Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based
Indonesia has many species of mangrove plants scattered throughout the coast to the river's edge. Species of mangrove plants can be distinguished based on root type, stem size, leaf shape, flower color, and fruit. Although each type of mangrove plant has different characteristics, several types...
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Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap
2025-06-01
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| Series: | Journal of Innovation Information Technology and Application |
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| Online Access: | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2676 |
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| author | Anindita Septiarini Rita Diana Rahmat Kamara Novianti Puspitasari Anton Prafanto |
| author_facet | Anindita Septiarini Rita Diana Rahmat Kamara Novianti Puspitasari Anton Prafanto |
| author_sort | Anindita Septiarini |
| collection | DOAJ |
| description | Indonesia has many species of mangrove plants scattered throughout the coast to the river's edge. Species of mangrove plants can be distinguished based on root type, stem size, leaf shape, flower color, and fruit. Although each type of mangrove plant has different characteristics, several types look similar, especially on the leaves. Therefore, a model was needed to classify mangrove plant species by applying current technology to make it easier to recognize the type of mangrove plant. This research aims to implement the Convolutional Neural Network (CNN) method in classifying mangrove plant species. The algorithm used is the 5th version of You Only Look Once (YOLO) with 3 different variants (YOLOv5s, YOLOv5m, and YOLOv5l). The three variants have various processing times and numbers of layers. This study uses mangrove leaf images with a total image dataset of 400 images consisting of 4 types of mangrove plants: Avicennia alba, Bruguiera gymnorhiza, Rhizopora apiculata, and Sonneratia alba. The model performance achieved 82.50%, 88.75%, and 93.75% accuracy using YOLOv5s, YOLOv5m, and YOLOv5l, respectively. |
| format | Article |
| id | doaj-art-b373cbcf98ff4c11b015806f86e012ff |
| institution | Kabale University |
| issn | 2716-0858 2715-9248 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap |
| record_format | Article |
| series | Journal of Innovation Information Technology and Application |
| spelling | doaj-art-b373cbcf98ff4c11b015806f86e012ff2025-08-20T03:29:44ZengPusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri CilacapJournal of Innovation Information Technology and Application2716-08582715-92482025-06-0171818710.35970/jinita.v7i1.26761760Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-BasedAnindita Septiarini0Rita Diana1Rahmat Kamara2Novianti Puspitasari3Anton Prafanto4Mulawarman UniversityMulawarman UniversityMulawarman UniversityMulawarman UniversityMulawarman UniversityIndonesia has many species of mangrove plants scattered throughout the coast to the river's edge. Species of mangrove plants can be distinguished based on root type, stem size, leaf shape, flower color, and fruit. Although each type of mangrove plant has different characteristics, several types look similar, especially on the leaves. Therefore, a model was needed to classify mangrove plant species by applying current technology to make it easier to recognize the type of mangrove plant. This research aims to implement the Convolutional Neural Network (CNN) method in classifying mangrove plant species. The algorithm used is the 5th version of You Only Look Once (YOLO) with 3 different variants (YOLOv5s, YOLOv5m, and YOLOv5l). The three variants have various processing times and numbers of layers. This study uses mangrove leaf images with a total image dataset of 400 images consisting of 4 types of mangrove plants: Avicennia alba, Bruguiera gymnorhiza, Rhizopora apiculata, and Sonneratia alba. The model performance achieved 82.50%, 88.75%, and 93.75% accuracy using YOLOv5s, YOLOv5m, and YOLOv5l, respectively.https://ejournal.pnc.ac.id/index.php/jinita/article/view/2676leaf classificationmangroveobject detectioncnnyolo |
| spellingShingle | Anindita Septiarini Rita Diana Rahmat Kamara Novianti Puspitasari Anton Prafanto Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based Journal of Innovation Information Technology and Application leaf classification mangrove object detection cnn yolo |
| title | Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based |
| title_full | Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based |
| title_fullStr | Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based |
| title_full_unstemmed | Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based |
| title_short | Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based |
| title_sort | comparison of yolov5 for classifying mangrove leaf species using cnn based |
| topic | leaf classification mangrove object detection cnn yolo |
| url | https://ejournal.pnc.ac.id/index.php/jinita/article/view/2676 |
| work_keys_str_mv | AT aninditaseptiarini comparisonofyolov5forclassifyingmangroveleafspeciesusingcnnbased AT ritadiana comparisonofyolov5forclassifyingmangroveleafspeciesusingcnnbased AT rahmatkamara comparisonofyolov5forclassifyingmangroveleafspeciesusingcnnbased AT noviantipuspitasari comparisonofyolov5forclassifyingmangroveleafspeciesusingcnnbased AT antonprafanto comparisonofyolov5forclassifyingmangroveleafspeciesusingcnnbased |