# Interactive Hybrid Rice Breeding with Parametric Dual Projection

arXiv: https://arxiv.org/abs/2507.11848 · HTML: https://mlsanity.com/paper/2507.11848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples](https://mlsanity.com/paper/2002.03103/) (2020)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [Dissimilarity-based Sparse Subset Selection](https://mlsanity.com/paper/1407.6810/) (2014)
- [Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets](https://mlsanity.com/paper/2412.16839/) (2024)
- [FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images](https://mlsanity.com/paper/1908.00671/) (2019)
- [ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map](https://mlsanity.com/paper/2407.12315/) (2024)
- [HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning](https://mlsanity.com/paper/2208.07491/) (2022)
- [Tasks, Techniques, and Tools for Genomic Data Visualization](https://mlsanity.com/paper/1905.02853/) (2019)
- [A Visual Analytics Approach to Facilitate the Proctoring of Online Exams](https://mlsanity.com/paper/2101.07990/) (2021)
- [Enhancing Single-Frame Supervision for Better Temporal Action Localization](https://mlsanity.com/paper/2312.05178/) (2023)

## Built on by (most cited first, 1 of 1)

- [Human-Guided Causal Knowledge Injection for Virtual Cells](https://mlsanity.com/paper/2608.08430/) (2026)

## Abstract (arXiv)

Hybrid rice breeding crossbreeds different rice lines and cultivates the resulting hybrids in fields to select those with desirable agronomic traits, such as higher yields. Recently, genomic selection has emerged as an efficient way for hybrid rice breeding. It predicts the traits of hybrids based on their genes, which helps exclude many undesired hybrids, largely reducing the workload of field cultivation. However, due to the limited accuracy of genomic prediction models, breeders still need to combine their experience with the models to identify regulatory genes that control traits and select hybrids, which remains a time-consuming process. To ease this process, in this paper, we proposed a visual analysis method to facilitate interactive hybrid rice breeding. Regulatory gene identification and hybrid selection naturally ensemble a dual-analysis task. Therefore, we developed a parametric dual projection method with theoretical guarantees to facilitate interactive dual analysis. Based on this dual projection method, we further developed a gene visualization and a hybrid visualization to verify the identified regulatory genes and hybrids. The effectiveness of our method is demonstrated through the quantitative evaluation of the parametric dual projection method, identified regulatory genes and desired hybrids in the case study, and positive feedback from breeders.