{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:37:16Z","timestamp":1785418636856,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Operational Program \u201cCentral Macedonia\u201d, European Structural and Investment Funds 2014\u20132020","award":["KMP6-0282067"],"award-info":[{"award-number":["KMP6-0282067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The verticillium fungus has become a widespread threat to olive fields around the world in recent years. The accurate and early detection of the disease at scale could support solving the problem. In this paper, we use the YOLO version 5 model to detect verticillium fungus in olive trees using aerial RGB imagery captured by unmanned aerial vehicles. The aim of our paper is to compare different architectures of the model and evaluate their performance on this task. The architectures are evaluated at two different input sizes each through the most widely used metrics for object detection and classification tasks (precision, recall, mAP@0.5 and mAP@0.5:0.95). Our results show that the YOLOv5 algorithm is able to deliver good results in detecting olive trees and predicting their status, with the different architectures having different strengths and weaknesses.<\/jats:p>","DOI":"10.3390\/a16070343","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T01:35:16Z","timestamp":1689644116000},"page":"343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2680-0442","authenticated-orcid":false,"given":"Marios","family":"Mamalis","sequence":"first","affiliation":[{"name":"Information Systems Laboratory, Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4416-8764","authenticated-orcid":false,"given":"Evangelos","family":"Kalampokis","sequence":"additional","affiliation":[{"name":"Information Systems Laboratory, Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilias","family":"Kalfas","sequence":"additional","affiliation":[{"name":"Strategic Project Management Office, American Farm School, 54 Marinou Antypa Street, 57001 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4663-2113","authenticated-orcid":false,"given":"Konstantinos","family":"Tarabanis","sequence":"additional","affiliation":[{"name":"Information Systems Laboratory, Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","first-page":"369","article-title":"Una nuova malatia dell\u2019olivo","volume":"83","author":"Ruggieri","year":"1946","journal-title":"L\u2019Italia Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1094\/PDIS-09-15-1018-RE","article-title":"The Effect of Short Irrigation Frequencies on the Development of Verticillium Wilt in the Susceptible Olive Cultivar \u2018Picual\u2019 under Field Conditions","volume":"100","author":"Serrano","year":"2016","journal-title":"Plant Dis."},{"key":"ref_3","first-page":"1","article-title":"Verticillium wilt of olive: A case study to implement an integrated strategy to control a soil-borne pathogen","volume":"344","year":"2010","journal-title":"Plant Soil"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1046\/j.1439-0434.2001.00585.x","article-title":"Characteristics of Bacteria from Oilseed Rape in Relation to their Biocontrol Activity against Verticillium dahliae","volume":"149","author":"Alstrom","year":"2001","journal-title":"J. Phytopathol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fichtel, L., Fr\u00fchwald, A.M., H\u00f6sch, L., Schreibmann, V., Bachmeir, C., and Bohlander, F. (2021, January 12\u201314). Tree Localization and Monitoring on Autonomous Drones employing Deep Learning. Proceedings of the 2021 29th Conference of Open Innovations Association (FRUCT), Tampere, Finland.","DOI":"10.23919\/FRUCT52173.2021.9435549"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10384","DOI":"10.1109\/ACCESS.2022.3144433","article-title":"Detection of Norway Spruce Trees (Picea Abies) Infested by Bark Beetle in UAV Images Using YOLOs Architectures","volume":"10","author":"Safonova","year":"2022","journal-title":"IEEE Access"},{"key":"ref_7","first-page":"102946","article-title":"Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery","volume":"112","author":"Puliti","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","first-page":"26","article-title":"New hosts of Verticillium alboatrum","volume":"34","author":"Snyder","year":"1950","journal-title":"Plant Dis. Report."},{"key":"ref_9","first-page":"105","article-title":"La verticilliose de l\u2019olivier en Greece","volume":"5","author":"Zachos","year":"1963","journal-title":"Benaki Phytopathol. Inst."},{"key":"ref_10","first-page":"41","article-title":"Verticillium wilt in olive in Algeria: Geographical distribution and extent of the disease","volume":"82","author":"Geiger","year":"2000","journal-title":"Olivae"},{"key":"ref_11","unstructured":"Jim\u00e9nez-D\u00edaz, R., Tjamos, E., and Cirulli, M. (1998). A Compendium of Verticillium Wilts in Tree Species, CPRO."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1046\/j.1365-3059.2003.00809.x","article-title":"Epidemiology of Verticillium dahliae on olive (cv. Picual) and its effect on yield under saline conditions","volume":"52","author":"Levin","year":"2003","journal-title":"Plant Pathol."},{"key":"ref_13","first-page":"16","article-title":"Dissemination factors of Verticillium wilt of olive in Jordan","volume":"25","author":"Naser","year":"1998","journal-title":"Dirasat. Agric. Sci."},{"key":"ref_14","first-page":"149","article-title":"First record of Verticillium dahliae on olive in Malta","volume":"87","author":"Mifsud","year":"2005","journal-title":"J. Plant Pathol."},{"key":"ref_15","first-page":"433","article-title":"Olive verticillium wilt or dieback of olive in Iran","volume":"69","author":"Sanei","year":"2004","journal-title":"Commun. Agric. Appl. Biol. Sci."},{"key":"ref_16","first-page":"45","article-title":"Verticillium wilt of olives in Turkey","volume":"1","author":"Saydam","year":"1972","journal-title":"J. Turk. Phytopathol."