 36.5% reduction in time required to locate hotspots. Furthermore, 97% of users reported that the platform provided an accurate big picture of bilberry yield. These results highlight the potential of combining crowdsourced citizen science with advanced LiDAR metrics to create digital twins of forest ecosystems that enhance human interaction with nature and optimize the sustainable harvest of wild food resources.