Self-Supervised Leaf Segmentation under Complex Lighting Conditions
This work addresses leaf segmentation for plant phenotyping researchers, but it is incremental as it adapts existing self-supervised methods to a new domain.
The paper tackled leaf segmentation in plant phenotyping under complex lighting by proposing a self-supervised framework combining semantic segmentation, color-based segmentation, and color correction, achieving effective and generalizable results across different plant species.
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.