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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1851
DC FieldValueLanguage
dc.contributor.authorVo Nguyen Minh Duyen_US
dc.date.accessioned2024-11-15T04:19:06Z-
dc.date.available2024-11-15T04:19:06Z-
dc.date.issued2024-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1851-
dc.description.abstractAccurate and fast detection of tomato diseases is critical for good crop management and sustainable agriculture, as illnesses identified correctly and promptly can lead to early treatment of the plants, effectively enhancing tomato plant yield. Due to the complexity and similarity of different tomato diseases and pests in the natural environment, there is a need for state-of-the-art object detection models to meet the demands for real time and accurate detection. YOLOV8 and RT-DeTR are the most advanced object detection models, boasting great accuracy and real-time detection speeds. As a result, this research aims to compare the efficacy of the YOLOv8 model and the RT-DeTR detection transformer model in tomato disease leaf detection.en_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectArtificial intelligence applicationen_US
dc.subjectCNN machine-learningen_US
dc.subjectYOLOv8en_US
dc.subjectObject detection modelsen_US
dc.titleA comparison between yolov8 and detection transformer_ A case study on diseased tomato leavesen_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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