},{"key":"ref_17","first-page":"29","article-title":"Olive diseases and disorders in Australia","volume":"59","author":"Sergeeva","year":"2009","journal-title":"Olive Dis. Disord. Aust."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1021\/jf063166d","article-title":"Dysfunctionality of the Xylem in Olea europaea L. Plants Associated with the Infection Process by Verticillium dahliae Kleb. Role of Phenolic Compounds in Plant Defense Mechanism","volume":"55","year":"2007","journal-title":"J. Agric. Food Chem."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pegg, G.F., and Brady, B.L. (2002). Verticillium Wilts, CABI Publishing.","DOI":"10.1079\/9780851995298.0000"},{"key":"ref_20","first-page":"1","article-title":"Symptomatology, incidence and distribution of Verticillium wilt of Olive trees in Andaluc\u00eda","volume":"23","author":"Caballero","year":"1984","journal-title":"Phytopathol. Mediterr."},{"key":"ref_21","first-page":"936","article-title":"Survey of verticillium wilt of olive trees in greece","volume":"63","author":"Thanassoulopoulos","year":"1979","journal-title":"Plant Dis. Report."},{"key":"ref_22","unstructured":"Kozlowski, T.T. (1978). Water Deficits and Plant Growth, Academic Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3389\/fpls.2018.00072","article-title":"Starch Hydrolysis and Vessel Occlusion Related to Wilt Symptoms in Olive Stems of Susceptible Cultivars Infected by Verticillium dahliae","volume":"9","author":"Trapero","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015, January 7\u201312). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo Algorithm Developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","unstructured":"Jocher, G. (2020). YOLOv5 by Ultralytics. Zenodo."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhou, J., Yang, Y., Liu, L., Liu, F., and Kong, W. (2022). Rapid Target Detection of Fruit Trees Using UAV Imaging and Improved Light YOLOv4 Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14174324"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tian, H., Fang, X., Lan, Y., Ma, C., Huang, H., Lu, X., Zhao, D., Liu, H., and Zhang, Y. (2022). Extraction of Citrus Trees from UAV Remote Sensing Imagery Using YOLOv5s and Coordinate Transformation. Remote Sens., 14.","DOI":"10.3390\/rs14174208"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"\u00d6zer, T., Akdo\u011fan, C., Ceng\u0131z, E., Kelek, M.M., Yildirim, K., O\u011fuz, Y., and Akko\u00e7, H. (2022, January 7\u20139). Cherry Tree Detection with Deep Learning. Proceedings of the 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, Turkey.","DOI":"10.1109\/ASYU56188.2022.9925332"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106560","DOI":"10.1016\/j.compag.2021.106560","article-title":"Deep neural network based date palm tree detection in drone imagery","volume":"192","author":"Jintasuttisak","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Biele, C., Kacprzyk, J., Owsi\u0144ski, J.W., Romanowski, A., and Sikorski, M. (2021). Digital Interaction and Machine Intelligence, Springer International Publishing.","DOI":"10.1007\/978-3-030-74728-2"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2021.11.016","article-title":"Oil palm tree counting in drone images","volume":"153","author":"Chowdhury","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wibowo, H., Sitanggang, I., Mushthofa, M., and Adrianto, H. (2022). Large-Scale Oil Palm Trees Detection from High-Resolution Remote Sensing Images Using Deep Learning. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6030089"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, Z., Ibrayim, M., and Hamdulla, A. (2022). Detection of Pine Wilt Nematode from Drone Images Using UAV. Sensors, 22.","DOI":"10.3390\/s22134704"},{"key":"ref_35","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Loy, C.C., and Tang, X. (2016, January 27\u201330). Learning deep representation for imbalanced classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.580"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and Van Der Maaten, L. (2018, January 8\u201314). Exploring the limits of weakly supervised pretraining. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"ref_38","first-page":"1","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","first-page":"1","article-title":"Learning to model the tail","volume":"30","author":"Wang","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102490","DOI":"10.1016\/j.rcim.2022.102490","article-title":"Automatic weld type classification, tacked spot recognition and weld ROI determination for robotic welding based on modified YOLOv5","volume":"81","author":"Chen","year":"2023","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C.E., Mann, H.M.R., and H\u00f8ye, T.T. (2023). Accurate detection and identification of insects from camera trap images with deep learning. PLoS Sustain. Transform., 2.","DOI":"10.1371\/journal.pstr.0000051"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kubera, E., Kubik-Komar, A., Kurasi\u0144ski, P., Piotrowska-Weryszko, K., and Skrzypiec, M. (2022). Detection and Recognition of Pollen Grains in Multilabel Microscopic Images. Sensors, 22.","DOI":"10.3390\/s22072690"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, S., Jin, Y., Ruan, Z., Ma, Z., Gao, R., and Su, Z. (2022). Real-Time Detection of Seedling Maize Weeds in Sustainable Agriculture. Sustainability, 14.","DOI":"10.3390\/su142215088"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/7\/343\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:13:42Z","timestamp":1760127222000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/7\/343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,17]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["a16070343"],"URL":"https:\/\/doi.org\/10.3390\/a16070343","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,17]]}}}