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Volume 16
Issue 7
10.3390/info16070539
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Detection of Wild Mushrooms Using Machine Learning and Computer Vision

by
Christos Chaschatzis
1,
Chrysoula Karaiskou
1,
Chryssanthi Iakovidou
1,
Panagiotis Radoglou-Grammatikis
1,*,
Stamatia Bibi
1,
Sotirios K. Goudos
2 and
Panagiotis G. Sarigiannidis
1
1
Department of Electrical and Computer Engineering, University of Western Macedonia Kozani, 50100 Kozani, Greece
2
Department of Physics, Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Information 2025, 16(7), 539; https://doi.org/10.3390/info16070539 (registering DOI)
Submission received: 13 May 2025 / Revised: 18 June 2025 / Accepted: 18 June 2025 / Published: 25 June 2025
(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)
Versions Notes

Abstract

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.
Keywords: wild mushroom detection; Macrolepiota procera; precision agriculture; machine learning; unmanned aerial vehicle; multispectral wild mushroom detection; Macrolepiota procera; precision agriculture; machine learning; unmanned aerial vehicle; multispectral

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MDPI and ACS Style

Chaschatzis, C.; Karaiskou, C.; Iakovidou, C.; Radoglou-Grammatikis, P.; Bibi, S.; Goudos, S.K.; Sarigiannidis, P.G. Detection of Wild Mushrooms Using Machine Learning and Computer Vision. Information 2025, 16, 539. https://doi.org/10.3390/info16070539

AMA Style

Chaschatzis C, Karaiskou C, Iakovidou C, Radoglou-Grammatikis P, Bibi S, Goudos SK, Sarigiannidis PG. Detection of Wild Mushrooms Using Machine Learning and Computer Vision. Information. 2025; 16(7):539. https://doi.org/10.3390/info16070539

Chicago/Turabian Style

Chaschatzis, Christos, Chrysoula Karaiskou, Chryssanthi Iakovidou, Panagiotis Radoglou-Grammatikis, Stamatia Bibi, Sotirios K. Goudos, and Panagiotis G. Sarigiannidis. 2025. "Detection of Wild Mushrooms Using Machine Learning and Computer Vision" Information 16, no. 7: 539. https://doi.org/10.3390/info16070539

APA Style

Chaschatzis, C., Karaiskou, C., Iakovidou, C., Radoglou-Grammatikis, P., Bibi, S., Goudos, S. K., & Sarigiannidis, P. G. (2025). Detection of Wild Mushrooms Using Machine Learning and Computer Vision. Information, 16(7), 539. https://doi.org/10.3390/info16070539

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