Comprehensive stomata image dataset of Sundarbans Mangrove and Ratargul Swamp forest tree species in Bangladesh.

Abstract

Plants' leaf stomata are crucial for various scientific research, including identifying species, studying ecology, conserving ecosystems, improving agriculture, and advancing the field of deep learning. This dataset, containing 1083 images, encompasses 11 species from two distinct locations in Bangladesh: nine from the Sundarbans mangrove forest and two from the Ratargul Swamp Forest. It is a valuable tool for refining machine learning algorithms that specialize in detecting stomata and categorizing species accurately. Researchers can explore a deeper understanding of plant physiology, adaptation mechanisms, and environmental interactions by employing pattern recognition, deep learning, and feature extraction techniques. Additionally, this dataset could be a potential tool for enhancing research in macroscopic metamaterials, extending its impact beyond traditional biological studies into interdisciplinary fields of technology and material science.

Plant phenotyping relevance

気孔画像データセット自体が研究の中心で、画像から気孔を検出する再利用可能な表現型取得基盤を提供しているため。

Code and data availability

This Data in Brief article deposits its own stomata image dataset (1083 images, 11 species) and stomatal trait measurements in Mendeley Data, with a direct public URL and DOI given in the Specifications Table.