Self-Supervised Leaf Segmentation under Complex Lighting Conditions

Lin, Xufeng and Li, Chang-Tsun and Adams, Scott and Kouzani, Abbas Z. and Jiang, Richard and He, Ligang and Hu, Yongjian and Vernon, Michael and Doeven, Egan H. and Webb, Lawrence and Mcclellan, Todd and Guskic, Adam (2023) Self-Supervised Leaf Segmentation under Complex Lighting Conditions. Pattern Recognition, 135: 109021. ISSN 0031-3203

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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.