Detection of Wild Mushrooms Using Machine Learning and Computer Vision

The increasing global demand for sustainable and high-quality agricultural products has driven interest in precision agriculture technologies. This study presents a novel approach to wild mushroom detection, particularly focusing on Macrolepiota procera as a focal species for demonstration and benchmarking. The proposed approach utilises unmanned aerial vehicles (UAVs) equipped with multispectral imaging and the YOLOv5 object detection algorithm. A custom dataset, the wild mushroom detection dataset (WOES), comprising 907 annotated aerial and ground images, was developed to support model training and evaluation. Our method integrates low-cost hardware with advanced deep learning and vegetation index analysis (NDRE) to enable real-time identification of mushrooms in forested environments. The proposed system achieved an identification accuracy exceeding 90% and completed detection tasks within 30 min per field survey. Although the dataset is geographically limited to Western Macedonia, Greece, and focused primarily on a morphologically distinctive species, the methodology is designed to be extendable to other wild mushroom types. This work contributes a replicable framework for scalable, cost-effective mushroom monitoring in ecological and agricultural applications.