Comparative analysis of stomatal pore instance segmentation: Mask R-CNN vs. YOLOv8 on Phenomics Stomatal dataset.

Abstract

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.

Plant phenotyping relevance

気孔孔の画像セグメンテーション手法を比較・評価し、専用データセットを導入して気孔寸法を推定しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper introduces the PhenomicsStomata dataset and trains Mask R-CNN/YOLOv8 models, but the supplied blocks contain no data availability statement, no public deposit of the dataset, annotations, trained checkpoints, or author analysis code. All cited URLs (Google Colab, RangeKing's YOLOv8 diagram, OpenCV, Matterport

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