# Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation

arXiv: https://arxiv.org/abs/2509.09961 · HTML: https://mlsanity.com/paper/2509.09961/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 52 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 28 of 28)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [Seesaw Loss for Long-Tailed Instance Segmentation](https://mlsanity.com/paper/2008.10032/) (2020)
- [Remix: Rebalanced Mixup](https://mlsanity.com/paper/2007.03943/) (2020)
- [Robust and Generalizable Visual Representation Learning via Random Convolutions](https://mlsanity.com/paper/2007.13003/) (2020)
- [Feature Space Augmentation for Long-Tailed Data](https://mlsanity.com/paper/2008.03673/) (2020)
- [Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning](https://mlsanity.com/paper/2110.05474/) (2021)
- [InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting](https://mlsanity.com/paper/1908.07801/) (2019)
- [Distributional Robustness Loss for Long-tail Learning](https://mlsanity.com/paper/2104.03066/) (2021)
- [Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation](https://mlsanity.com/paper/2107.11279/) (2021)
- [FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation](https://mlsanity.com/paper/2102.12867/) (2021)
- [A Survey on Long-Tailed Visual Recognition](https://mlsanity.com/paper/2205.13775/) (2022)
- [UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2205.10334/) (2022)
- [Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation](https://mlsanity.com/paper/2008.05676/) (2020)
- [Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline](https://mlsanity.com/paper/2408.03120/) (2024)
- [PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation](https://mlsanity.com/paper/2409.04038/) (2024)
- [Snap and Diagnose: An Advanced Multimodal Retrieval System for Identifying Plant Diseases in the Wild](https://mlsanity.com/paper/2408.14723/) (2024)
- [Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT](https://mlsanity.com/paper/2207.07919/) (2022)
- [CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction](https://mlsanity.com/paper/2409.08443/) (2024)
- [Track Any Peppers: Weakly Supervised Sweet Pepper Tracking Using VLMs](https://mlsanity.com/paper/2411.06702/) (2024)

## Built on by (most cited first, 3 of 3)

- [StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components](https://mlsanity.com/paper/2602.00703/) (2026)
- [Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation](https://mlsanity.com/paper/2604.05415/) (2026)
- [Benchmarking Vision-Language Models for Microscopic Plant Image Understanding](https://mlsanity.com/paper/2606.22497/) (2026)

## Abstract (arXiv)

Accurate segmentation of foliar diseases and insect damage in wheat is crucial for effective crop management and disease control. However, the insect damage typically occupies only a tiny fraction of annotated pixels. This extreme pixel-level imbalance poses a significant challenge to the segmentation performance, which can result in overfitting to common classes and insufficient learning of rare classes, thereby impairing overall performance. In this paper, we propose a Random Projected Copy-and-Paste (RPCP) augmentation technique to address the pixel imbalance problem. Specifically, we extract rare insect-damage patches from annotated training images and apply random geometric transformations to simulate variations. The transformed patches are then pasted in appropriate regions while avoiding overlaps with lesions or existing damaged regions. In addition, we apply a random projection filter to the pasted regions, refining local features and ensuring a natural blend with the new background. Experiments show that our method substantially improves segmentation performance on the insect damage class, while maintaining or even slightly enhancing accuracy on other categories. Our results highlight the effectiveness of targeted augmentation in mitigating extreme pixel imbalance, offering a straightforward yet effective solution for agricultural segmentation problems.