# CA-Cut: Crop-Aligned Cutout for Data Augmentation to Learn More Robust Under-Canopy Navigation

arXiv: https://arxiv.org/abs/2507.17727 · HTML: https://mlsanity.com/paper/2507.17727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [What to Hide from Your Students: Attention-Guided Masked Image Modeling](https://mlsanity.com/paper/2203.12719/) (2022)
- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information](https://mlsanity.com/paper/2104.08029/) (2021)
- [Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows](https://mlsanity.com/paper/2107.00700/) (2021)
- [Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies](https://mlsanity.com/paper/2109.10135/) (2021)
- [Towards Infield Navigation: leveraging simulated data for crop row detection](https://mlsanity.com/paper/2204.01811/) (2022)
- [Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots](https://mlsanity.com/paper/2209.04278/) (2022)
- [Colorful Cutout: Enhancing Image Data Augmentation with Curriculum Learning](https://mlsanity.com/paper/2403.20012/) (2024)
- [BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions](https://mlsanity.com/paper/2503.24032/) (2025)

## Abstract (arXiv)

State-of-the-art visual under-canopy navigation methods are designed with deep learning-based perception models to distinguish traversable space from crop rows. While these models have demonstrated successful performance, they require large amounts of training data to ensure reliability in real-world field deployment. However, data collection is costly, demanding significant human resources for in-field sampling and annotation. To address this challenge, various data augmentation techniques are commonly employed during model training, such as color jittering, Gaussian blur, and horizontal flip, to diversify training data and enhance model robustness. In this paper, we hypothesize that utilizing only these augmentation techniques may lead to suboptimal performance, particularly in complex under-canopy environments with frequent occlusions, debris, and non-uniform spacing of crops. Instead, we propose a novel augmentation method, so-called Crop-Aligned Cutout (CA-Cut) which masks random regions out in input images that are spatially distributed around crop rows on the sides to encourage trained models to capture high-level contextual features even when fine-grained information is obstructed. Our extensive experiments with a public cornfield dataset demonstrate that masking-based augmentations are effective for simulating occlusions and significantly improving robustness in semantic keypoint predictions for visual navigation. In particular, we show that biasing the mask distribution toward crop rows in CA-Cut is critical for enhancing both prediction accuracy and generalizability across diverse environments achieving up to a 36.9% reduction in prediction error. In addition, we conduct ablation studies to determine the number of masks, the size of each mask, and the spatial distribution of masks to maximize overall performance.