Recognition of leaf diseases in hazelnut orchards with drone imagery by deep learning models: effectiveness of U-Net model variants

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dc.contributor.authorBoyar, Tülin
dc.contributor.authorTuran, Salih Can
dc.contributor.authorÇıplak, Zeki
dc.contributor.authorYıldız, Seyit Gazi
dc.contributor.authorSarıkaş, Ali
dc.contributor.authorYıldız, Kazım
dc.contributor.authorDemir, Önder
dc.date.accessioned2025-10-06T08:25:40Z
dc.date.available2025-10-06T08:25:40Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Bilgisayar Programcılığı Programı
dc.description26th International Conference on Computer Systems and Technologies, CompSysTech 2025, Hybrid, Ruse, 27-28 June 2025
dc.description.abstractThis paper presents a novel semantic segmentation approach for the binary classification of diseased and healthy regions in hazelnut orchards, addressing a significant gap in existing research by focusing on tree-level analysis. Utilizing video imagery captured by unmanned aerial vehicles (UAVs), this study investigates the challenges associated with drone-based image acquisition, such as environmental conditions, physical obstructions, battery life, and image stability. A unique image dataset specifically designed for tree-based disease detection is created. Various deep learning models, including seven U-Net variants, are employed to tackle the complexities of image processing and segmentation. The U-Net++ model is identified as the most effective for this task, demonstrating strong performance in distinguishing affected areas. However, further optimization of hyperparameters and expansion of the dataset are recommended to enhance model robustness and generalization. This research underscores the critical need for additional studies on UAV-based tree-level disease detection and highlights the substantial potential of deep learning in agricultural technology for mitigating production losses through early and accurate identification of stressed regions.
dc.description.sponsorshipMarmara University ; FYL-2022-10761
dc.identifier.doi10.1109/CompSysTech65493.2025.11136971
dc.identifier.scopus2-s2.0-105016454365
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/CompSysTech65493.2025.11136971
dc.identifier.urihttps://hdl.handle.net/11501/2389
dc.indekslendigikaynakScopus
dc.institutionauthorÇıplak, Zeki
dc.institutionauthorid0000-0002-0086-3223
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof26th International Conference on Computer Systems and Technologies, CompSysTech 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectDeep Learning
dc.subjectImage Processing
dc.subjectLeaf Diseases
dc.subjectMachine Vision
dc.subjectTree-Level Analysis
dc.subjectUnmanned Aerial Vehicles
dc.titleRecognition of leaf diseases in hazelnut orchards with drone imagery by deep learning models: effectiveness of U-Net model variants
dc.typeConference Object

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