A Hybrid Multistage Model Based on YOLO and Modified Inception Network for Rice Leaf Disease Analysis

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dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.contributor.authorDeari, Sabri
dc.contributor.authorUlukaya, Sezer
dc.date.accessioned2024-06-12T10:59:11Z
dc.date.available2024-06-12T10:59:11Z
dc.date.issued2024
dc.departmentTrakya Üniversitesien_US
dc.description.abstractLack or excess of water, moisture, and nutrients may cause diseases in various growing stages of rice. Unlike related studies, this work aims to detect each disease's symptom separately, rather than just classifying images by a classifier or showing the whole diseased leaf in a single bounding box. In this way, we consider all disease regions and make it possible to observe better the disease progression by considering detected boxes. Our motivation for this hybrid study stems from the fact that more than one disease symptom may occur on a leaf and the detection of symptoms at an early stage can positively affect the harvest yield. The main aim of this study is to classify rice leaf disease images accurately, reduce false detections, and validate the predictions of the classification network utilizing an object detection network. Therefore, we identify two stages for this work. In the first part, the task of classification, and in the second part, the task of determining the location of the disease symptoms is conducted. We use data augmentation and disout techniques to prevent overfitting in the classification process and to improve performance by modifying the classification network. Finally, we discuss how classification robustness can be tested and false predictions can be eliminated using the classification network Inception v3 and the detection network YOLOv5x jointly. As a result of the proposed hybrid model, state-of-the-art results are achieved with 96.67 % accuracy and 98.24 % F1 score on the publicly available rice leaf disease dataset.en_US
dc.identifier.doi10.1007/s13369-023-08408-1
dc.identifier.endpage6723en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85176568426en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6715en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-023-08408-1
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20356
dc.identifier.volume49en_US
dc.identifier.wosWOS:001105143100002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectRiceen_US
dc.subjectDisouten_US
dc.subjectRegularizationen_US
dc.subjectSmart Agricultureen_US
dc.subjectClassificationen_US
dc.titleA Hybrid Multistage Model Based on YOLO and Modified Inception Network for Rice Leaf Disease Analysisen_US
dc.typeArticleen_US

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