Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset
This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset pos...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1414849/full |
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| author | Thanh Tuan Thai Thanh Tuan Thai Thanh Tuan Thai Ki-Bon Ku Anh Tuan Le Anh Tuan Le San Su Min Oh Ngo Hoang Phan Ngo Hoang Phan In-Jung Kim Yong Suk Chung Yong Suk Chung |
| author_facet | Thanh Tuan Thai Thanh Tuan Thai Thanh Tuan Thai Ki-Bon Ku Anh Tuan Le Anh Tuan Le San Su Min Oh Ngo Hoang Phan Ngo Hoang Phan In-Jung Kim Yong Suk Chung Yong Suk Chung |
| author_sort | Thanh Tuan Thai |
| collection | DOAJ |
| description | This study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research. |
| format | Article |
| id | doaj-art-90f2bd42a36d46bb99bcdb806605d114 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-90f2bd42a36d46bb99bcdb806605d1142025-08-20T02:19:38ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14148491414849Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal datasetThanh Tuan Thai0Thanh Tuan Thai1Thanh Tuan Thai2Ki-Bon Ku3Anh Tuan Le4Anh Tuan Le5San Su Min Oh6Ngo Hoang Phan7Ngo Hoang Phan8In-Jung Kim9Yong Suk Chung10Yong Suk Chung11Department of Plant Resources and Environment, Jeju National University, Jeju, Republic of KoreaMultimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City, VietnamMultimedia Communications Laboratory, Vietnam National University, Ho Chi Minh City, VietnamDepartment of Electrical and Computer Engineering, Iowa State University, Ames, IA, United StatesMultimedia Communications Laboratory, Vietnam National University, Ho Chi Minh City, VietnamFaculty of Biology and Biotechnology, University of Science, Ho Chi Minh City, VietnamDepartment of Horticulture, Jeju National University, Jeju, Republic of KoreaMultimedia Communications Laboratory, Vietnam National University, Ho Chi Minh City, VietnamFaculty of Biology and Biotechnology, University of Science, Ho Chi Minh City, VietnamFaculty of Biotechnology, Bio-Resources Computing Research Center, Jeju National University, Jeju, Republic of KoreaDepartment of Plant Resources and Environment, Jeju National University, Jeju, Republic of KoreaPhytomix Corporation, Jeju, Republic of KoreaThis study conducts a rigorous comparative analysis between two cutting-edge instance segmentation methods, Mask R-CNN and YOLOv8, focusing on stomata pore analysis. A novel dataset specifically tailored for stomata pore instance segmentation, named PhenomicsStomata, was introduced. This dataset posed challenges such as low resolution and image imperfections, prompting the application of advanced preprocessing techniques, including image enhancement using the Lucy-Richardson Algorithm. The models underwent comprehensive evaluation, considering accuracy, precision, and recall as key parameters. Notably, YOLOv8 demonstrated superior performance over Mask R-CNN, particularly in accurately calculating stomata pore dimensions. Beyond this comparative study, the implications of our findings extend across diverse biological research, providing a robust foundation for advancing our understanding of plant physiology. Furthermore, the preprocessing enhancements offer valuable insights for refining image analysis techniques, showcasing the potential for broader applications in scientific domains. This research marks a significant stride in unraveling the complexities of plant structures, offering both theoretical insights and practical applications in scientific research.https://www.frontiersin.org/articles/10.3389/fpls.2024.1414849/fullstomataphenotypinginstance segmentationMask-RCNNYOLO |
| spellingShingle | Thanh Tuan Thai Thanh Tuan Thai Thanh Tuan Thai Ki-Bon Ku Anh Tuan Le Anh Tuan Le San Su Min Oh Ngo Hoang Phan Ngo Hoang Phan In-Jung Kim Yong Suk Chung Yong Suk Chung Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset Frontiers in Plant Science stomata phenotyping instance segmentation Mask-RCNN YOLO |
| title | Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset |
| title_full | Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset |
| title_fullStr | Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset |
| title_full_unstemmed | Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset |
| title_short | Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset |
| title_sort | comparative analysis of stomatal pore instance segmentation mask r cnn vs yolov8 on phenomics stomatal dataset |
| topic | stomata phenotyping instance segmentation Mask-RCNN YOLO |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1414849/full |
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