Leaf epidermis images for robust identification of plants.

Abstract

This paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis. All the experiments were carried out using 32 plant species with 309 epidermal samples captured by an optical microscope coupled to a digital camera. The results of the computational methods using texture features were compared to the conventional approach, where quantitative measurements of stomatal traits (density, length and width) were manually obtained. Epidermis image classification using texture has achieved a success rate of over 96%, while success rate was around 60% for quantitative measurements taken manually. Furthermore, we verified the robustness of our method accounting for natural phenotypic plasticity of stomata, analysing samples from the same species grown in different environments. Texture methods were robust even when considering phenotypic plasticity of stomatal traits with a decrease of 20% in the success rate, as quantitative measurements proved to be fully sensitive with a decrease of 77%. Results from the comparison between the computational approach and the conventional quantitative measurements lead us to discover how computational systems are advantageous and promising in terms of solving problems related to Botany, such as species identification.

Plant phenotyping relevance

葉表皮の顕微鏡画像からテクスチャ特徴を抽出し、従来の気孔形質測定と比較・頑健性評価を行う手法開発および検証であり、植物表現型取得が中心である。

Code and data availability

The article describes 309 leaf epidermis microscopy images and texture-based classification experiments, but contains no data availability statement, no public dataset deposit, and no author code/workflow URL. The only URLs present are the CC BY license links. No paper-specific public asset is identified.

No evidence-backed public reproduction asset is currently recorded.