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Self-Supervised Leaf Segmentation under Complex Lighting Conditions
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DOI：10.1016/j.patcog.2022.109021.png)
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Abstract
En 中文
As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered in-creasing attention in recent years. While self-supervised learning is emerging as an effective alterna-tive 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 seman-tically 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 identi-fying 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 general-izable leaf segmentation.(c) 2022 Published by Elsevier Ltd.
Keywords:
Self -supervised learning
Convolutional neural networks
Image -based plant phenotyping
Leaf segmentation
Color correction
Cannabis
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