# Self-Supervised Leaf Segmentation under Complex Lighting Conditions

arXiv: https://arxiv.org/abs/2203.15943 · HTML: https://mlsanity.com/paper/2203.15943/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 67 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals](https://mlsanity.com/paper/2102.06191/) (2021)
- [Colorization as a Proxy Task for Visual Understanding](https://mlsanity.com/paper/1703.04044/) (2017)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [Autoregressive Unsupervised Image Segmentation](https://mlsanity.com/paper/2007.08247/) (2020)
- [Deep Leaf Segmentation Using Synthetic Data](https://mlsanity.com/paper/1807.10931/) (2018)
- [Convolutional CRFs for Semantic Segmentation](https://mlsanity.com/paper/1805.04777/) (2018)
- [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https://mlsanity.com/paper/1709.00938/) (2017)
- [Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants](https://mlsanity.com/paper/1903.08583/) (2019)
- [Scalable learning for bridging the species gap in image-based plant phenotyping](https://mlsanity.com/paper/2003.10757/) (2020)
- [Automatic Leaf Extraction from Outdoor Images](https://mlsanity.com/paper/1709.06437/) (2017)
- [Instance Segmentation by Deep Coloring](https://mlsanity.com/paper/1807.10007/) (2018)

## Built on by (most cited first, 5 of 5)

- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Self-Supervised Learning for Image Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2505.13584/) (2025)
- [DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning](https://mlsanity.com/paper/2411.08340/) (2024)
- [Self-Supervised Contrastive Learning for Multi-Label Images](https://mlsanity.com/paper/2506.23156/) (2025)
- [CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis](https://mlsanity.com/paper/2605.03259/) (2026)

## Abstract (arXiv)

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.