Self-Supervised Leaf Segmentation under Complex Lighting Conditions

Abstract

As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision tasks, its adaptation for image-based plant phenotyping remains rather unexplored. In this work, we present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. The self-supervised semantic segmentation model groups the semantically similar pixels by iteratively referring to the self-contained information, allowing the pixels of the same semantic object to be jointly considered by the color-based leaf segmentation algorithm for identifying the leaf regions. Additionally, we propose to use a self-supervised color correction model for images taken under complex illumination conditions. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework in achieving effective and generalizable leaf segmentation.

Plant phenotyping relevance

植物画像から葉領域を抽出する自己教師ありセグメンテーション手法の開発が中心であり、画像ベース植物フェノタイピングの方法論に該当する。

Code and data availability

The paper's authors explicitly state that the developed code and datasets (including their Cannabis leaf image dataset used for self-supervised leaf segmentation phenotyping) will be made publicly available at their GitHub repository, which matches the allowed URL.